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• This paper formulates the task assignment and route planning of multiple AGVs in an intelligent warehouse as a fixed-destination Multiple Depot Traveling Salesman Problem (MDTSP). Considering the layout of the automated warehouse and the driving characteristics of the AGVs, a MILP model is developed to minimize the total completion time. A series of valid inequalities are presented to strengthen the model by analyzing the characteristics of the problem. • We develop a hyper-heuristic that uses a novel selection strategy based on the improved Multi-Armed Bandits algorithm called Co-SLMAB. It applies the Exponential Monte Carlo with counters (EMCQ) as the acceptance criterion. To our knowledge, ours is the first study to investigate the suitability of a reinforcement learning-based hyper-heuristic (RLHH) method for solving the multi-AGVs task assignment and scheduling problem. • We introduce a novel scheduling approach that optimizes racks' allocation between AGVs and the handling sequence in the RMFS. Then, an efficient method for conflict-free AGV path planning is proposed, which takes different collision avoidance measures depending on the situation. Practicality is achieved by integrating task scheduling and path planning for multi-AGV systems. • We demonstrate the superior performance of the RLHH on various problem instances through a comparative analysis with other algorithms. Furthermore, we analyze the efficiency of the proposed algorithm based on real-life warehouse layouts and perform a sensitivity analysis on AGV configurations. Numerical investigations reveal that the proposed approach greatly enhances the productivity of RMFS and the coordination of AGVs in an actual intelligent warehouse scenario. Globally, e-commerce warehouses have begun implementing robotic mobile fulfillment systems (RMFS), which can improve order-picking efficiency by using automated guided vehicles (AGVs) to realize operations from parts to pickers. AGVs depart from their initial points, move to a target rack position, and subsequently transport racks to picking stations. The AGVs return the racks to their original positions after the workers pick them up. When all tasks are completed, the AGVs return to their starting point. In this context, the main challenge is the task assignment and route planning of multiple AGVs to minimize travel times. We formulate a mixed-integer linear programming (MILP) model with valid inequalities to solve small problem instances optimally. We introduce a reinforcement learning (RL)-based hyper-heuristic (HH) framework to solve large instances to near-optimality. A typical HH framework comprises two levels: high-level heuristics (HLH) and low-level heuristics (LLH). The framework starts from an initial solution and improves iteratively through LLHs, while the HLH invokes a selection strategy and an acceptance criterion to generate a new solution. We propose a novel selection strategy based on the improved Multi-Armed Bandits algorithm called Co-SLMAB and Exponential Monte Carlo with counters (EMCQ) as the acceptance criterion. The corresponding collision avoidance rules are then formulated for different conflicts to construct a conflict-free traveling route for AGVs. Besides testing the proposed framework's effectiveness in real-life warehouse layouts, we perform extensive computational experiments and a thorough sensitivity analysis. The results show that (i) the proposed valid inequalities aid in obtaining better lower bounds and significantly speed up the solution process; (ii) the Co-SLMAB-HH framework is quite competitive compared to CPLEX, outperforming the other tested hyper-heuristics and the problem-specific heuristic regarding convergence and computation time; and (iii) a pool of LLHs consisting of a wide range of different operators is advantageous over a limited set of simple operators while solving problems using hyper-heuristics. [ABSTRACT FROM AUTHOR]

• Evaluating and enhancing the resilience of urban logistics systems under emergency lockdowns. • Quantifying the performance of urban logistics systems by integrating distributional equity and delivery efficiency. • Proposing a quick-response capacity reallocation strategy for resilience enhancement. • Case study on the 2022 COVID-19 outbreak in Shanghai with managerial insights. The urban logistics system serves an essential role in providing daily necessities for residents but is vulnerable to resource shortages in disasters. For instance, cluster infections and access restrictions caused by public health emergencies like COVID-19 pose a severe challenge to the resilient operations of urban logistics systems. Especially, the restriction level and the labor shortage always vary a lot on a citywide basis, resulting in a dilemma in handling distributional equity and delivery efficiency. Motivated by this issue, this paper studies the resilience assessment method and enhancement strategy for urban logistics systems under emergency lockdowns with integrated consideration of equity and efficiency. The resilience-oriented capacity reallocation models are developed for quick-response strategies in the case of two disruption types, i.e., closure of distribution centers and capacity degradation of delivery routes. Numerical studies of a real-world case in Shanghai are conducted to illustrate the feasibility and effectiveness of our methods under different disruption levels. The comparison of reallocation schemes with respect to different weights of equity and efficiency is also performed to discuss their impacts on resilience. The results indicate the importance of considering equity in logistics distribution under large-scale disruptions. If distributional equity is highly valued, the service capability can retain up to 74 % of the original level despite half of the distribution centers being shut down. In addition, different concerns on equity and efficiency can lead to variations in system resilience for the same disruption scenario, but the capacity reallocation schemes consistently align with the aim of resilience enhancement. Finally, several managerial insights are provided for decision-makers on urban logistics planning and intelligent disaster management during emergencies. [ABSTRACT FROM AUTHOR]

• We investigated the effects of product substitutability in a triadic supply chain. • Product homogeneity can harm the profits of manufacturers and supply chain. • We explored the effect of power structure on the supply chain. With the development of the retail industry and the increasing complexity of the power relationship between common retailers and manufacturers, research on product substitutability has become increasingly critical for operational management and decision-making regarding substitutable products. We investigated the effects of product substitutability on retail prices, profits, and social welfare for a triadic supply chain comprised of a retailer and two competing manufacturers. We then extended our analysis to the Nash bargaining game to evaluate the impact of product substitutability and bargaining power on equilibrium in a multiunit bilateral negotiation. The findings revealed that product homogeneity can harm the profits of manufacturers and the overall supply chain. In contrast, product substitutability's impact on the profits of retailers depends on the inter-firm power relationship. Moreover, the retailer's profit was found to consistently increase with respect to substitutability in a manufacturer Stackelberg model, but not so in the vertical Nash and retailer Stackelberg models. We also explored the effect of power structure on supply chain performance. Our results provide valuable insights that can help manufacturers and retailers decide on pricing, sourcing, and brand positioning to improve economic and social performance, as well as assist the government in deciding whether product differentiation should be encouraged. [ABSTRACT FROM AUTHOR]

In this paper, we study a supply chain network design problem that takes into account several real-world features. Focusing on the growing use of third-party warehouse services, the paper discusses the creation of a network that can adjust to the needs of businesses to enhance operational flexibility and resource utilization. Moreover, it addresses the challenge of delivery date flexibility where the conventional single-day delivery is replaced with a range of possible delivery dates. Considering these two types of flexibility, we formulate a supply chain network design problem that includes all production, inventory, location, and distribution decisions. We propose a solution approach called the relax-and-restrict matheuristic. This approach iteratively solves three versions of the model: two relaxed versions and a restricted one. Through a series of computational experiments, we highlight the efficiency of the proposed method. Furthermore, managerial insights are presented on the role of flexibility in supply chain network design and how it relates to economies of scale. The results demonstrate how synergistic effects between two types of flexibility improve logistics performance by achieving cost-effectiveness. • We formally introduce the flexible integrated supply network design problem. • We define two types of flexibility: delivery due dates and storage locations. • We develop a relax-and-restrict matheuristic to obtain solutions for real-size instances of the problem. • We demonstrate how the two types of flexibility can reinforce each other. • We provide managerial insights on the role of flexibility in network design. [ABSTRACT FROM AUTHOR]

