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• Offering a novel fuzzy robust approach for closed-loop supply chain network design. • Examining the hybrid uncertainties and flexibility of constraints in the problem. • Considering economic, responsibility, and environmental subjects in modeling. • Using the interactive fuzzy programming approach to solve the multi-objective model. • Introducing a new application for stone paper closed-loop supply chain network design. Nowadays, the production of stone paper, in addition to its widespread utilization in various fields, does not require water consumption, cutting down trees, and stone paper products are easily recyclable and recoverable. Due to the importance of developing and using stone paper and paying attention to environmental matters, Closed-Loop Supply Chain Network Design (CLSCND) is very important for stone paper products. Moreover, due to epistemic, randomness uncertainties, the uncertainties in Objective Function (OF), and the flexible constraints for CLSCND in the real world, this paper introduces a novel Mixed Robust Stochastic, Possibilistic, and Flexible Programming (MRSPFP) approach based on credibility theory. The different attitudes of the Decision-Makers (DMs) are addressed by a more flexible measurement of the optimistic and pessimistic parameters using the criterion of credibility. In the study, a comprehensive procedure is proposed for stone paper CLSCND, to minimize costs, increase responsiveness by minimizing transit time between different facilities, and regarding environmental concerns into account through minimizing carbon emissions. The model is solved utilizing an interactive fuzzy programming solution procedure and the Best-Worst Method (BWM). The results of sensitivity analysis, the effect of changing the problem parameters, and the performance of the proposed model are investigated and compared. The results show that the MRSPFP model has a better performance compared to other previous models. The MRSPFP model performs better in strategic decisions that require high investment costs due to the minimization of the absolute deviation of OF from its mean. Also, the applied results of the study show that CLSCND has a good capability and potential for sustainable development in the field of stone paper. [ABSTRACT FROM AUTHOR]

The emergence of artificial intelligence (AI) and its related capabilities has led industries to rethink the existing practices of conventional supply chain management and data analysis. Machine learning (ML), Deep Learning (DL) and their unique ability to predict future data and classify data have led to important research in the supply chain (SC) domain, particularly in identifying and prioritizing supply chain risks. This paper proposes several DL methodologies to exploit the benefit of DL, particularly to identify whether any product will be delivered late due to any unforeseen reason in a complex SC system. Four different DL architectures (Simple-LSTM, Deep-LSTM, 1D-CNN, and TCN-1DSPCNN models) are proposed to extract features, while six variant classifiers: Softmax, random trees (RT), random forest (RF), K-nearest neighbor (KNN), artificial neural network (ANN), and support vector machine (SVM), were used to classify delay or non-delay information. By seamlessly capturing intricate temporal dependencies, these DL models enhance accuracy in robustly identifying supply chain late orders. Leveraging their hierarchical feature learning, these proposed DL models excel in recognizing subtle patterns and correlations, making them ideal for classifying late orders within the supply chain. Their parallel processing prowess facilitates real-time decision support, allowing organizations to address potential delays and allocate resources effectively and proactively. Five-fold cross-validation is presented to avoid over-fitting and to prove the efficiency of the proposed DL models. The total accuracies of the six ML classifiers are 74.03, 75.81, 93.35, 87.72, 93.59, and 95.10, respectively, while the maximum accuracies obtained from four proposed DL methodologies obtained an accuracy of 97.6, 98.63, 100, 100% respectively using the SVM classifier for predicting late orders based on five-fold cross-validation. • This paper investigates a few DL approaches to extract features of SC data. • Both RNN and CNN are applied in the same model. • An improved CNN model has been proposed for feature extraction. • An online dataset is employed to validate the proposed DL architectures. [ABSTRACT FROM AUTHOR]

Largely inspired by the practice that both the brand name supplier and the platform motivate to encroach on the online retailing market, this paper investigates the optimal encroaching decisions in an online retailing marketplace that is currently owned by multiple online retailers. Depending on whether and who will encroach on, this paper conducts three different encroaching scenarios including the benchmark without encroaching, the brand name supplier encroaching, and the platform encroaching. A Stackelberg game model is then developed to capture competitions among three parties and thus address equilibria in this paper. With analytical studies and numerical experiments, several key findings are derived. For example, but not limited to, the optimal commission set by the platform is contingent largely upon the encroaching scenarios and the number of online retailers. Interestingly, the optimal commission tends to be consistent when there are as many online retailers as possible, regardless of whether and who will encroach. This paper also analytically reveals that the encroaching cost, the product inherent (the fraction of product cost over its market potential), and the optimal commission, induce key impacts on the individuals' retailing encroaching decisions. A free rider effect incurred by the retailing encroaching is found and examined in this platform supply chain. The results derived in our paper shed lights on operational and encroaching decisions for the platform supply chain. • Develop a game theoretical model in an E-commerce platform supply chain with multiple online retailers. • Explore an endogenous commission set by the platform across different encroaching scenarios. • Investigate the optimal retailing encroaching decisions for two potential entrants. • Reveal impacts of retailing encroaching on individual profits and find a free ride effect incurred by the encroaching. [ABSTRACT FROM AUTHOR]

