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As consumers' green awareness continues to grow, the level of a product's eco-friendliness and the quality of its marketing have become significant factors in shaping consumers' purchasing decisions. The power structures within the supply chain, as well as corresponding government subsidy policies, are also key elements influencing sustainable strategies for the green supply chain. In a green supply chain comprising one manufacturer and one e-commerce platform, two sales models exist within the online e-commerce platforms: reselling and agency selling. This paper establishes and analyzes three distinct Stackelberg game models, namely: manufacturer-led model without subsidy (bm), manufacturer-led model with subsidy (sm), and platform-led model with subsidy (sp). The results are shown as follows, with the rise of consumers' environmental awareness, more consumers opt for green products, inspiring the manufacturer to increase its optimal greenness and platform to enhance its optimal marketing efforts level. Notably, government subsidies provide a significant stimulus. An increase in the green technology cost coefficient leads to a decline in the manufacturer's profits across all three modes. Intriguingly, the manufacturer's profits are always highest in the sp mode. As the marketing efforts cost coefficient increases, the platform's profits decrease in the bm and sm modes. In contrast, in the sp mode, the platform's profits increase rather than a decrease. The choice of mode primarily depends on the platform's marketing efforts cost coefficient. When this coefficient exceeds a threshold, the platform chooses the sp mode. However, due to the relatively low marketing efficiency in this scenario, the manufacturer prefers the sm mode. For the government, the sp mode involves agency selling, serves as an effective mechanism to redistribute subsidies, thereby yielding the maximum social welfare benefits. Management insights are provided for the manufacturer and platform managers to make decisions about the degree of greenness and marketing efforts level, along with insights for governments to optimize subsidy policies. [ABSTRACT FROM AUTHOR]

Blockchain is widely used in the manufacturing industry. This paper establishes a dual-channel supply chain composed of a manufacturer and an e-retailer. A monopoly manufacturer conducts indirect online selling through retailers as well as direct offline selling. The manufacturer chooses to adopt a self-built blockchain traceability system (SBT) or a third-party blockchain traceability system (TBT). Game analysis is developed to depict the pricing decision for the manufacturer and e-retailer. The optimal pricing decisions of the supply chain between manufacturer and e-retailer for different blockchain traceability strategies are obtained. We explore the influence of consumers' traceability awareness on the decisions of dual-channel supply chain members when adopting different blockchain traceability strategies. The main results show that when the fee paid to the blockchain service provider is low, the manufacturer will prefer to adopt TBT. Moreover, we prove that consumers' traceability awareness, the cost of adopting TBT, the blockchain traceability technology level, and the research and development cost factor of blockchain technology could affect the decisions of supply chain members. Finally, some management suggestions are provided. [ABSTRACT FROM AUTHOR]

Online order picking has become one of the key issues for ensuring the service level of modern retail e-commerce. This paper studies the online order batching and batch assignment problem (OOBBAP) with multiple pickers of picker-to-parts warehouses. First, we establish a biobjective optimization model that minimises the total picking time and order response time. Then, an online order batching algorithm embedded with the sequencing algorithm and optimal path algorithm is designed to solve the model. Finally, we use retail ecommerce from China with data on 87,277 orders of an online shopping festival to examine the optimization results of the model. The optimization results of the key indicators are satisfactory. The optimization rates of the maximum order response time, average order response time, total picking time, average picking time of picking posts, picker total walking distance and average walking distance are between 7.45% and 30.20%. The model demonstrates an excellent ability to solve the OOBBAP in multiple picker warehouses, which can provide a reference solution for similar picking problems in picker-toparts retail e-commerce warehouses. [ABSTRACT FROM AUTHOR]