Many studies concentrate on the vertical collaboration between upstream and downstream enterprises in the supply chain. However, we have observed that some competing manufacturers also engage in horizontal collaboration in practice, where they hold minority ownership shares in their rival, especially for those with limited initial capital. Although such collaboration has become increasingly prevalent, achieving the expected effects remains challenging due to frequent conflicts in collaboration mechanisms. This study considers a two-echelon supply chain with one supplier and two manufacturers with different financial statuses, where manufacturer 2 not only engages in Cournot competition with manufacturer 1, but also offers a horizontal collaboration contract as a collaborator. By incorporating the initial capital of manufacturer 1, we develop a benchmark sufficient capital model, and two other models such as capital constrained model and the horizontal collaboration model. The impact of horizontal cooperation on the optimal decisions of manufacturers is analyzed to investigate whether a collaboration strategy exists that can mitigate these conflicts while improving the profits of all participants. We prove that the horizontal collaboration can benefit both manufacturers when manufacturer 1 is highly capital constrained and holds a large equity share in itself. However, collaborating with a competing manufacturer 2 does not always benefit the capital-constrained manufacturer 1 compared to a situation where both manufacturers have sufficient capital, but it can increase the profits of manufacturer 2 and the entire supply chain in certain conditions. There exists an optimal collaboration mechanism that can achieve a win–win situation, where manufacturer 2 transfers partial payments to the capital-constrained manufacturer 1. The transfer payment is influenced by the cost differences between the manufacturers and the shareholding ratio. These findings can provide valuable management insights to enable competitors to adopt the optimal collaboration method in the face of capital constraints. • Investigate horizontal collaboration among competitors by considering initial capital. • Analyze the impacts of horizontal cooperation on the decisions of supply chain members. • Identify the conditions that capital-constrained manufacturers collaborate with competitors. • Propose an optimal collaboration mechanism that can achieve win–win situation. [ABSTRACT FROM AUTHOR]

This study examines the equilibrium channel structure strategy in a supply chain where a manufacturer can sell products to consumers via/on an online platform, and firms may focus on both profit and consumer surplus. We consider three supply chain channel structures: a reselling channel (mode R), wherein the platform acts as the reseller; an agency channel (mode A), wherein the platform acts as the marketplace; and a dual channel (mode D), wherein the platform acts as both the reseller and marketplace. This study explores the strategic effect of the dual-purpose concern on the co-opetition relationship of firms and derives ramifications for the optimal channel structure. First, the platform may abandon mode A when the manufacturer's consumer surplus concern is high, even if the commission rate is high. This is because the manufacturer's sales revenue may be harmed by the higher selling quantity and lower price resulting from the increased concern of consumer surplus in mode A. The manufacturer prefers mode D (mode R) only when the commission rate is low (high). However, the supply chain may prefer mode R (mode D) when the platform's consumer surplus concern is high (low) irrespective of the commission rate. Second, with dual-purpose firms, both the platform and manufacturer may prefer mode R (mode D) when the commission rate is medium and the platform's consumer surplus concern is high (low). Third, mode A is beneficial to consumer surplus and social welfare as long as agency selling is viable, which occurs when the consumer surplus concern and the commission rate are not too high; otherwise, mode D is the optimal choice, because it enables the supply chain to sell products to consumers in a more flexible way. Finally, the results are robust to a parsimonious dual-purpose platform, an imperfect competitive market, price competition with a dominant platform, multiple competing manufacturers, and a non-zero slotting fee. • The equilibrium strategy with an online platform and dual-purpose firms is studied. • Dual-purpose concern has a strategic effect on the firms' co-opetition relationship. • Sales aggressiveness effect and co-opetition alteration effect are identified. • Influences of the two effects above on firms' channel preferences are examined. [ABSTRACT FROM AUTHOR]

• We consider two kinds of spillover effects and asymmetric demand in live steaming supply chain. • We investigate the manufacturer or e-retailer who cooperate with the KOL will bring both positive and negative spillover effects in a dual-channel live steaming supply chain. • We find that the manufacturer prefers e-retailer to cooperate with the KOL rather than implementing live streaming on his own. • We extend the consumers return and a hybrid scenario (i.e., both manufacturer and e-retailer collaborate with the KOL simultaneously) based on the main model to explore more interesting findings. Live streaming selling is an emerging business mode to promote sales volume by cooperation between the members in e-commence supply chains and the key opinion leader (i.e., KOL). However, the manufacturer or e-retailer who cooperate with the KOL will bring both positive and negative spillover effects in a dual-channel e-commence supply chain. This study investigates the impact of two prominent factors, including spillover effects and consumer acceptance of direct-selling channel on equilibrium results of two sellers under three scenarios, including no KOL scenario, the manufacturer cooperating with KOL scenario (MK), and the e-retailer cooperating with KOL scenario (RK). Our findings show that regardless of the change of the spillover effects and the consumer acceptance, the manufacturer always prefers RK scenario instead of MK scenario. The rationale is that if the live streaming brings the positive spillover effect under RK scenario, the manufacturer can take free-riding behavior to improve his profit; if the live streaming brings the negative spillover effect, the manufacturer might set a lower wholesale price under RK scenario to mitigate the double marginalization. Thus, the RK scenario is the optimal option for the manufacturer in most cases to earn more profit, while the e-retailer has more options under different conditions. Moreover, the main model is extended to discuss the impact of parameters, including different service cost coefficients and consumer returns on sellers' preference. In addition, the result of numerical studies shows that a Pareto zone can be found to achieve a win-win situation when both manufacturer and e-retailer cooperate with the KOL simultaneously. [ABSTRACT FROM AUTHOR]

• Investigate pricing and recycling decisions in dual recycling channels. • Study customer green preference influences on reverse supply chain decisions. • Optimize the recycling channel's configuration for remanufacturers. • Design coordination contracts to maximize profits of reverse supply chain partners. This study investigates the selection of recycling mode and the coordination mechanism in a reverse supply chain (RSC) that consists of one remanufacturer, one traditional recycler, and one smart recycler. We characterize the collection cost differences between traditional recycling and smart recycling, and the scrap rate of used products by smart recycling. We consider three recycling modes with recycling pricing competition, conditional on whether the remanufacturer collaborates with the traditional recycler, with the smart recycler, or with both recyclers. We show that the optimal recycling channel strategy for the remanufacturer is to enroll both the traditional recycler and the smart recycler, and the competition between these two recyclers exerts some non-trial implications on the firms' equilibrium pricing strategy and profitability. By comparing the equilibrium outcomes with a centralized system, we further develop two coordination contracts: a two-part tariff contract and a transfer price combined with a profit-sharing contract, both of which can effectively mitigate double marginalization and improve the supply chain's overall performance. Our findings provide useful guidance for the design of recycling channels, and for the coordination of RSC members to achieve Pareto improvement. [ABSTRACT FROM AUTHOR]

• Platform encroaches on retail if it identifies a product with high sales potential. • Sales-, carbon-, and dual-signaling strategies are explored. • The choice of signaling strategy depends on market status. • Carbon signaling increases separating cost but decreases the environmental impact. Information asymmetry and e-commerce platform encroachment affect the production and sales strategies of manufacturers, wherein sales information is an effective signal to realize information disclosure. Meanwhile, the low-carbon characteristics of products provide manufacturers with an alternative signal option. We establish a dynamic signaling game model in which a manufacturer sells green products through a platform and the sales potential of the product is unknown to the platform in the first stage. The platform encroaches on the manufacturer with high sales potential (HM) in the second stage. This encroachment behavior prompts the HM to disguise itself as a manufacturer with low sales potential (LM) to evade platform encroachment, thereby aggravating information asymmetry. We explore the signals that the LM should use to separate itself from the HM more efficiently and the optimal low-carbon production strategies under three signaling strategies (i.e., sales, carbon, and dual signals). We reveal that the carbon signal gives the HM a stronger motivation to disguise itself than the sales signal, thereby increasing the LM's separating costs but decreasing the environmental impact. In addition, the LM should choose the sales signal in a high-quality market; in contrast, the dual (carbon) signal is optimal in a general (low-quality) market. Our results offer insights for green manufacturing and signal selection under information asymmetry and platform encroachment. [ABSTRACT FROM AUTHOR]

• A chance constrained stochastic programming model is proposed. • The uncertainty, dynamism and diversity of distribution system are considered. • The level assignment method is utilized to address distribution center disruptions. • The multimodal transportation system is introduced for road network disruptions. • An evolutionary algorithm is developed for large-scale cases. This paper presents a comprehensive approach to addressing the challenges of designing a reliable emergency logistics network under the dynamic uncertainty of natural disasters. The proposed multimodal transportation strategy based on level assignments can effectively mitigate the risk of facility disruptions and network uncertainty and enhance the adaptability of the system to diverse emergency supplies. The established two-stage chance constrained stochastic programming model and improved evolutionary algorithm are powerful tools for optimizing the network design and improving its resilience. Based on the real-world case of the Ya'an earthquake in China, the impacts of various practical constraints are analyzed. Several insights into the decision-making process of emergency logistics management are provided: managers should prioritize time utility over cost utility and use multiple aircraft and helicopters, supplies should be sorted into fewer and more important categories and placed in smaller packages for efficient transportation, multimodal transportation should be considered based on geographical and traffic conditions. [ABSTRACT FROM AUTHOR]