This paper constructs a supply chain model consisting of two manufacturers and one platform, where the platform provides sales service to enhance the demand. The platform has demand information and decides whether and with which manufacturer to share the information. In practice, if the platform shares the information, the manufacturer can use the shared information to guide pricing decisions and further use Consumer-to-Manufacturer (C2M) strategy to drive product production. C2M means that the manufacturer produces products based on the shared information, making products satisfy consumers' needs, thereby increasing the market demand. Therefore, this paper uses a theoretical model to answer a key question: considering the role of C2M, whether the platform prefers to share demand information with others, and if the platform shares, which strategy is most beneficial to each player? First, this paper obtains the optimal equilibrium solutions under three strategies, that is, no information sharing (strategy NN), information sharing with one manufacturer (strategy SN or strategy NS), and information sharing with two manufacturers (strategy SS). Second, this paper derives that the information effect is the highest for all players under strategy SS. Whether the platform shares information with one or two manufacturers, the market effect has positive influences on both players. The cost effect does not affect the platform but hurts the informed manufacturer. Third, the conditions of the platform sharing information are obtained. There is a sales service efficiency threshold, if the sales service is more efficient, the platform shares the information with one manufacturer; otherwise, strategy SS is optimal. Fourth, through comparative analysis, this paper points out that when the C2M costs are lower, both manufacturers prefer the platform to choose strategy SS. Interestingly, when the C2M costs are higher, both manufacturers want the competitor to have information, which is different from the conclusions of previous studies. In addition, strategy SS makes the platform, and two manufacturers achieve Pareto optimality under certain conditions. Finally, the results are verified by numerical experiments. [ABSTRACT FROM AUTHOR]

Under dual constraints of green production responsibility and recycling responsibility, the optimal contract between upstream and downstream firms is crucial to achieve low-carbon development, but information asymmetry poses a great challenge to the above contract design. Based on Stackelberg game theory, this paper develops a contract model in a supply chain with information asymmetry under dual environmental responsibility constraints, in which the producer invests in green technology while the retailer exerts green marketing efforts. The paper aims to explore how the producer designs optimal contracts under the different information structures to improve environmental performance while ensuring profitability. Results indicate that: (i) in the full information scenario, the two-part optimal contract makes the decentralized supply chain to achieve the same channel profit and carbon emissions as the centralized scenario. (ii) In the information asymmetry scenario, the producer should design differentiated contract configurations for different types of retailers to eliminate double marginalization. That is, the producer designs low wholesale price and high side payment for the high-type retailer, which enables the producer to earn high profit with low carbon emissions and makes the retailer to obtain additional information profit. While under some conditions, the producer considers the possibility of terminating cooperation with the retailer of low-type, as such a cutoff policy not only guarantees the profitability of the channel members but also improves environmental performance. Furthermore, the impact of some key factors on firms' decisions, profits, and equilibrium carbon emissions is summarized to guide management decisions. [ABSTRACT FROM AUTHOR]

• Supply chain collaboration affects considerably supply chain performances. • We propose a new collaboration model (IS-OUT) for multi-echelon supply chains. • The IS-OUT relies upon the ordering structure of the classical order-up-to policy. • Simulation is adopted to investigate the value of IS-OUT in supply chains. • IS-OUT is successful to improve the supply chain performances. Extensive research has shown that collaboration has substantial impact on supply chains performance. However, most of previous research has focused on information sharing-based collaboration models that require considerable effort to be implemented, such as information exchange supply chain (IESC). This paper introduces a new efficient collaboration model (IS-OUT) for multi-echelon supply chains. The IS-OUT model relies upon the ordering mechanism of the classical order-up-to policy (OUT). In traditional supply chains (TSC), although OUT replenishment orders include two pieces of information: demand forecast and inventory position balance, they are transferred to upstream echelons as single-quantity orders. In IS-OUT, the order information is transferred to the upstream echelons as two component parts of information to provide better coordination in supply chains. In this paper, the mathematical formulation of IS-OUT is presented, and simulation is adopted to compare the performance of TSC, IS-OUT, and IESC under special conditions and assumptions, considering various performance metrics. Although the results indicate that IESC model offers higher performance mostly than IS-OUT, IS-OUT outperforms or at least is comparable with IESC under a few conditions. Since IS-OUT model is easier to implement than IESC, IS-OUT provides a compromise between extent and information sharing requirements, and performance efficiency. The results should help a decision maker to select the model that optimizes its operating environment. [ABSTRACT FROM AUTHOR]