Storage location allocation has a great impact on the utilization of warehouse storage space, operation efficiency, cost and employee well-being. We establish a multiobjective model of storage location optimization of manual picking zones by adopting the principle of high delivery frequency priority, correlation principle and large-capacity priority principle. The intelligent genetic algorithm and particle swarm optimization (IGPSO) algorithm are used to solve the multiobjective model. By using the 3.6 million-order data of a retail e-commerce warehouse to optimize the storage location allocation, the results show that the maximum and average response time, the actual order picking time, the daily walking distance of the picker and other key indicators achieve good results. This research can be well applied to optimize the storage location allocation of similar retail e-commerce warehouses. [ABSTRACT FROM AUTHOR]

Regarding the fulfillment optimization of online retail orders, many researchers focus more on warehouse optimization and distribution center optimization. However, under the background of new retailing, traditional retailers carry out online services, forming an order fulfillment model with physical stores as front warehouses. Studies that focus on physical stores and consider both order splitting and store delivery are rare, which cannot meet the order optimization needs of traditional retailers. To this end, this study proposes a new problem called the "Multi-Store Collaborative Delivery Optimization (MCDO)", in which not only make the order-split plans for stores but also design the order-delivery routes for them, such that the order fulfillment cost is minimized. To solve the problem, a Top-K breadth-first search and a local search are integrated to construct a hybrid heuristic algorithm, named "Top-K Recommendation & Improved Local Search (TKILS)". This study optimizes the search efficiency of the breadth-first search by controlling the number of sub-orders and improving the initial solution of the local search using a greedy cost function. Then achieve the joint optimization of order-split and order-delivery by improving the local optimization operators. Finally, extensive experiments on synthetic and real datasets validate the effectiveness and applicability of the algorithm this study proposed. [ABSTRACT FROM AUTHOR]

Most studies on supply chain coordination assume that consumers are rational. However, with the development of e-commerce, consumer-bounded rationality has become an important issue with respect to supply chain coordination. Based on the assumption that some consumers are loyal to the offline shop and others are reference-dependent, this article examines the mechanism of vertical restraints and their competitive effects. This research study found that compared with the assumption of rational consumers used in previous literature, vertical restraints help internalize the "channel price gap externality" when consumers are loss averse. When separately operating, the offline shop will set a higher price due to its consumers' higher loyalty and willingness to pay. However, given the positive externality of this price to the online retail sales, the offline price is still lower than the level under vertical integration. When the upstream manufacturer achieves supply chain coordination with vertical restraint contracts, the channel price gap externality is internalized, and the channel price gap expands to stimulate loss-averse consumers' purchasing decisions. [ABSTRACT FROM AUTHOR]

The timeliness of order deliveries seriously impacts customers' evaluation of logistics services and, hence, has increasingly received attention. Due to the diverse and large quantities of orders under the background of electronic commerce, how to pick orders efficiently while also adapting these features has become one of the most challenging problems for distribution centers. However, previous studies have not accounted for the differences in the stochastic characteristics of order generation, which may lead to asymmetric optimization problems. With this in mind, a new asymmetric polling-based model that assumes the varying stochastic characteristics to analyze such order picking systems is put forward. In addition, two important indicators of the system, mean queue length (MQL) and mean waiting time (MWT), are derived by using probability-generating functions and the embedded Markov chain. Then, simulation experiments and a comparison of the numerical and theoretical results are conducted, showing the usefulness and practicalities of the proposed model. Finally, the paper discusses the characteristics of the novel order picking system and the influence of the MQL and MWT on it. [ABSTRACT FROM AUTHOR]