In the last decade, different multi-deep rack climbing robotic (MRR) systems have been introduced, particularly in e-commerce warehouses. These systems have great benefits, as aisles are no longer needed, allowing a high storage density on a small footprint. They only need vertical channels through which battery-powered robots can climb the racks, retrieve totes from any desired position, and bring them to a workstation. This paper studies two novel MRR system layouts: the cross and the compact layout. In addition, we compare performance with the more traditional aisle-based layout. The system performance, particularly operational cost and energy consumption, depends on these system layouts. The paper establishes queuing network models to investigate the trade-off between storage capacity and throughput of the system with these three layouts, taking robot blocking prevention into account. We compare the throughput, storage density, horizontal travel time, and energy consumption of the system. The results show that, in most cases, the compact layout outperforms other layouts on throughput. For energy consumption, the choice of layout depends on the footprint. We formulate a model to assist warehouse managers in choosing a layout of minimum annual operational cost, with a required storage and throughput capacity. We also compare the MRR system with an alternative robotic compact storage and retrieval system on operational cost and energy consumption. The MRR system appears to always have lower energy consumption and operational cost. • We establish models to investigate layout design of a new developed warehouse. • Our model considers the energy consumption and robot blocking. • Our model can choose the layout with minimal cost satisfying performance constraints. • Our model can identify conditions when an MRR system outperform other systems. • We propose a framework to trade-off between storage and throughput capacity. [ABSTRACT FROM AUTHOR]

• We examine the interaction between the retailer's free brand spillover and the manufacturer's selection of logistics formats. • Free brand spillover prevents the manufacturer from purchasing the retailer's logistics. • We identify the retailer's preferences on brand spillover adoption. • Brand spillover leads to a win–win outcome for the retailer and the manufacturer. Brand spillover is a pivotal tool that online retailers have adopted to market their new brand products, especially when competing against well-known product brands that prone to free-riding. This common practice seems beneficial to the adopters, but the motivation remains unclear in a supply chain setting. This study explores a co-opetitive supply chain in which a famous brand manufacturer (she) sells her own-branded products by commission via an online retailer (he) with a private label, who determines whether or not to utilize free brand spillover. The upstream manufacturer has to decide between two logistics service strategies, namely, Scenario T (i.e., using third-party logistics to serve the sales of their own-branded products) and Scenario R (i.e., employing the high-quality logistics of the online retailer). We explore the strategic interactions of the two firms and characterize their equilibrium. We reveal that when the decreased brand attractiveness of implementing third-party logistics is low, the brand manufacturer opts for the third-party logistics; otherwise, she embraces the high-quality logistics of the retailer. Interestingly, the brand manufacturer may be less inclined to utilize the logistics service of the retailer if he employs free brand spillover because it will help intensify market competition from the manufacturer's perspective. Moreover, although free brand spillover enhances the attractiveness of the retailer's private label without any cost, the retailer may not adopt it when the brand spillover intensity is low and the decreased brand attractiveness of the third-party logistics is moderate. Finally, we provide managerial insights and guidance for the firms' managers on how to use the above strategies to obtain a "win–win" outcome. We also extend our analysis to the setting with imperfect substitutes, where the deterrence effect of brand spillover on the manufacturer's logistics choice remains the same. [ABSTRACT FROM AUTHOR]

• For multi-dock-door cases, expected distance for Layout C increases dramatically. • Layout B always performs better than Layouts A and C for dual-command travel. • Layout A is wider and shorter than Layouts B and C for dual-command travel. • Increasing number of dock doors increases optimal shape factor for Layouts A and B. • Increasing number of dock doors decreases optimal shape factor for Layout C. This study develops discrete formulations of expected distance traveled in a unit-load warehouse with a middle-cross-aisle. Defining optimal shape factor as the width-to-depth ratio of a rectangle-shaped warehouse minimizing expected distance traveled, our results reveal the number of dock doors and the number of storage/retrieval locations significantly affect the optimal shape factor regardless of the orientation of storage/retrieval locations. Providing computational results based on realistic parameter values, configurations are compared based on the number of storage/retrieval locations. Based on the results, recommendations are provided regarding conditions in which each layout configuration minimizes expected distance traveled. [ABSTRACT FROM AUTHOR]

• We propose a production-distribution problem with ship-buy-exchange options. • Regression and extrapolation guided optimization method is proposed for the problem. • The proposed optimization method is efficient for solving the problem. The production–distribution problem relates to the location of manufacturing plants and warehouses, and to the distribution of multiple commodities from plants to warehouses and from warehouses to customers. Motivated by industrial practices, we extend herein the problem by considering ship–buy–exchange options. Given the complexity of the studied problems, the branch-and-bound method embedded in commercial solvers cannot obtain favorable solutions for medium- and large-sized instances. Herein, we propose a new regression and extrapolation guided optimization method for the solution of these problems. This method obtains the solution values related to the Lagrangian and uncapacitated relaxation problems and performs a regression analysis with these identified solutions and the optimal values to generate the optimal solution patterns. The generated knowledge pertaining to these optimal patterns is extended from small- to large-sized problems using the extrapolation and quadratic regression approaches. When solving large-sized problems, the method employs the extrapolated knowledge in a local search procedure to fix some of the variables and combines an optimization method to solve the reduced-sized problems. We compare this optimization method with other existent methods in the literature. The computational results indicate that with the use of the same computational resources, our optimization method can identify supply chain decisions at lower costs. [ABSTRACT FROM AUTHOR]

• Sustainable production-inventory for multiple factories with JIT logistics. • Cost of the production-inventory, transportation, recycled part recovery and the emission control policy are considered. • Three decision models of cost effective model, carbon tax model and carbon cap and trade model are proposed. • Optimal policy to minimize the expected total cost of the system is obtained. • Sensitivity analysis is carried out and managerial implications are derived. Based on two sustainable manufacturing strategies: resource recycling and emission reduction, this paper addresses a new production-inventory problem of multiple factories with JIT logistics, component recovery and emission control. Motivated by beer industry, we formulate this problem based on two decision mechanisms: (1) PI-CR decision mechanism–sole consideration of component recovery (component recycling) (2) PI-RE decision mechanism–joint consideration of component recycling and emission control. In the PI-RE decision mechanism, two emission control policies—carbon tax and carbon cap & trade are implemented. Numerical analysis is demonstrated to show the application of the models, and the managerial implications are put forward. Results show that emission control and resource recycling strategies have different impacts on the decision, increasing the return rate of recoverable component is an effective approach to currently reduce emissions and cost, while the cap & trade policy is more effect than the carbon tax policy in controlling emissions. [ABSTRACT FROM AUTHOR]

Motivated by the industrial practice that the giant E-commerce platforms are well motivated to engage in offering the extended warranty service, this paper investigates the optimal extended warranty provision with a platform supply chain. Depending on who acts as the extended warranty underwriter with the two online channel modes (i.e., the online agency mode or the online reselling mode), several different extended warranty scenarios are hence formulated, based on which we apply Stackelberg game to address the equilibria. With the analytical studies, our results show several important and interesting insights. For example, but not limited to, it is found that: (a) the dynamics of the optimal extended warranty strategy is largely different in response to various combinations of the warranty underwriters and the online channel modes; (b) the agent, who takes the ownership over the product (i.e., the manufacturer with the online agency mode and the platform with the online reselling mode), acts more responsively in providing the extended warranty service across the two online channel modes; (c) offering the pricing discount is an important intermediary that affects the Pareto improvement zone benefiting the two individuals in this platform supply chain; (d) the specific extended warranty strategies that the warranty underwriter adopts across two online channel modes induce a significant implication for selecting the optimal online channel modes for both warranty underwriters. These results derived in our paper shed lights for the platform supply chain practice on who and when to offer the extended warranty service. • Explore optimal EW service provision with an E-commerce platform supply chain. • Reveal dynamics of the optimal EW strategy in response to various channel modes and warranty underwriters. • Examine who is better off by offing the extended warranty with two different online channel modes. • Examine the best online channel mode for different warranty underwriters. [ABSTRACT FROM AUTHOR]