Due to increasing air pollution, which is a consequence of the environmental effects of production in various industries, green supply chain management (GSCM) has attracted the attention of both scholars and practitioners. Green supplier selection is an important problem in GSCM and seeks to satisfy a firm's environmental goals as well as its economic targets. In this paper, for the first time, a green supplier selection problem considering both green and non-green evaluation criteria in a closed-loop supply chain is studied, and a cap-and-trade mechanism as a way of controlling the air pollution caused by manufacturers is proposed. To solve this particular problem, we propose a multi-objective robust optimization (RO) model. This specific model is an effective approach to handle uncertainty. A numerical example using randomly generated data, accompanied by subsequent discussion of the proposed approach, is deployed to validate the model. The results prove that the developed model for green supplier selection is able to effectively enhance the decision-making process of the experts. By illustrating the trade-off in robustness between the model and proposed solutions, as well as the effect of the deviation penalty on the closeness of results to the achieved solution, we show how firms can make optimal decisions when assigning the parameters. Furthermore, analyses show that decreasing the allowance amount (cap) and increasing the allowance prices in the cap-and-trade system escalate the firms' costs, but lower the amount of carbon released. Finally, we show that the cap-and-trade mechanism results in a better solution in terms of the total utility of the supply chain compared to the penalty-based system for a specific range of carbon allowance. • This paper generalizes the green supplier selection and order allocation problem. • A mathematical model for the problem is developed. • A robust optimization approach to handle the model uncertainty is developed. • The impact of changes in model parameters on the objective functions is analyzed. • The cap-and-trade mechanism is compared with a penalty-based mechanism. [ABSTRACT FROM AUTHOR]

Effective supply chain management is crucial for economic growth, and sustainability is becoming a key consideration for large companies. COVID-19 has presented significant challenges to supply chains, making PCR testing a vital product during the pandemic. It detects the presence of the virus if you are infected at the time and detects fragments of the virus even after you are no longer infected. This paper proposes a multi-objective mathematical linear model to optimize a sustainable, resilient, and responsive supply chain for PCR diagnostic tests. The model aims to minimize costs, negative societal impact caused by shortages, and environmental impact, using a scenario-based approach with stochastic programming. The model is validated by investigating a real-life case study in one of Iran's high-risk supply chain areas. The proposed model is solved using the revised multi-choice goal programming method. Lastly, sensitivity analyses based on effective parameters are conducted to analyze the behavior of the developed Mixed-Integer Linear Programming. According to the results, not only is the model capable of balancing three objective functions, but it is also capable of providing resilient and responsive networks. To enhance the design of the supply chain network, this paper has considered various COVID-19 variants and their infectious rates, in contrast to prior studies that did not consider the variations in demand and societal impact exhibited by different virus variants. [ABSTRACT FROM AUTHOR]

With the burgeoning of the platform economy, the online-to-offline catering industry has developed rapidly. However, the industry faces various challenges, such as fierce channel competition and fake reviews. This paper investigates a Catering Platform Service Supply Chain (CPSSC) consisting of a restaurant (she) and an online service platform (he). The restaurant decides whether to join the platform or not. If yes, we further delve into the issue of the restaurant's potential fake reviews and devise a platform supervision mechanism as a viable solution. The research findings first show that if the restaurant's offline profit is below a threshold, she would be incentivized to join the platform when the commission rate is relatively low. Furthermore, this incentive would decrease when more offline consumers switch to the online channel. Secondly, the restaurant will set a higher online pricing ratio after joining the platform if its online review rating is above the industry average, and vice versa. Thirdly, if both firms focus on their short-term benefits while neglecting their corporate reputations, they would fall into the dilemma of boiling frogs in gradually heated water. To solve this dilemma, we establish an evolutionary game model to explore the long-term strategic interaction between the restaurant and platform while concurrently formulating a robust supervision mechanism. The results suggest that reinforcing consumers' trust in the platform's supervision and enhancing the supervision effectiveness can significantly benefit the entire CPSSC. [ABSTRACT FROM AUTHOR]