The crisis that affected Brazil as a result of the coronavirus disease 2019 (COVID-19) pandemic impacted several businesses. However, the distribution of products sold by e-commerce is one business that had increased its demand on a large scale. The Southeastern Region of Brazil has a wide territorial extension, a high number of inhabitants, and difficulties with transport logistics. Thus, this work presents a solution for choosing a location for the installation of an e-commerce warehouse, considering three possible locations, in two different Brazilian states: Rio de Janeiro and Sao Paulo. Our main objective is to identify policies that allow a balance in the trade-off between risks and service levels. Specifically, we seek to decrease logistics costs and ensure improvements in services. The choice of a location for a warehouse is a decision problem that can be solved with multiple criteria decision-making (MCDM). Three methods of MCDM have been applied: Fuzzy Decision Approach (FDA), Multi-Attribute Utility Theory (MAUT), and Multi-Attribute Value Theory (MAVT). The reasons for the application of the MAUT and MAVT methods originate from their easy understanding and use, while the concepts of FDA address the consideration of uncertainty. To maintain the simplicity of MCDM application, the FDA model is symmetric, even with symmetrical and asymmetrical fuzzy sets. Important criteria were the distance from capital cities, distance to ports, distance to airports, distance to highways, and the costs of land. Three different locations were considered for the installation of the warehouse. All three methods, FDA, MAUT, and MAVT, resulted in Location 1 as the best location for the installation of the warehouse for the distribution of e-commerce products. A discussion on conceptual qualitative divergences and the small quantitative divergence of results is also included in the conclusions. [ABSTRACT FROM AUTHOR]

Adding a new sales channel creates new challenges in terms of inventory management due to the difference in order size and fulfillment times as well as the uncertainty in the demand of both channels. The objective is to design a mixed inventory management model composed of a joint replenishment model for the traditional retail channel and a continuous review model for the Online channel, both with stochastic demand, due to the difference they present in their operational management. A heuristic method was developed to solve the problem and for its validation numerical examples were evaluated where an 11% reduction was found in the cost of the dual channel warehouse inventory policy with the proposed model which seeks to represent an approximation of management such as joint replenishment at retailers. [ABSTRACT FROM AUTHOR]

In recent years, with the integration and development of the internet and various industries, the social economy has begun to develop toward diversification. The rise of e-commerce logistics has driven the transformation and upgrading of the manufacturing industry and injected new vitality into e-commerce. It also promotes the new development of cargo warehousing as one of the important components. Traditional warehousing began to gradually develop toward intelligent informatization, relying on emerging science and technology. Smart warehousing also brings valuable development opportunities and challenges to the manufacturing industry. On the basis of the existing achievements, this paper proposes a solution of an intelligent warehousing system for e-commerce logistics based on multimedia technology. It, firstly, introduces the structure of the intelligent warehousing system in detail, and then, it analyzes the requirements for the functions of the intelligent warehousing system based on multimedia technology. It includes user management, receiving business, storage business, tally business, and outbound business. In the end, the intelligent warehousing system based on multimedia technology and the traditional intelligent warehousing system are compared and tested for system function and experience. The test results show that the work efficiency of the intelligent warehousing system based on multimedia technology is significantly higher than that of the traditional intelligent warehousing system. In terms of user experience, the performance of the intelligent warehousing system based on multimedia technology has reached 8 points and above. It proves its usefulness and effectiveness. [ABSTRACT FROM AUTHOR]

Given the different fairness preferences of online retailers and their investment in emission reduction and revenue sharing with manufacturers, an e-commerce low-carbon supply chain decision model was established using Stackelberg game theory under three circumstances: no fairness preference, symmetric fairness preferences, and asymmetric fairness preferences. Results reveal that the asymmetric fairness preference behaviors of online retailers weaken the manufacturers' profits, where the online retailer's utility is negatively correlated with its asymmetric fairness preference coefficient. The real fairness preference coefficient of the online retailer estimated by the manufacturer is negatively correlated with the manufacturer's wholesale price and carbon emission reduction. The revenue sharing proportion of the manufacturer presents a positive correlation with its wholesale price but shows no correlation with the retail price, the green degree, or the supply chain profit. Within a feasible region, the proportion of the online retailer's investment in emission reduction is positively correlated with the manufacturer's profit, the online retailer's utility, the total utility of the supply chain, the carbon emission reduction, the product's retail price, and the product's wholesale price. [ABSTRACT FROM AUTHOR]