• Investors generally react positively to production resumptions amid COVID-19. • Early production resumption seen as riskier by investors amid COVID-19. • Local COVID-19 cases exacerbate investor concerns of early production resumption. • High debt pressure renders investors favor early production resumption. • Disruption management mindset needs reassessment to consider risk in recovery. The speed of recovery from supply chain disruption has been identified as the predominant factor in building a resilient supply chain. However, COVID-19 as an example of an evolving crisis may challenge this assumption. Infection risk concerns may influence production resumption decision-making because any incidents of infection may lead to further shutdowns of production lines and undermine firms' long-term cash flows. Sampling 244 production resumption announcements by Chinese manufacturers in the early COVID-19 crisis (February–March 2020), our analysis shows that, generally, investors react positively to production resumptions. However, investors perceived the earlier production resumptions were higher risk (indicated by declined stock price). Such concerns were exacerbated by more locally confirmed cases of COVID-19 but were less salient for manufacturers with high debts (liquidity pressure). This study calls for a reassessment of the current disruption management mindset in response to new evolving crises (e.g., COVID-19) and provides theoretical, practical, and policy implications for building resilient supply chains. [ABSTRACT FROM AUTHOR]

Online platforms typically store multiple products in their warehouses, which often run out of space. Hence we are motivated to study how platforms can effectively allocate their limited warehouse capacity among different products. We establish a game-theoretic model to investigate the optimal warehouse capacity allocation strategy of a platform that serves the dual role of a marketplace and a reseller. Our research reveals that, in the single-product case, a platform's profit increases – first convexly and then concavely – with warehouse capacity. Second, we study the optimal warehouse capacity allocation strategy of a platform in the case of multiple products with different characteristics. We establish that the capacity allocated to a product increases with the commission rate and with the platform's unit procurement cost, and also as the retailer's unit procurement cost decreases. However, the capacity allocation for products characterized by high fixed warehouse operating costs or high unit product volumes depends to a large extent on the platform's total warehouse capacity. The numerical study verifies our theoretical results and generates more management insights. We complete our study with two extensions. The first shows that platforms without warehouses cannot use the same warehouse operation strategy as those that do have warehouses unless (a) the warehouse investment cost coefficient is relatively low and (b) the platform intends to manage all products in its newly built warehouse. Our second extension demonstrates that a platform is generally more willing to store and sell high-quality (than low-quality) products. • An online platform serves dual roles of a marketplace and a reseller. • Consumers have heterogeneous preferences for the platform/retailer. • The platform stores multiple products in its warehouse, which has a capacity constraint. • We investigate the platform's warehouse capacity allocation scheme for multiple products. [ABSTRACT FROM AUTHOR]

Dynamic last-mile distribution of emergency supplies to affected people in post-disaster situations is a key emergency response task, which however is seldom addressed in the literature. To fill this gap, we present a path-based location-inventory-routing model for developing a dynamic last-mile emergency supply distribution plan, including a local distribution center, points of distribution, and demand points. The model can be applied to evolving post-hurricane situations, where candidate point of distribution sites and demands dynamically change. Both cost and equity are incorporated into the planning goal to ensure that distribution of emergency supplies is cost-efficient and fair. With a case study of the previously flooded Red Hook neighborhood in New York City during Hurricane Sandy, we show that our model outperforms different comparison models significantly. Moreover, we obtain managerial insights regarding location, inventory, and routing flexibility, which could be leveraged for post-hurricane distribution practice. • Emergency supply distribution in post-hurricane situations. • Joint dynamic location, inventory, and routing. • Managerial insights on location, inventory, and routing flexibility. [ABSTRACT FROM AUTHOR]

We investigate operational decisions in a distribution warehouse facing stochastic demand and incurring fixed and linear transportation costs for the inbound inventory replenishment and outbound dispatch decisions. In order to realize scale economies associated with transportation both on the outbound and inbound sides, dispatch schedules and replenishment decisions at the warehouse must be synchronized over time. Immediate delivery policies on the outbound side are not financially viable because outbound dispatch operations will benefit from temporal shipment consolidation. Our focus in this setting is the analytical modeling of hybrid shipment consolidation policies and their comparison to the time-based and quantity-based counterparts. To this end, we propose analytical (exact and approximate) methods to compute and compare the cost under hybrid policies relative to its alternatives. Since shipment consolidation impacts customer waiting and inventory holding, we also investigate the average delay per order and average inventory per time unit as two important metrics of the distribution operation's performance, along with the annual cost. We compare these metrics among the alternative under hybrid, time-based, and quantity-based policies. The comparison then allows us to offer an explicit analytical comparison of long-run average cost under these three policies without needing to solve the corresponding optimization problems. Notably, our results offer an analytical characterization of relative cost performance (vis-a-vis the numerical comparison available in the literature) and demonstrate the implications of alternative shipment consolidation policies regardless of the values of model parameters. The results are of practical value in the context of the design and operation of an integrated framework for inventory-transportation systems. • We consider a distribution warehouse where both the inventory replenishment and delivery decisions are subject to fixed and per-unit transportation charges so that outbound dispatch operations and costs will benefit from a temporal shipment consolidation policy. Our interest here is the exact modeling and analysis of hybrid shipment consolidation policies under stochastic demand. • Overall, we demonstrate the relative impact and value of hybrid policies over quantity and time-based policies, analytically. • Our findings are of practical value in the context of selecting an operational policy for shipment consolidation in an integrated framework for inventory-transportation systems. [ABSTRACT FROM AUTHOR]

We study a shuttle-based storage and retrieval system with two independent lifts shared in a single mast. In such a system, two lifts move simultaneously but cannot pass each other, and each retrieval request is finished with the cooperation of a lift and a shuttle. Therefore, given a set of retrieval requests, the retrieval request scheduling problem consists of determining the sequence in which the requests are retrieved and assigning each request to a lift with the objective of minimizing the makespan, considering both the lift-lift interaction and lift-shuttle interaction. The problem is formulated as a mixed-integer programming model and proved to be NP-hard. We propose a decomposition-based adaptive large neighborhood search heuristic to quickly compute near-optimal solutions, using the property that the assignment of requests to lifts for a given retrieval sequence can be exactly solved in polynomial time by a dynamic programming approach. Numerical results indicate that our algorithm achieves a lower makespan than the methods proposed in the literature and used in practice. We also apply the proposed algorithm using real data to a realistic setting that considers multiple planning horizons, showing that it significantly outperforms the policy the company currently uses. • Studies the retrieval request scheduling in a shuttle-based storage and retrieval system with two lifts. • Investigates both the lift-lift interaction and lift-shuttle interaction. • A decomposition-based adaptive large neighborhood search algorithm provides high quality solutions quickly. • The algorithm is verified by a real case study and generates a 16% improvement. [ABSTRACT FROM AUTHOR]

• we price differentially by considering consumers' preference for remanufactured and new products. • we develop the models with or without fairness concern to explore its impact on decision-making. • We incorporate a relative fairness reference point into the manufacturer's utility function and find the manufacturer's fairness concern behaviour to punish the e-platform is detrimental to cultivating a remanufactured product consumer market. • A new contract of "dual-revenue and service-cost sharing" can effectively eliminate the efficiency loss of the two decentralized models. The e-commerce market expands rapidly, and manufacturers sell remanufactured and new products online, making e-commerce closed-loop supply chains face product differential pricing and follower firms' unfair psychology due to the large profit gap between system members. Therefore, we price differentially by considering consumers' preference for remanufactured and new products and develop a centralized model and two decentralized models with or without fairness concern to explore its impact on decision-making. We show that consumers' higher preference for the remanufactured product is conducive to the two products' price and the remanufactured product's sales, then the sensitivity analysis shows that the higher preference positively affects system operation. We next incorporate a relative fairness reference point into the manufacturer's utility function and find the manufacturer's fairness concern behaviour to punish the e-platform is detrimental to cultivating a remanufactured product consumer market and causes the loss of its profits to be greater than that of the e-platform's profit. Additionally, a new contract of "dual-revenue and service-cost sharing" can effectively eliminate the efficiency loss of the two decentralized models. Our paper provides theoretical contributions to product pricing, the setting of a utility function with fairness concern, and the design of dual coordination mechanism. [ABSTRACT FROM AUTHOR]