This paper studies a manufacturer's selling channel structure choice in a supply chain wherein the firms may concern about consumer surplus. In addition, the retailer can make the demand-enhancement investment, the effect of which may spill over to the manufacturer, subsequently altering the manufacturer's selling channel structure choice among the reselling channel (mode R), the direct-selling channel (mode M), and the dual channels (mode D). Several intriguing results are obtained. First, when the manufacturer concerns about consumer surplus, the manufacturer prefers mode R if both the retailer's investment cost and the manufacturer's concern for consumer surplus are either high or low, while choosing mode M if the investment cost is high and the concern for consumer surplus is relatively low. Conversely, the manufacturer may not choose mode M when the retailer concerns about consumer surplus. Besides, whether the manufacturer or the retailer concerns about consumer surplus, only mode R may arise as the jointly preferred channel choice for the firms. Second, mode R is more profitable for the supply chain when the manufacturer's (retailer's) concern for consumer surplus is sufficiently high, because the sales competition is weaker and the retailer's investment incentive is stronger in mode R. Third, mode M cannot be optimal for consumer surplus and social welfare due to the absence of the retailer's demand-enhancement investment. Finally, the manufacturer's (retailer's) increasing concern for consumer surplus does not necessarily improve consumer surplus and social welfare because of the effect of investment cost on the manufacturer's equilibrium channel structure strategy. • Manufacturer channel choice with dual-purpose firms and demand investment is studied. • Mode R may arise as the equilibrium for dual-purpose firms under certain conditions. • The manufacturer may abandon mode M when the retailer cares about consumer surplus. • The manufacturer's dual-purpose concern does not always favor consumer surplus. [ABSTRACT FROM AUTHOR]

Designing a system to improve the performance of the supply chain by linking pricing and advertising will bring significant benefits to the supply chain components. Due to the expansion of customers' activities in social networks and society's greater interest in obtaining information from this space, companies have changed their marketing methods. Organizations have turned to influencer marketing for advertising to take advantage of social networks' potential. Despite the recent growth of influencer marketing, the issue of optimal identification and appropriate spending based on the characteristics of influencers to convey a company message or advertisement has not been carefully studied in the academic literature, nor has it been addressed in practice. Therefore, in this paper, a hybrid framework of machine learning and optimization is developed to help organizations (i) conduct successful and optimal marketing campaigns by selecting the best influencers and (ii) examine the effectiveness of advertising on the pricing of products in the supply chain. The framework designed in this study is validated using data extracted from the Instagram platform. The results demonstrate that businesses should adopt novel approaches to get the maximum benefit from advertisements on social media, which depends on a more accurate selection of influential users. Contrary to previous works, our results demonstrate that choosing influencers based on their influence network will not necessarily bring the expected efficiency for businesses. [ABSTRACT FROM AUTHOR]

Highlights • An uncertain resource allocation-targets setting approach is proposed. • Uncertainties of resources and targets are modeled using type-II & type I fuzzy sets. • Fuzzy De-Novo programming for resource allocation and target setting is developed. • A real case of the natural gas supply chain is analyzed. Abstract Developing effective approaches to design optimal resources of system based on the concepts of benchmark in DEA and optimal design in De-Novo programming is one of the important managerial decision making problems. In this paper, a decision support system is developed for allocation of resources and setting the targets across a set of entities in an equitable manner in presence of uncertainty. The proposed approach has two main modules. First, the most suitable system is designed using De-Novo programming. De-Novo programming. De-Novo programming is used to optimally determine the inputs (i.e., resources) and outputs (i.e., targets) of DMUs in network DEA rather than optimizing existing DMUs. Then, the optimal values of resources are allocated and optimal values of the targets are set in a complex network structure. Furthermore, in real-world problems budget of resources and targets are usually mixed with uncertainties, so in this paper, two concept of fuzzy and interval type-II fuzzy resources and target are developed for resource allocation and target setting. Finally numerical example based on real case of natural gas supply chain is also used to evaluate the applicability and efficacy of the proposed models. Graphical abstract Image, graphical abstract [ABSTRACT FROM AUTHOR]

• Considering pistachio, a supply chain network is firstly designed in this paper. • A mixed linear mathematical model is formulated based on the proposed problem. • Efficient and well-known old and recent meta -heuristic algorithms are utilized. • Three hybrid meta -heuristic algorithms are developed. • The quality of the proposed algorithms is investigated and compared. Nowadays, recent advances and developments in the agricultural sector have raised concerns regarding the environmental impact of agricultural wastes and the safety of agricultural products. Recently, reverse logistics have been used to address these issues. It is also possible to reduce the environmental impact of these by-products and return them to the network by opening compost centers and designing an optimal supply chain distribution network. In this paper, a new mixed linear mathematical model is developed for an agriculture supply chain network to minimize the total fixed and variable costs of the closed-loop supply chain,. To address the proposed model, efficient and well-known old and recent meta -heuristic algorithms are utilized. Besides, to boost intensification and diversification phases, three hybrid meta -heuristic algorithms are developed. Finally, the quality of the meta -heuristic and hybrid algorithms are investigated and compared. [ABSTRACT FROM AUTHOR]