This paper constructs an e-commerce supply chain (ECSC) that includes one e-commerce platform and one low-carbon manufacturer. Based on bounded rationality, the complexity of pricing, service level, and emission reduction effort decisions in an ECSC is investigated in two scenarios: horizontal Nash (HN) game and long-term platform Stackelberg (LPS) game. Finally, the effects of different adjustment speeds and critical parameters on decision variables, profits, and system stability are analyzed through various numerical simulations. The study revealed that the price adjustment parameters have a more noticeable impact on the stability and profit of ECSC in both scenarios. It is interesting that the chaos caused by the adjustment speed of the platform's decisions leads to a decrease in the platform's profits. However, it causes continuous fluctuations in low-carbon manufacturers' profits. In the HN game, when the increase of critical parameters is beneficial to low-carbon manufacturers, the system's stability will decrease. In addition, the system's stability in the LPS game is only affected by the adjustment speed of the platform's decisions. By comparison, we find that the adjustment of the platform's price plays a vital role in the ECSC under different power structures. Finally, the adaptive adjustment mechanism is used to control the chaos caused by the price of the platform in the ECSC system. [ABSTRACT FROM AUTHOR]

In recent years, thousands of products are ordered by online every day with the development of e-commerce and customers expect their orders to be delivered on the same day or the following day despite the significant increase in the number orders. This expectation in delivery times, becoming one of the most important factors that affect customer satisfaction, reveals the requirement of effective and efficient operations in warehouses to deliver orders to customers on time. In warehouses, the most time is spent in storing/retrieving products on/from shelves (storage and retrieval). The expected distances traveled for storing and retrieving vary depending on the warehouse design, the mid-cross-aisle location and operation type. In this study, mathematical formulations of expected distances for single-command, dual-command and cross-docking operations are developed in a discrete storage space. A nonlinear-integer programming model is developed for mixed-command operations by using discrete formulations developed and the optimization model is solved for two different scenarios. As a result, considering the effect on expected travel distance of the warehouse design, the mid-cross-aisle location and the operation type, the optimal location of the mid-cross aisle and warehouse design are determined in a unit-load warehouse having multiple dock doors along two adjacent walls. [ABSTRACT FROM AUTHOR]

In supply chain systems, with the rapid growth of electronic commerce, a lot of firms are puzzled with the issue about how to best manage the distribution channels. In this article, we study the online channel management, pricing and coordination (i.e., optimization) in supply chains in which both channel members have an opportunity to introduce an online channel for retailing products to consumers. We focus on examining the roles of “channel leadership,” online channel decision and customer channel preference. Through systematic analysis and comparisons, we obtain some important insights, such as: 1) consumers do not care about who the channel leader is, but care about who establishes the online channel; 2) the dual-channel structure can make the manufacturer charge a “higher wholesale price” regardless of who establishes the online channel and who the channel leader is; and 3) no matter who the channel leader is, the dual-channel structure hurts the consumers’ benefit when the retailer establishes the online channel. We also illustrate how the proposed dual-channel supply chain can be optimized with the use of supply chain contracts. [ABSTRACT FROM AUTHOR]

Robotic Mobile Fulfillment System (RMFS) is a new type of parts-to-picker order picking system and has become the development trend of e-commerce logistics distribution centers. There are usually a large number of tasks need to be allocated to many robots and the picking time for e-commerce orders is usually very tight, which puts forward higher requirements for the efficiency of multirobot task allocation (MRTA) in e-commerce RMFS. Current researches on MRTA in RMFS seldom consider task correlation and the balance among picking stations. In this paper, a task time cost model considering task correlation is built according to the characteristics of the picking process. Then, a multirobot task allocation model minimizing the overall picking time is established considering both the picking time balance of picking stations and the load balance of robots. Finally, a four-stage balanced heuristic auction algorithm is designed to solve the task allocation model and the tasks with execution sequence for each robot are obtained. By comparing with the traditional task time cost model and the algorithm without considering the balance among picking stations, it is found that the proposed model and algorithm can significantly shorten the overall picking time. [ABSTRACT FROM AUTHOR]