• The study presents a queueing theory-based analytical model for planning parts-to-picker order-picking systems. • The planning paradigm for efficient order fulfillment considers a human factor termed as "perceived workload." • The study reveals that deploying more robots does not necessarily increase performance. • This work contributes to designing a human-centeric work environment for parts-to-picker order fulfillment systems. In the digital age, coordinating robots and humans is critical in e-commerce warehousing. Motivated by observed industrial practices, this study presents a queueing theory based analytical model to investigate the planning issue of robot-human coordination in a parts-to-picker warehousing system. The critical planning decisions involve finding the optimal number of robots in the warehouse, the expected number of robots at essential locations of the warehouse, and performance analysis of the order picking system. A distinctive feature of this study is the conceptualization of a human factor called perceived workload (which depends on the number of robots) in the order picking planning model for efficient order fulfillment. Our analyses interestingly suggest that deploying more robots in warehouses with a parts-to-picker system does not necessarily increase the warehousing system's performance; instead, a trade-off exists. Additionally, the workload-dependent service rate significantly influences the robots queueing in front of the order picking station (internal queue) and the synchronization station (external queue) in the warehouse. More importantly, this work contributes to the design of a human-centric work environment for parts-to-picker order fulfillment system. [ABSTRACT FROM AUTHOR]

This study investigates the optimal contract design with countervailing incentives under asymmetric selling cost information in a dual-channel supply chain comprising a manufacturer and retailer. The manufacturer's selling cost is private and determines their reservation profit, which gives rise to type-dependent participation constraints. This study provides several intriguing results. First, when the external market correctly evaluates the selling cost advantage of the manufacturer, information asymmetry does not necessarily hamper channel efficiency, even in the dual-channel setting. Second, the retailer may distort the manufacturer's selling quantity either upward or downward, while the retailer may distort their own selling quantity in the opposite direction. Such a synergistic effect unique to the dual channels may help the retailer balance information rent extraction and channel efficiency. Third, the manufacturer may benefit from intensified channel competition as long as the cost differential is not significant, or the cost differential is significant, but the channel substitution rate is relatively low. Finally, further examining the value of information, we demonstrate that the profit differential of the retailer (manufacturer, supply chain) exhibits an inverted U-shaped (U-shaped, hat-shaped) pattern with respect to the low-cost manufacturer's reservation profit. • The optimal contract with countervailing incentives in dual channels is studied. • Information asymmetry does not necessarily undermine channel efficiency. • The supply chain members can benefit from the intensified channel competition. • The efficient manufacturer's proportion may benefit/hurt the firms and supply chain. [ABSTRACT FROM AUTHOR]

This study focuses on an agricultural supply chain with a capital-constrained manufacturer, a bank and an insurer under three power structures: insurer-led Stackelberg (IS), bank-led Stackelberg (BS) and vertical Nash (VN). By establishing a two-level Stackelberg game, we examine the application condition and role of agricultural insurance under three channel structures and reveal the preferences of the supply chain and its participants for different channel structures. This study shows that, in either model, the manufacturer purchases agricultural insurance when the bank is highly loss-averse, but the probability of the manufacturer adopting agricultural insurance in the BS model is lower than that of the VN model but higher than that of the IS model. In these three models, the manufacturer's application of agricultural insurance increases the profits of the manufacturer, bank and insurer but reduces supply chain efficiency. In addition, from the perspective of the profit level and profit share of supply chain participants, for the manufacturer, the VN model of the two-level supply chain power structure betters than the BS and IS models of the three-level supply chain power structure, but the BS model is superior to the IS model; for the bank and the insurer, the higher their leadership positions in which power structure model, the better the power structure model. From the perspective of the overall profit and efficiency of the agricultural supply chain, the model preference of the supply chain is the same as that of the manufacturer. The findings provide new theoretical insights for the application of agricultural insurance and power relationship in the agricultural supply chain. • Application conditions and roles of agricultural insurance in IS, BS and VN models. • Profit relationship among the manufacturer, bank and insurer in IS, BS and VN models. • Preferences of the involved participants for different channel power structures. [ABSTRACT FROM AUTHOR]

• Opportunism, while long viewed as hazard for buyers and suppliers, is even more complex for sustainability-related issues. • Conceptual model jointly considers the codification of expectations and verification of performance (and false suspicions). • Leverage the expertise of a broad set of stakeholders, including non-governmental organizations and industry associations. • Adjust weighting of transactional and relational governance with changes in dyadic relationships and external stakeholdersc. Opportunism has long been highlighted as one of the hazards of complex buyer–supplier relationships. This hazard has become more challenging to manage as the pressure from consumers and the public for improved sustainability-related performance is passed along the supply chain between buyers and suppliers. For example, buyer demands for and supplier implementation of sustainability requirements, such as low carbon emissions or fair worker treatment, are often poorly defined; difficult to document; and subject to change as pressure for immediate improvement builds, as scientific understanding deepens and as societal expectations advance. This complex setting generates tempting openings for either a buyer or supplier to act opportunistically. In an effort to advance both theory and managerial practice, we consider three aspects of sustainability-related opportunism. First, in line with prior research, we focus on defining sustainability-related opportunism as jointly considering the codification of expectations and verification of performance (while allowing for false suspicions). Second, our conceptualization stresses the need to move away from an implied static view to embrace more fully the changing nature of stakeholders' sustainability-related concerns. For example, supply chain relationships evolve based on repeated interactions as firms influence each other's beliefs, practices and outcomes. Third, in a related sense, a dynamic model can combine several theoretical perspectives to inform how the balance of transactional and relational governance mechanisms might adapt as our understanding of sustainability changes, institutional forces evolve, and dyadic relationships mature. [ABSTRACT FROM AUTHOR]

• We study whether an e-commerce platform should establish self-owned logistics. • We explore optimal logistics integration strategies and its influence on firms' decision. • Patform entry decision is the only dominant decision for the manufacturer. • E-tailer's logistics integration strategy hinges on the combination of cross-price elasticity, unit logistics service cost, and commission rate. We study whether an e-commerce platform should costly establish self-owned logistics to support the logistics distribution services of both the manufacturer and e-tailer. We derive optimal logistics integration strategies and investigate the influence of the logistics integration decision on the platform entry decision of the manufacturer. We find that no matter who provides the logistics service, the platform entry decision is the only dominant decision for the manufacturer given that the manufacturer has priority in pricing. For the e-tailer, when the product cross-price elasticity coefficient is large, regardless of the unit logistics service cost, the e-tailer will be better off under the scenario where a third-party provider provides the logistics service. When the cross-price elasticity coefficient is small and the platform commission rate is high, the dominant strategy of the e-tailer depends on the unit logistics service cost gauge of both the e-tailer and the third-party logistics provider. In addition, when the cross-price elasticity coefficient is moderate, building a logistics system is always the most favorable option for the e-tailer. [ABSTRACT FROM AUTHOR]

The warehouse reshuffling problem deals with rearranging pallets in a storage from their initial to desired locations as fast as possible. In most studies involving the rearrangement of pallets, only relocation moves have been considered where a pallet is always relocated to an empty storage location. In this paper, we consider a warehouse with a twin shuttle stacker crane, which is designed to perform swap moves where the pallet at a storage location is swapped with that currently loaded on the stacker crane. We analyze and evaluate the warehouse reshuffling problem with swap moves, both analytically and experimentally, and examine differences to the problem variant with relocation moves. We propose heuristic algorithms as well as a transformation to the traveling salesman problem, which is applied to calculate optimal solutions using exact algorithms. Finally, we present computational results for randomly generated data based on a real-world company setting. • Extended study of the warehouse reshuffling problem. • Considered swap moves as a new type of stacker crane movement. • Comparison between swap and relocation moves, both analytically and experimentally. • Transformation to the TSP, development of exact and heuristic algorithms. • Extensive computational study. [ABSTRACT FROM AUTHOR]

Business interruption (BI) insurance and backup transportation have been widely used in distribution centers’ daily risk management. If disruption occurs, a firm (distribution center) can exert efforts to resume its transportation, although its unit transportation cost during the recovery process is uncertain. This paper studies how ex ante BI insurance can affect the ex post transportation recovery, and compares BI insurance with the ex post action—backup transportation. We investigate four strategies: basic strategy, BI insurance strategy, backup transportation strategy, and mixed strategy (integration of the last two). The distribution center that seeks the least profit loss prefers the mixed strategy to other three strategies. However, the mixed strategy might in turn require longer transportation recovery time than the BI insurance strategy. The BI insurance and transportation recovery are complementary, but the BI insurance and backup transportation are substitutable, the backup transportation and the transportation recovery are also substitutable. We also find that the choice of BI insurance strategy and the backup transportation strategy depends on transportation market, insurance market and distribution center’s operational environments. [ABSTRACT FROM AUTHOR]