Adopting various pricing policies has been highly regarded in recent years for setting prices and increasing firms' profits. One of the most common steps in pricing is to identify costs. Since a significant part of costs is related to the corresponding supply chain, many researchers in different fields have used decision-making for simultaneous pricing and network design. However, there is no such approach in the field of healthcare. This paper tries to fill this gap by formulating a mixed-integer nonlinear bi-level programming model examining the interaction of hospitals and their medicines suppliers. At the upper level, there is a competitive market where a new firm (entrant) intends to enter the market and faces the challenge of pricing medicines and making network design decisions. At the lower level, there is a private hospital competing with a public hospital, and it also struggles with healthcare services pricing and supplier selection. A comprehensive utility function that considers healthcare services prices, quality, waiting time, health insurance, readmission rate, and referral rate is extended at this level. Three novel meta -heuristic algorithms are recommended, including bi-level, improved fruit fly, jellyfish optimization, and forensic-based investigation optimization algorithms to solve the presented complex mathematical problem. [ABSTRACT FROM AUTHOR]

Efficient generalisation in supply chain inventory management is challenging due to a potential mismatch between the model optimised and objective reality. It is hard to know how the real world is configured and, thus, hard to train an agent optimally for it. We address this problem by combining offline training and online adaptation. Agents were trained offline using data from all possible environmental configurations, termed contexts. During an online adaptation phase, agents search for the context maximising rewards. Agents adapted online rapidly and achieved performance close to knowing the context a-priori. In particular, they acted optimally without inferring the correct context, but by finding a suitable one for reward maximisation. By enabling agents to leverage off-line training and online adaptation, we improve their efficiency and effectiveness in unknown environments. The methodology has broader potential applications and contributes to making RL algorithms useful in practical scenarios. We have released the code for this paper under https://github.com/abatsis/supply_chain_few_shot_RL. • Agents are usually trained in simulations, but tested in the real world. • We use online adaptation to close the gap between simulation and the real world. • This problem is prevalent in real-world application of AI. • We test our methods in supply chain optimisation. [ABSTRACT FROM AUTHOR]

When should manufacturers and retailers change their prices? Should they change prices based on the phases of the product life cycle (PLC) that are affected by advertising or without its effect? To answer these questions, we develop dynamic models for analyzing market demand, pricing decisions, and advertising cost according to the properties of PLC phases. Our contributions extend beyond conventional analyses. For the first time, we introduce dynamic mathematical models that capture market demand, pricing decisions, and advertising costs, intricately aligning them with the unique attributes of each PLC phase. Furthermore, this study pioneers the consideration of the effect of advertising on demand but also examining its influence on the duration of PLC phases, shedding light on a previously unexplored facet of market behavior. Furthermore, our research presents a groundbreaking perspective on the relationship between advertising, pricing, and the PLC phases. Based on the PLC perspective, product prices, especially for electronic products, decrease over time. Therefore, manufacturers and retailers should adopt appropriate price-switching policies to remain competitive. This paper examines a dynamic supply chain consisting of one manufacturer and one retailer, that demand changes over time. The price switching policies including: 1) the first policy: at the beginning of the advertising-influenced PLC phases, and 2) the second policy: at the beginning of without advertising-influenced PLC phases, are investigated. The results indicate that the wholesale and retail prices under the second policy are always lower than the first policy. In the second policy, the retailer's profit is higher, if the price reduction rate is not too low, and the other hand the manufacturer's profit is higher than other policy. [ABSTRACT FROM AUTHOR]

This paper studies how technology suppliers can optimally design licensing contracts when there is incomplete information on the licensee's demand potential for the product in a high-technology supply chain. A licensing contract typically contains an up-front payment and royalties on product sales. The licensor decides to license one technology to a licensee, which designs products based on the supplier's technology and outsources the product manufacturing to a competitive downstream manufacturer. We apply principal-agent models to formulate the licensor's contracting problem while considering the transparency of the licensing royalty and find that under demand information asymmetry, the optimal contract structure changes with the demand potential. More specifically, both the separating and pooling strategies can be the optimal strategies for the licensor under the observable contract case. Conversely, under the unobservable case, only the separating strategy is the optimal licensing strategy. And the licensor can always charge zero royalty to a high-type design firm regardless of the menu strategy or the single contract. Furthermore, we examine the effect of licensing royalty transparency on licensing decisions and firms' profits and highlight the effect of downstream competition on the licensor's profit. Our results show that the licensor can be better off in the observable contract case. By contrast, the design firm may be better off in the unobservable contract case. We provide a rationale for the licensing contract design under demand information asymmetry and further shed light on the choice between public and secret licensing terms. [ABSTRACT FROM AUTHOR]