We consider the joint decision problem of renting three types of warehouses and allocating products among the warehouses in an E-commerce supply network, which are supplier, regional, and local warehouses. The research is motivated by a real-world emerging problem in an open warehouse platform (OWP) environment. We focus on the distinct characteristics of a network using an OWP and its underlying cost drivers and propose a nonlinear mixed integer programming model that reflects the interdependencies among inbound transportation, warehousing, delivery, and return, as well as time-sensitive demand. Our nonlinear model with multiple products is converted into a linear form so that it can be solved by a solver. The application of the model to a real-world case of a leading E-commerce company in China demonstrates a significant potential to increase profit, and our sensitivity analysis provides insights for operations managers into warehouse rent and inventory decisions. [ABSTRACT FROM AUTHOR]

A flow picking system in which the existing picking list is updated in real time has been considered as an effective solution for e-commerce warehouses to increase order fulfillment efficiency. The pivotal issues of performance analysis of flow picking systems, and comparison between batch picking systems and flow picking systems are of great concern, both for academics and practitioners of warehouse operation management. In this study, we first develop analytic models to estimate the critical performance indicators of a flow picking system, including picking density and turnover time of an order. Second, we leverage the proposed models and real warehouse data to compare the performance of batch picking and flow picking systems through simulation. Our results show that a flow picking system requires fewer order pickers and shorter walking distances than a batch picking system in most scenarios, especially those with a higher order arrival rate to achieve the same service level. Our study can provide valuable guidelines to warehouse managers and decision-makers for choosing an order fulfillment solution by comparing a batch picking system and a flow picking system. [ABSTRACT FROM AUTHOR]

E-commerce processes must work in harmony and simultaneously with warehouse and operation activities on the logistics side. E-commerce activities include the processes resulting in positive order requests. Routing orders in a correct and fast way depends on the integrated and seamless solution of warehouse technologies with ERP systems. The aim of this study is to manage the warehouse capacity efficiently by predicting the products to be demanded in the future. In the study, the data of an e-commerce company operating at national level was processed in Azure ML (Machine Learning) Studio and the future and demand forecast values were tried to be calculated. The study in which the new generation machine learning cloud platform was introduced and model performances were measured comparatively aims to plan and develop ecommerce sector and logistics activities based on data. [ABSTRACT FROM AUTHOR]

Since the COVID-19 pandemic hit last year, countries locked their borders. Thus, international shipping deteriorates drastically. Simultaneously, social distancing increased the need for immediate online consumption and fast home delivery. In the non-digital world, products still need to be shipped to their destination using trucks, trains, airplanes, and ships. Simultaneously, requirements for volumes of goods, transport costs, external limiting factors, etc., must be precisely defined. The article aims to find the optimal location selection solution based on the created mathematical model of the Modified Steiner-Weber Problem with restrictive conditions. The model allows for the central warehouse's optimal location and minimizes distribution costs from the central warehouse to subwarehouses/ branches located in individual EU countries. The mathematical model has been applied to a case study of a selected e-commerce dealing, which has established branches in capital cities but does not have an established central warehouse. Systematization of literature sources and approaches to solving the problem of e-commerce distribution center location showed that 86% of the studied companies plan to use on-demand warehousing in the next three to five years. Therefore, the need for warehousing would be preserved. The authors noted that they do not necessarily need to have it in-house. Consequently, fulfillment centers and warehouses would likely continue to be a significant component in the future logistics system. This research would like to stress how important the management of the effective optimization of e-commerce distribution center location is and how to achieve it. The success of Amazon in the US, Europe, and Alibaba in China has genuinely redefined consumer expectations. With the emergence of services like Amazon Prime, consumers now expect same-day delivery. The solution enabling this evolution has been a mix of manufacturing where the production costs are optimal, just-in-time shipping, highly automated fulfillment centers, and mobile connectivity growth. The proposed model results showed that the best location for a central location and storage center concerning the e-commerce environment, including minimum annual transport costs, is near Bristol in the United Kingdom. Eighty-six percent of the companies in the study plan to use ondemand warehousing in the next three to five years, and the solution enabling this evolution has been a combination of manufacturing where the production costs are optimal, just-in-time shipping, highly automated fulfillment centers, and, to a growing extent, mobile connectivity. [ABSTRACT FROM AUTHOR]