We propose a bi-objective, bilevel optimization model for the location of relief distribution centers (DCs) in humanitarian logistics. The upper-level decision-maker (an aid-providing organization) selects locations for capacitated DCs. On the lower level, beneficiaries choose a DC according to distance and amount of supply to be expected. This effects a user equilibrium on the lower decision level. Upper level objectives are to minimize total opening cost for the DCs and total uncovered demand. We develop an exact algorithm for determining the Pareto frontier of the problem, integrating the adaptive epsilon-constraint method, a branch-and-bound procedure, and the Frank-Wolfe procedure. [ABSTRACT FROM AUTHOR]

This paper proposes a deterministic mixed integer linear programming (MILP) model for downstream petroleum supply chain (PSC) network to determine the optimal distribution center (DC) locations, capacities, transportation modes, and transfer volumes. The model minimizes multi-echelon multi-product cost along the refineries, distribution centers, transportation modes and demand nodes. The relationship between strategic planning and multimodal transportation is further elucidated. A case study was considered with real data from the U.S. petroleum industry and transportation networks within Geographic Information System (GIS). A scenario analysis is also conducted to demonstrate the impact of key parameters on PSC decisions and total cost. [ABSTRACT FROM AUTHOR]

Emerging advanced technologies enable logistics service providers (LSPs) to offer supply chain services, such as inventory management, rather than only shipping services. In the traditional retailer-led system, the retailer makes the ordering and transshipment decisions and outsources shipping to the LSP, but this paper studies an LSP-led preventive transshipment system where the LSP is the decision-maker, providing the optimal ordering and transshipment policy solution to the online retailer. An optimal two-threshold transshipment policy is proposed for the LSP-led system. The LSP has lower operational cost for transshipment than the retailer, so one may expect that the LSP can bear more risk of mismatch between the transshipment and uncertain demand, leading the LSP to be more aggressive in transshipment. Interestingly, the analysis reveals that the transshipment quantity chosen by the LSP is not necessarily high. The LSP will transfer less inventory between distribution centers than the retailer if the delivery cost or transshipment cost of the LSP, or the lost sale cost, holding fee, or selling price of the retailer are high. Based on real industrial data, the evaluation result shows that preventive transshipment in the LSP-led system can bring an average increase of 40.33% (even 93.31% for products with high demand uncertainty in the first selling period) in the total profit of the retailer and the LSP over the non-transshipment scenario. Importantly, even if the transshipment cost is the same for both the retailer and LSP, using the optimal policy decided by the LSP rather than the retailer will generate a profit improvement of 6.37%. • An LSP-led preventive transshipment system where the LSP is the decision-maker. • An optimal two-threshold transshipment policy is proposed. • A linear transshipment contract coordinates the transshipment policy in two systems. • The performance of the two systems is evaluated on real industrial data. • The policy decided by the LSP can improve the system profit by 6.37%. [ABSTRACT FROM AUTHOR]

Online customization is very popular among consumers in current e-commerce because of its flexible customization time and diverse product attributes. This paper establishes a dual-channel supply chain composed of one manufacturer and one retailer under mass customization. The manufacturer sells standard products offline via the retailer when providing modular customized products online. Two online distribution strategies of the manufacturer are considered: The manufacturer can choose direct or agency sales through the retailer's online channel. Additionally, the return policy of modular customized products available online is explored. A Stackelberg game model is proposed under each return scenario (with and without online consumer returns) to derive the optimal solutions for different channel strategies. Then, the influence of different parameters on the optimal modularity level and product prices is investigated under each model. Through sensitivity analysis, we found that the cross-price sensitivity coefficient of dual channels has a considerably positive impact on optimal decisions. Furthermore, the manufacturer's channel strategy and return policy decisions are examined. The result indicates that direct sales should be chosen if manufacturers' online channel operating costs are lower than a certain threshold that increases with the revenue allocation ratio under agency sales. When the market demand of the product that is allowed to be returned is much more sensitive to the refund than its return quantity, a full refund policy should be offered for online customized products. These conclusions also apply to the return scenario where full refunds are provided for both standard and customized products. • A two-level dual-channel supply chain under mass customization is studied. • Optimal modularity and pricing decisions are analyzed under a Stackelberg game model. • The manufacturer's channel strategy and return policy decisions are examined. • Manufacturer prefers direct sales if its e-channel operating cost is sufficiently low. • Return policy depends on the sensitivity of demand and return quantity to the refund. [ABSTRACT FROM AUTHOR]

Warehouse reshuffling is a reorganization strategy that consists of repositioning items by moving them sequentially. This study investigates how to optimize warehouse reshuffling and quantifies the effect of common assumptions. A mathematical programming formulation for the general warehouse reshuffling problem, the complexity of the problem, several heuristics based on the problem structure, a formal proof delimitating instances where double-handling can be a productive move, and managerial insights on the performance of reshuffling policies in various environments are presented. Experimental results suggest that the proposed heuristics improve upon a benchmark heuristic by relaxing how cycles are handled and incorporating double-handling. [ABSTRACT FROM AUTHOR]

We study a typical problem within the air cargo supply chain, concerning the transportation of standard Unit Load Devices (ULDs) from freight forwarders' to ground handlers' warehouses. First, ULDs are picked up by a set of available trucks at the freight forwarders' premises within a time window. Next, they are delivered to the ground handlers, also within a time window, and discharged according to a Last In First Out (LIFO) policy. Due to space constraints, ground handlers have limited capacity to serve the trucks and waiting times may arise, especially in case freight forwarders do not coordinate their operations. Therefore, in this paper we consider a cooperative framework where this transportation is coordinated by a central planner. The goal of the planner is to find a proper routing and scheduling that minimizes the sum of the transportation and waiting times at the ground handlers' warehouses, while satisfying the capacity of the trucks. We propose two mathematical formulations, one based on the routing and the other based on the packing aspect of the problem. To solve large instances of the problem, an Adaptive Large Neighborhood Search algorithm is also developed. With numerical experiments, we compare the performances of the two models and the metaheuristic, and we quantify the benefits of the proposed framework to reduce waiting times. • We study a problem in air cargo supply chains about transport of ULDs from forwarders to ground handlers. • We model cooperation in contrast to current individualistic operational practices. • We develop two exact formulations and a meta-heuristic approach. • We test the models using instances representative of a major cargo hub airport. • The experiments show the benefits of the cooperative approach. [ABSTRACT FROM AUTHOR]

• We study a dual-channel supply chain with pricing and service effort decisions. • Three timing scheme strategies are evaluated based on the timing of the decisions. • We show that the wholesale price-led timing scheme strategy produces a Pareto zone. This study investigates a dual-channel supply chain that consists of a manufacturer and two retailers wherein the manufacturer produces and supplies a single product to the retailers. The manufacturer decides the wholesale prices while the retailers decide the retail prices and their service effort levels. Considering three different timing scheme strategies based on the timing of firms' pricing and service effort decisions, we show that the wholesale price-led timing scheme strategy—where the manufacturer takes the wholesale pricing decisions before the retailers announce their service effort levels and prices—produces a Pareto zone, in which all parties in the supply chain including the manufacturer, retailers, and consumers are better off. Moreover, we numerically validate the robustness of this result when there exists an asymmetric cross-effect of the service effort where the service efforts made by one retailer could influence the demand of the other retailer in an asymmetrical way. [ABSTRACT FROM AUTHOR]

• A novel delivery-driven intralogistics synchronization problem is proposed. • The model seeks a tradeoff between manual picking and robotic sorting operations with given delivery requirements. • The proposed synchronized solutions are consistently superior to the sequential solutions under various scenarios. This paper treats intralogistics processing in a typical robotic forward-reserve e-commerce warehouse involving a reserve area with manual order-picking operations and a forward area with robotic parts-to-picker order sorting operations. It is critical to synchronize intralogistics operations between the two areas considering delivery requirements to optimize the performance in terms of makespan and costs. This challenge is formulated as a delivery-driven intralogistics synchronization (DDIS) problem. This paper develops a tailored variable neighborhood search solution method. A series of comprehensive numerical experiments under various scenarios show the superiority and stability of the DDIS as benchmarked with sequential approaches. A substantial reduction can be achieved in both makespan and forward area size, indicating significantly improved intralogistics operational efficiency and space utilization. Managerial insights are also discussed for specific action plans regarding the market size, the labor force, and the trolley configuration. [ABSTRACT FROM AUTHOR]