Setting the optimal vehicle routes and the optimal level of inventory to avoid shortage, in addition to reducing costs of transportation, are two of the main objectives in developing the distribution systems of perishable goods. Despite its importance, an area that still requires more investigation is the simultaneous consideration of hub location selection and routing and warehousing operations. In other words, addressing decisions related to the locations of the warehouses, vehicle routing, and warehouse management at the same time could improve the outcome. Therefore, this paper presents a new non-linear mixed-integer model for warehouse location selection, in addition to an integrated multi-objective mixed-integer model for routing and warehousing operations. Moreover, features of real operations are considered, such as dynamic conditions, order picking, and delivery, customer prioritization, time windows for customers, and different perishable goods. Minimizing the operations' costs and time under uncertain conditions is achieved by proposing a possibilistic-robust optimization algorithm. The optimization approach regards the weight of each objective using the opinions of experts by using a novel hesitant fuzzy approach. Finally, a case study is presented, and the results are compared with the actual data along with validating the model by using several numerical examples. The results have shown that the method can perfectly form the supply chain of perishable goods. Furthermore, the optimization method can provide the experts with more flexibility in finding a compromise solution. [ABSTRACT FROM AUTHOR]

Decarbonizing innovation plays a significant role in curbing carbon emissions, allowing a firm to meet governmental environmental regulations and gain a competitive edge. However, innovation uncertainty and technology spillovers deter a firm from doing so. This paper studies the issue of firms' decarbonizing innovation investment in a competing market under carbon emission regulation. We construct game-theoretic models of two competing supply chains, each consisting of a manufacturer and a retailer. Either manufacturer has the opportunity to invest in decarbonizing innovation, but she may fail in the end. According to the manufacturers' innovation investment decisions, several subcases with no investment, unilateral decarbonizing investment, and bilateral decarbonizing investment are explored. The results show that when both manufacturers have a strong absorbing capacity, neither invests in decarbonizing innovation; when they are weak in absorbing external technology, bilateral decarbonizing investment exists. The findings also reveal that unilateral decarboning investment dominates when the probability of innovation success is moderate. There is a case in which both manufacturers do not conduct decarbonizing investment, although the probability of innovation success is high. In addition, we also find that a retailer can make more profit if his upstream manufacturer invests in decarbonizing innovation than in the no-investment scenario. However, he is harmed by bilateral decarbonizing investment if his upstream manufacturer is weak in absorbing external knowledge relative to the case of unilateral decarbonizing investment. Our results elucidated how technology spillovers and the stochastic nature of innovation affect a manufacturer's decarbonizing innovation investment strategy in a competitive environment. [ABSTRACT FROM AUTHOR]

The purpose of our study is to discuss the impact of consumers' market distribution and consumers' fairness concerns on the decisions of supply chain participants. Based on utility theory, we innovatively incorporate consumers' fairness concerns as a significant factor into the game model and focus on the impact of the utility gap between products on consumers' purchasing willingness. In this paper, we establish a two-echelon supply chain consisting of one traditional manufacturer, one low-carbon manufacturer, and one retailer, and comparatively analyze the equilibrium results under the initial state and consumers' fairness concerns. The results show that the presence of consumers' fairness concerns results in the higher wholesale price, higher retail price, and larger profit of low-carbon products yet leads to a decline in traditional products' pricing and profit, and the impacts on the low-carbon manufacturer are more significant than on the traditional manufacturer. A reduction appears in the retailer's profit since consumers' fairness concerns decrease the retailer's ability to differential pricing. Additionally, the impacts of consumers' fairness concerns on the game equilibrium are also affected by consumers' market distribution and low-carbon manufacturers' technical efficiency, and the impacts are more pronounced in an environment where low-carbon manufacturers' R&D investments are more efficient. Furthermore, in a market dominated by low-carbon preferred consumers, low-carbon manufacturers are always more profitable than traditional manufacturers, while in a market dominated by traditionally preferred consumers, low-carbon manufacturers are only able to achieve higher profits than traditional manufacturers when R&D is quite efficient and consumers' fairness concerns are relatively strong. [ABSTRACT FROM AUTHOR]

The present paper analyzes a dual channel two-echelon supply chain model consisting of one manufacturer and one retailer for a single product. The retailer uses traditional retail channel whereas the manufacturer uses e-tail channel as well as retail channel to boost the sales of the product. Customer feedback is an important factor in present global business environment because from the customer feedback, the manufacturer can reveal the level of satisfaction of the customers and this can help the to improve the product quality. Good feedback from the customers also attracts other new customers to buy the products. A company can never know whether customers are getting value out of their product without customer feedback. The effects of getting customer feedback from the customers and the promotional effort by advertising or by sales team's initiatives are taken into account in the present model. The model is solved analytically to determine the decision variables which maximize the total profit in case of decentralized as well as centralized systems and the results have been illustrated by numerical examples. The study of this paper reveals that the profit margins of the individual channel members increase considerably if the feedback of the customers on the product is considered. • The effect of feedback received from the customers has been investigated in the model. • The effect of feedback received from the customers increases the profit. • Promotional effort by sales team's initiatives are considered in the model. • Stochastic demand has been considered in the model for it is more realistic situation. • A positive feedback reviews increase the reliability of the product. [ABSTRACT FROM AUTHOR]