The rise of e-commerce helped fuel consumer appetite for quick home deliveries. One consequence has been the placing of some logistics facilities in proximity to denser consumer markets. The trend departs from prevailing discussion on "logistics sprawl," or the proliferation of warehousing into the urban periphery. This study spatially and statistically explores the facility- and region-level dimensions that characterize the centrality of e-commerce logistics platforms. Analyzing 910 operational Amazon logistics platforms in 89 U.S. metropolitan statistical areas (MSAs) between 2013 and 2021, this study estimates temporal changes in distances to relative, population centroids and population-weighted market densities. Results reveal that although some platforms serving last-mile deliveries locate closer to consumers than upstream distribution platforms to better fulfill time-demands, centrality varies due to facility operating characteristics, market size, and when the platform opened. • Do e-commerce logistics platforms open closer to urban consumers to meet heightened time-demands and do they decentralize in alignment with broader warehousing trends? • This study employs centrographic techniques to measure platform distances from consumer centers and densities between 2013 and 2021. • Platforms serving last-mile deliveries locate closer to consumers than upstream platforms, but facility size, costs, public incentives received, and metropolitan population, among other factors, determine centrality. • Platforms opened between 2020 and 2021, including last-mile platforms, located less centrally than years prior with implications for facility operations. [ABSTRACT FROM AUTHOR]

Under the influence of COVID-19, the efficiency of e-commerce distribution in public health emergencies has become the key to ensuring people's normal lives. With the development of e-commerce, the location of distribution center is becoming more and more important, which is related to the healthy and long-term development of e-commerce. However, the traditional location efficiency is low, which cannot play an immediate role in the development of e-commerce. This paper combines weiszfeld algorithm, improves particle swarm optimization algorithm, improves the operation efficiency and location accuracy, and finally gets a relatively satisfactory result. Results data analysis shows that the improvement and integration of the algorithm is reasonable and effective. This method has important reference value for e-commerce location. [ABSTRACT FROM AUTHOR]

An efficient storage strategy for retail e-commerce warehousing is important for minimizing the order retrieval time to improve the warehouse-output efficiency. In this paper, we consider a model and algorithm to solve the cargo location problem in a retail e-commerce warehouse. The problem is abstracted into storing cargo on three-dimensional shelves, and the mathematical model is built considering three objectives: efficiency, stability, and classification. An artificial swarm algorithm is designed to solve the proposed models. Computational experiments performed on a warehouse show that the proposed approach is effective at solving the cargo location assignment problem and is significant for the operation and organization of a retail e-commerce warehouse. [ABSTRACT FROM AUTHOR]