We study a two-period supply chain consisting of a manufacturer, who participates in a cap-and-trade scheme and faces an uncertain emission permit price, and a retailer, who sells the product from the manufacturer to consumer and faces a price-sensitive demand. In the face of uncertain future permit prices, the manufacturer determines the wholesale price and pollution abatement in each period that reduces pollutants per unit of output one period later. We model the problem as a two-period Stackelberg game, obtain a feedback Stackelberg equilibrium, and investigate the effects of emissions trading on the manufacturer's abatement investment, supply chain performance, and social welfare. We obtain a revenue-and-cost-sharing contract that coordinates the dynamic stochastic supply chain. We show that the abatement level increases in the permit price and decreases in its uncertainty. Moreover, the dirtier the supply chain is, the less is the effect of permit price and the more is the effect of its uncertainty. Both the manufacturer and the channel may benefit from the permit price uncertainty. For a dirty supply chain, a lower price and higher uncertainty intensify the double marginalization effect. If the second-period sales revenue is low enough, the manufacturer benefits and the retailer loses under the coordinating contract. Finally, when the toxicity of pollutants is relatively low (high), the effect of emissions trading on social welfare might be stronger in a cleaner (dirtier) supply chain. • We consider pollution abatement decisions having lagged effects in a two-period cap-and-trade supply chain. • We assume permits are traded in a market just like shares of stocks. • We obtain the optimal abatement decisions in the underlying two-period Stackelberg game. • We obtain coordinating contracts for the two-period supply chain. • We also discuss the social welfare objective for different types of supply chains. [ABSTRACT FROM AUTHOR]

Motivated by a real-world healthcare supply case of a medical implant company, this paper studies a supply network configuration problem that integrates warehouse selections for vendor managed inventory (VMI), inventory policy, and delivery routing optimization together. The problem is a variant of the classic location-inventory-routing problem (LIRP) with both deterministic demand and uncertain demand, where multi-product, multi-period, multi-type delivery, delivery time limit and VMI are considered. Two types of delivery are used: one is the scheduled bulk delivery to the VMI warehouses and the other is direct shipping for hospitals. To address the problem, first, a deterministic MILP model is presented for the integrated LIRP. Then, to deal with the uncertainty in demand, we propose a robust optimization model and transform it into a tractable linear equivalent formulation. Further, considering the effect of COVID-19 pandemic on the demand and delivery time, a new robust model is proposed to account for this special situation. Numerical experiments are conducted to verify the advantage of the proposed robust optimization models. The sensitivity analysis provides some interesting managerial insights, and a real-world case of medical implant supply configuration with 78 hospitals is solved. • present a real-world case of medical implant supply network integrating VMI and direct shipping. • the first location-inventory-routing problem in a healthcare supply chain and its MILP model. • the first robust optimization model for LIRP, especially in the healthcare system with VMI. • extend the robust healthcare LIRP model with considering impacts of the COVID-19 pandemic. • the real-world healthcare supply problem is solved, and managerial insights are provided. [ABSTRACT FROM AUTHOR]

• We define the recyclable waste transportation vehicle parking center problem. • A novel integrated multi-criteria decision-making method is developed. • Decision-making preference and criteria weight have a significant impact. • The advantages and robustness of method have been verified by sensitivity analysis. This study is motivated by a third-party logistics company in a modern city in China that manages the transportation of recyclable waste for the municipal government. Research in the area has rarely attended to the location of recyclable waste transportation vehicle parking centers (RWTVPCs). This study develops an integrated multi-criteria decision-making (MCDM) method through the decision-making trial and evaluation laboratory (DEMATEL) approach, entropy weight (EW), and weighted aggregated sum product assessment (WASPAS) to select suitable RWTVPC sites. An interesting finding is that the MCDM method is more suitable for this task than the traditional location allocation model. A comparison of three MCDM methods verified the advantages of the proposed method in terms of stability and reliability. Moreover, we found through a sensitivity analysis that the ranking of alternatives was greatly affected by changes in the criteria weights but was not sensitive to changes in the parameters of the preferential decisions. Besides, the proposed method has advantages in terms of reducing the cost of transportation of recyclable waste and improving the efficiency of transportation. This provides a reference for the large-scale collection of urban recyclable waste. This paper provides some important implications for waste management that are useful for the implementation of the DEMATEL-EW-WASPAS method. [ABSTRACT FROM AUTHOR]

This paper studies bundling in a two-level supply chain consisting of a manufacturer and a retailer. Past literature offers deep insights into product bundling but overlooks the implications of bundling at different supply chain levels, i.e., downstream and upstream, which has been widely observed in practice. In this paper, we allow either the manufacturer or the retailer to initiate bundling. We find that production costs drive the manufacturer's and the retailer's bundling decisions differently. In particular, the manufacturer adopts bundling when costs are not too high or low, while the retailer executes bundling when costs are medium or moderately low. We also characterize the conditions under which bundling benefits the firms. Our analysis reveals that while the manufacturer always benefits from initiating bundling, this is not necessarily the case for the retailer. Interestingly, when costs are moderately high, the retailer benefits from manufacturer-initiated bundling but is hurt by retailer-initiated bundling. Furthermore, whether initiated by the manufacturer or the retailer, bundling can lead to higher supply chain profit, consumer surplus as well as social welfare. Our results help clarify the impact of upstream and downstream bundling decisions on supply chain dynamics. • We compare bundling under manufacturer- and retailer-initiated scenarios. • The production costs drive the bundling decisions differently in each scenario. • We characterize the conditions under which bundling benefits the firms. • Bundling can lead to higher channel profit, consumer surplus, and social welfare. [ABSTRACT FROM AUTHOR]

In this paper, we consider an omnichannel retailer (o-tailer) selling multiple products through a capacitated retail network over a multi-period horizon, where each inventory facility is equipped with a certain ability to fulfill the orders regardless of the channel. We study omnichannel retail operations with the implementation of ship-from-store to minimize the o-tailer's expected total cost. At the start of each period, the o-tailer determines how much to replenish each product for each distribution center (DC), and how to allocate its inventory from DCs to different stores. At the end of each period, the o-tailer decides from which DCs and/or stores to fulfill the realized online demands. We formulate the problem as a multi-period stochastic optimization model and adopt a robust two-phase approach (RTA) to solve it. Phase 1 determines the binary replenishment decisions using a target-oriented robust optimization approach, whereas Phase 2 uses a linear decision rule to adaptively determine the replenishment, allocation, and fulfillment quantities. Numerical experiments suggest that the RTA outperforms existing approaches and can efficiently produce good-quality and robust solutions for realistic problem instances. A further sensitivity analysis explores the impacts of various system parameters on the RTA's performance and the benefits of omnichannel fulfilment. • Formulate a multi-period stochastic model for the omnichannel retail operations with the implementation of ship-from-store. • Adopt a tractable robust two-phase approach to solve the problem under demand distributional ambiguity. • The approach produces good-quality solutions with robust performance while preserving tractability for realistic problem instances. • Investigate the impacts of various parameters on the benefits of omnichannel fulfillment. [ABSTRACT FROM AUTHOR]

• A new crowd-shipping system is studied. • A Mixed Integer Nonlinear Programming model is formulated. • An Adaptive Large Neighborhood Search (ALNS) heuristic is developed. • The proposed ALNS outperforms CPLEX in terms of computational time. • Sensitivity analyses are provided as interesting managerial insights. This research introduces a new variant of the vehicle routing problem in the last-mile delivery process - namely, the Crowd-Shipping Problem with Time Windows, Transshipment Nodes, and Delivery Options (CSPTW-TN-DO). Two types of fleets (i.e., dedicated vehicles and occasional drivers) are available to serve three types of customers. Type 1 customers require a home delivery. The parcel of type 2 customers must be sent to the selected alternative delivery point (ADP). Type 3 customers have the flexibility to either receive their parcel at home or at the selected ADP. Dedicated vehicles are able to serve all types of customers, whereas occasional drivers only make home deliveries. The objective of CSPTW-TN-DO is to minimize the total distribution cost of employing both fleets. We formulate a Mixed Integer Nonlinear Programming (MINLP) model for the problem and solve the model by the commercial solver CPLEX after applying a linearization process. We also propose an Adaptive Large Neighborhood Search (ALNS) to solve a set of newly generated CSPTW-TN-DO instances. The computational results indicate that the proposed ALNS provides high-quality solutions. In addition, we show that the VRPTW with a primary objective of minimizing the total distribution cost is a special case of CSPTW-TN-DO, and that the proposed ALNS achieves comparative performance to the state-of-the-art algorithms for VRPTW. After analyzing several scenarios, we conclude that simultaneously considering occasional drivers, transshipment nodes, and delivery options offers a great opportunity for a last-mile delivery system to reduce its total distribution cost. [ABSTRACT FROM AUTHOR]