• New Fermatean fuzzy cross entropy-based optimization weighting procedure is developed. • An integrated Fermatean fuzzy weighting procedure is developed for decision expert's weight. • A double normalized MARCOS approach with Fermatean fuzzy information is proposed. • A case study of warehouse location for the automotive manufacturing company is discussed. • The comparative study and sensitivity investigation are made to confirm the robustness of the obtained results. The automotive industry is one of the most competitive sectors, and it requires a well-structured logistics system to meet the industry' vital requirements such as just-in-time, lean and agile supply chain operations, productivity and sustainability. Well-located and well-designed warehouses can make reaching these aims for the automotive industry possible and more accessible. Hence, determining a location for a warehouse is a highly critical, tactical, and managerial resolution for the automotive industry, as there is a strong correlation between well-located warehouses and the well-structured logistics network in the automotive industry. Although the WSS is a significant decision-making problem, we observed four critical and severe gaps in the existing literature: (1) the authors preferred to apply traditional objective & subjective frames, and they overlooked existing highly complicated uncertainties. (2) The number of studies focusing on the WSS problem in the automotive industry is surprisingly scarce. (3) It is not sufficiently clear how these factors used in the previous studies were determined, which causes doubts about their reliability. (4) there is no satisfactory evidence of which approaches were used to identify the factors in the previous papers. By considering these gaps, we propose two approaches which can be accepted as a novelty of the paper. First is the extension of the Delphi techniques based on the Fermetean fuzzy sets (FFs) used for identifying the criteria. It also combines the two traditional approaches (i.e., literature review and professionals' evaluations to identify the criteria) with the FF-Delphi technique. The second is the Double Normalized MARCOS approach based on FFs (FF- DN MARCOS) implemented to identify the weights of the criteria and ranking performance of the alternatives. The proposed model was implemented to identify the best warehouse location for the automotive manufacturing company. The results show that the C1 "energy availability & cost" criterion is the most influential criterion and the C5 proximity to port and customs criterion is the second most crucial factor. Then we executed a comprehensive sensitivity analysis, and the results approved the suggested model's validity and robustness despite excessive modifications in the criteria weights. [ABSTRACT FROM AUTHOR]

• This paper determines the best price & order quantity for three-level supply chain. • Centralized, semi-decentralized, & decentralized models are investigated. • Salop spatial model to demonstrate the pricing competition between entities. • Impacts of price, quantity, holding cost, & production rates are investigated. A key objective of this paper is to investigate the supply chain coordination frameworks to minimize the double marginalization effect in the case of price-sensitive demand. This paper investigates the impact of pricing strategy and ordering/production policy on the performance of a three-layer supply chain by using the Salop spatial model. The Nash Game approach determines the best price and order quantity to locally maximize the expected profits of the individual members in the decentralized supply chain. The total profit of the centralized structure is used as the benchmark to validate the channel member's decision on the selling price and order quantity. A numerical analysis has been conducted to confirm the theoretical results. [ABSTRACT FROM AUTHOR]

Sustainable Supply Chain Network Design (SCND) poses a complex challenge, as it often requires trade-offs in decision-making. The problem becomes even more challenging when there is a need to integrate risk management into the design process. It is steered by this challenge that the purpose of the paper is twofold. First, it proposes a Simulation-Based Decision Support System (SBDSS) for a real case study of a Portuguese wine SCND problem. Second, it analyzes different wine Supply Chain (SC) configurations under risk situations and sustainability objectives. The main contributions of this research are as follows. First, it concerns the method that was employed, which, contrarily to traditional approaches in literature (e.g., stochastic or genetic programming), consists of a simulation approach, in which agents and their interactions in an environment with uncertainty are modeled. Second, for the generation of non-dominated solutions, a simheuristic was employed, which combines multi-objective optimization with simulation. Finally, the type of industry that was considered, due to the lack of cases focusing on it in literature. The results suggest that the proposed approach determines a set of Pareto-front solutions that consider the sustainability dimensions of SCND, as well as risk situations. Furthermore, the implications that several parameters have on the sustainability of the designed SCs were assessed, as well as the impacts that the considered risks have on the performance of SCs. In particular, it was found that transportation risks impacted considerably more the performance of the SC than the risks on the prices of energy and raw materials. [ABSTRACT FROM AUTHOR]