Due to the rapid growth of electronic commerce via the Internet, there is a need to streamline the supply chain in order to reduce product distribution costs and shorten delivery lead times. Among these, the role of distribution centers that connect physical and commercial distribution has become more important. Improvement in work efficiency in the distribution centers has also become an important theme. Aware of such problems, we focus on order picking operations where products are collected based on the customer order since it is one of the most labor-intensive activities in the distribution center. We consider methods to shorten the average traveling distance of order-picking operators from two perspectives: 1) Improving the floor layout of the distribution center, and 2) introducing better management rules to determine the storage position of products. In the first half of the paper, the mathematical model to evaluate the average traveling distance of the picker is proposed. The model proposed assumes that the width of the aisle and the size of the shelf are sufficiently small, and obtains the value of the traveling distance analytically. In addition, improvement measures are considered from the two perspectives stated above, and are evaluated by comparing with the case in conventional Traditional Layout with horizontal and vertical aisles. In the second half of the paper, two improvement measures combining the two perspectives are proposed based on the results of the first half of the paper. Both measures move the accumulation point of products to the center of the lower side of the floor. In addition, the first measure adopts the addition of a diagonal aisle on the floor and classbased management to determine the storage position of products. The second measure executes class-based storage in an area where the work floor is divided using diagonal straight lines, which means being equidistant from the accumulation point of products. As a result, this paper clarifies that the two improvement measures proposed in the second half of the paper shorten the average traveling distance by 25% or more compared to case in conventional Traditional Layout. [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]

With the rapid development of online shopping in recent years, logistics distribution has received much attention from enterprises and online consumers. Logistics distribution involves many factors and complex processes; conventional qualitative methods are unable to provide an effective analysis. Thus, this paper sets a framework to solve the above problem. A case study of an E-commerce enterprise in Shanghai on logistics distribution is proposed to discretize the whole process and minimize the total costs. Then the AnyLogic software is used to simulate and optimize the system from three aspects, including routes selection, warehouses quantity, and warehouses layout. Finally, this paper analyzes the simulation results, which would provide some valuable references for practical logistics. [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 problem of locating distribution centers for delivering fresh food as a part of electronic commerce is a strategic decision problem for enterprises. This paper establishes a model for locating distribution centers that considers the uncertainty of customer demands for fresh goods in terms of time-sensitiveness and freshness. Based on the methodology of robust optimization in dealing with uncertain problems, this paper optimizes the location model in discrete demand probabilistic scenarios. In this paper, an improved fruit fly optimization algorithm is proposed to solve the distribution center location problem. An example is given to show that the proposed model and algorithm are robust and can effectively handle the complications caused by uncertain demand. The model proposed in this paper proves valuable both theoretically and practically in the selection of locations of distribution centers. [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]

The vertical sorting system is an emerging system in warehouse section. It achieves higher level of automated sorting by applying conveyor sorter and AGVs. This paper investigates the optimization problem of parcel sorting in an e-commerce warehouse where parcels are waiting to be sorted and delivered. The assignment among the parcels, picking stations and AGVs are determined together with the objective of minimizing the finishing time of processing the last parcel, so that a detailed operational plan for warehouse sorting can be worked out. A mixed-integer linear programming model is developed based on the problem, and particle swarm optimization (PSO) algorithm is employed for problem solving. Numerical experiment shows that the proposed PSO algorithm can solve the model with high efficiency, and sensitivity analysis give suggestion about the arrangement of trailers' location. [ABSTRACT FROM AUTHOR]

• We study the E-commerce Shipping Problem where orders with deadlines must be shipped. • The retailer can enter the supply chain of a service provider at various levels. • The retailer must make consolidation decisions at each period to ship orders. • We model and solve the static and dynamic cases of this problem. • We develop and compare four shipping policies for the dynamic case. We consider an e-commerce retailer who must ship orders from a warehouse to a set of customers with delivery deadlines. As is often the case, the retailer uses a third-party service provider to ensure its distribution. The retailer can enter the supply chain of the service provider at various levels. Entering it at a higher level entails lower sorting costs for the retailer, but higher delivery costs, and longer delivery times. The customer orders arrive at various moments over a rolling planning horizon. This means that the retailer must also make consolidation decisions. We model and solve the static and dynamic cases of this problem. The static case is modeled as an integer linear program and solved by CPLEX. We develop and compare four shipping policies for the dynamic case. Extensive computational results based on real location data from California and Texas are reported. [ABSTRACT FROM AUTHOR]