• Customer returns are considered in a supply chain with a manufacturer and a retailer. • Customer returns rate information is known to retailer, but unknown to manufacturer. • Manufacturer's optimal information strategy and retailer's preference are examined. • There exists a region where the manufacturer does not acquire the information. • Both parties may have consistent preference on information strategy. We develop a model for a supply chain with a manufacturer and a retailer facing customer returns, in which information about the rate of customer returns is obtained and held asymmetrically. The retailer implements a full refund policy and accepts returns, collecting and retaining data on the rate of returns. The manufacturer is uninformed about the returns rate. The manufacturer has four strategies as to returns rate information: not acquiring the information, acquiring the information through a side payment contract, offering a buyback policy in order to participate in the returns process, or encroaching with a direct channel. We identify the manufacturer's optimal information strategy and discuss the retailer's preferences. We also identify win–win outcomes for the manufacturer and the retailer. We find that a buyback policy helps the manufacturer to obtain returns rate information for free when the salvage value is the same for the manufacturer and the retailer. When the manufacturer's salvage value is relatively low, there always exists a region where the manufacturer does not try to acquire the information. The retailer may benefit from the cases in which the manufacturer does not acquire the information, adopts a side payment contract, or institutes a buyback policy, but the retailer is always worse off from the manufacturer's channel encroachment strategy. Both the manufacturer and the retailer may have consistent preferences as to the manufacturer's strategy. [ABSTRACT FROM AUTHOR]

Business-to-consumer platforms (e.g., Tmall.com and JD.com) demand the efficient retrieval handling of storage systems. The amount of a certain item to be retrieved is often less than a pallet load; thus, dynamically re-assigning the storage locations of nonempty pallets after each retrieval may improve retrieval operational efficiency. We call such a dynamic decision on the location for the returned pallets relocation. We treat the relocation of each non-empty pallet as an operational decision rather than a tactical decision. Due to its dynamic nature, we formulate relocation as a dynamic program that minimizes the total crane travel time. To overcome the computational curse of dimensionality, we propose several heuristic relocation policies that are inspired by conventional storage location assignment policies. Based on the findings from preliminary numerical experiments, we design an approximate dynamic programming-based relocation policy that achieves a greater operational efficiency improvement than the aforementioned heuristics. Through a more extensive set of numerical experiments, we find that random relocation is generally no better than no relocation, and that closest-open relocation is effective across different rack shapes, crane velocity configurations, and retrieval characteristics. Our experiments also show that the approximate dynamic programming-based relocation policy consistently yields further improvement over closest-open relocation, at the expense of higher (but still affordable) computational complexity. • Dynamic storage relocation of Automated Storage/Retrieval Systems (ASRS) is studied. • Storage relocation can improve ASRS with less-than-pallet-load retrieval operations. • Storage relocation policies adopted by many B2C logistics systems are inefficient. • The proposed policy shines under various warehouse/retrieval characteristics. • Effective relocation policy can mitigate adverse effects of poor warehouse design. [ABSTRACT FROM AUTHOR]

• Introduction of private brands may benefit the manufacturer. • The manufacturer may benefit more with a higher proportional fee. • Investment effect can act as a buffer when the manufacturer is faced with threats. • The manufacturer prefers the agency mode after private-brand introduction. • If investment efficiency is high, the manufacturer prefers the reselling mode after private-brand introduction. This paper investigates the effect of a platform's private-brand introduction on a manufacturer that sells products through that platform by considering the investment effect and selling mode. We first analyze the role of the private brand and the investment effect on the manufacturer under the reselling mode and the agency mode, respectively. We then examine how the private brand introduction and the investment effect impact the manufacturer's optimal selling mode choice. The introduction of a private brand is believed to threaten manufacturers that sell goods through the platform. However, we find that, in the reselling mode, a manufacturer can benefit from private-brand introduction through the increased demand and wholesale price generated by the platform's increased marketing investment, as long as the competition intensity remains relatively low. In the agency mode, the manufacturer benefits more with a higher proportional fee after private-brand introduction. Further, the investment effect can act as a buffer, making the manufacturer more robust when facing threats. Also, after private-brand introduction, the manufacturer prefers the agency mode, but when it comes to investment and high investment efficiency, it is wiser for the manufacturer to keep reselling when facing private-brand introduction. [ABSTRACT FROM AUTHOR]

A retail platform or intermediary can purchase products from multiple upstream manufacturers through different types of contracts. Traditional studies assume that the contracts and their terms are common knowledge; that is, each party of the supply chain can observe the contracts reached. In practice, however, this is not necessarily the case. Taking the result under contract observability as a benchmark, this paper investigates the contracting of the supply chain comprised of one retail platform and two upstream manufacturers when contracts are unobservable. Two representative contracts are considered: the traditional and succinct wholesale price contract and the nonlinear, more complicated, two-part tariff contract. We apply the framework of perfect Bayesian equilibrium and also use passive beliefs as an equilibrium refinement criterion to analyze the model. Our study shows that the classical result – that the more complicated contract can increase channel profitability – may no longer be valid when contracts are unobservable to competing manufacturers. On the contrary, the manufacturers and the whole supply chain can benefit from adopting wholesale price contracts rather than two-part tariff contracts. To examine the robustness of our findings, we consider a commission fee contract and another classic demand function in an extended analysis, and the results show that the main findings and key insights remain valid. The intuition behind these findings is that the unobservability of contracts affects the opportunistic behavior of competing manufacturers. The practical value of these findings is mainly reflected in the evidence that supply chain members must carefully consider the impact of contract (un)observability when agreeing on contracts. • Investigates the effect of contract unobservability on the supply chain with upstream competition. • Reveals that a simpler wholesale price contract performs better with contract unobservability. • Verifies the robustness of the results and insights by incorporating other classical demand function. • The impact of contract (un)observability should be given enough attentions in practice. [ABSTRACT FROM AUTHOR]

Abstract: The warehouse network re-design problem includes integrating or eliminating existing warehouses and establishing new sites. In this paper, we incorporate variability in product demand and operational costs with a two-stage stochastic modeling approach. We use the Sample Average Approximation (SAA) approach together with Benders decomposition to provide a solution method. Our results indicate not only that the stochastic solution is an improvement over the deterministic solution but also that the solutions’ differences grow with increasing uncertainty. The stochastic solutions show more robustness than the deterministic solutions. The computational results show that a change in the type of probability distribution of the stochastic parameters does not significantly affect the value of the stochastic solutions. [Copyright &y& Elsevier]

Abstract: We show how to configure a cross aisle in a unit-load warehouse to facilitate travel between storage locations and multiple pickup and deposit points on one side. We use our models to investigate designs having two types of cross aisles—those that form a “Flying-V” and those that form an “Inverted-V.” Our numerical results suggest that there is a benefit to using a Flying-V aisle design, but the benefit is more modest than in the case of a single P&D point. Thus, to the extent practicable, pickup and deposit points should be concentrated toward the middle of the warehouse. [Copyright &y& Elsevier]

Abstract: We study the location-inventory problem in three-level supply networks. Our model integrates three decisions: the distribution centers location, flows allocation, and shipment sizes. We propose a nonlinear continuous formulation, including transportation, fixed, handling and holding costs, which decomposes into a closed-form equation and a linear program when the DC flows are fixed. We thus develop an iterative heuristic that estimates the DC flows a priori, solves the linear program, and then improves the DC flow estimations. Extensive numerical experiments show that the approach can design large supply networks both effectively and efficiently, and a case study is discussed. [Copyright &y& Elsevier]