Dual-channel supply chain (DCSC) is one type of patterns that combines offline channel and online channel in a common market. It is known that demand plays an important role in DCSC. In the literature, however, the demand is assumed to be deterministic or stochastic with completely known probability distribution. In contrast, this paper addresses the uncertain demand from a new perspective by considering the distribution uncertainty, and the uncertainty source comes from subjectivity. When only partial demand distribution information is available, this paper introduces a new uncertainty distribution set to characterize the ambiguous demand distribution. Based on the proposed uncertainty distribution set, a novel distributionally robust bilevel optimization modeling framework is developed for our capital-constrained DCSC. In order to address the upstream manufacturer's capital constraint, three financing strategies, i.e., bank financing, trade credit and hybrid financing (a combination of bank and equity financing) are considered. An analytically tractable method is developed to obtain the corresponding robust equilibrium solutions under the three financing strategies. Numerical analysis are conducted to demonstrate how the demand ambiguity and the equity ratio affect the manufacturer's equilibrium financing strategy. The numerical results show that the change of the uncertainty perturbation parameters can change the manufacturer's financing strategy. When the values of uncertainty perturbation parameters are relatively small, the equilibrium financing strategy is either trade credit or hybrid financing. When the values of uncertainty perturbation parameters are medium or large, bank financing is the equilibrium financing strategy. Also, the manufacturer's equilibrium financing strategy is affected by the equity financing ratio. When the equity financing ratio is small, the equilibrium financing strategy is either trade credit or hybrid financing. When the equity financing ratio is medium or large, the equilibrium financing strategy is always hybrid financing. The demand uncertainty can affect the manufacturer's financing strategy. As a result, the capital-constrained manufacturer should take into account the demand uncertainty to make her informed financing decisions. • The possibility distribution information of uncertain demand is partially available. • A uncertainty distribution set is presented to characterize ambiguous distribution. • A robust bilevel optimization model is developed for a capital constrained DCSC. • Numerical analysis is conducted to validate the proposed optimization method. [ABSTRACT FROM AUTHOR]

• The paper suggests a methodological frame for solving the container seaport selection problems. • The suggested MCDM model consisting of the LBWA method to calculate the weights of the criteria. • The fuzzy CoCoSo'B technique was applied to determine the preference of the decision options. • The validity of the presented multi-criteria framework is confirmed through a case study. Reducing transportation costs is one of today's global supply chains' essential tasks. For this purpose, as the crucial players of the worldwide supply chains, the international container shipping companies try to find optimal ways to reduce the shipping costs. Unnecessary transhipment and displacement operations are the leading causes of high logistics and transport costs. Selecting the most appropriate container seaport is one of the most effective ways to minimize these costs. The proper container port choice can provide an optimum container shipping system consisting of productive, efficient, and less costly container lines. Besides, a methodological framework is needed to address these kinds of highly complex decision-making issues. To serve this purpose, this paper proposes a fuzzy integrated MCDM approach consisting of the Fuzzy LBWA and fuzzy CoCoSo'B techniques. The consistency and stability of the model applied to evaluate the European container ports have been approved by a comprehensive sensitivity analysis. When the obtained results are evaluated, While C5 "Port Costs" has been determined as the most influential criterion, A2 "Port of Antwerp" (2.034) is the best and A10 "Port of Barcelona" (1.266) is the worst alternative. The obtained results were validated by comparing the results of the implemented popular MCDM frameworks with the help of Spearman Correlation Coefficient (SCC) [(SCC (MABAC) = 1.00; SCC (MAIRCA) = 1.00 and SCC (CoCoSo) = 0.915]. All SCC values have over 0.80 that can be accepted high correlation coefficient. The analysis results prove that the proposed fuzzy technique can be implemented to solve the highly complex decision-making problems faced in the maritime industry. [ABSTRACT FROM AUTHOR]

• An integrated model for sustainable supplier selection. • Considers all three sustainability perspectives simultaneously. • Obtains criteria weights by both subjective and objective methods. • Two types of weights are combined by Deviation Maximization method reasonably. • An improved fuzzy MULTIMOORA method to rank sustainable suppliers. With the increasing awareness of environmental protection and social responsibility, sustainable supplier selection (SSS) has been receiving more and more attention. However, traditional methods for selecting sustainable suppliers suffer from three main drawbacks: (i) Few existing studies have considered criteria weightings that have been determined both subjectively and objectively, reasonably and simultaneously. Moreover, among these limited studies, existing methods for combining weightings that have been calculated both subjectively and objectively provide only a poor degree of differentiation. (ii) Although MULTIMOORA method has high potential to solve the complex SSS problem, few researchers have given it much attention. (iii) The current Reference Point Approach within MULTIMOORA is not comprehensive as it only considers the distance between the alternative and the positive ideal point. Together, these drawbacks result in both inefficiency and ineffectiveness in SSS. This paper proposes a novel fuzzy MULTIMOORA-based method for SSS. The proposed method, firstly, obtains weights by both group Best Worst Method (subjective method) and fuzzy Shannon Entropy Method (objective method), respectively. Then the two types of weights are combined by Deviation Maximization method, reasonably and effectively. Finally, the paper introduces an improved fuzzy MULTIMOORA method to rank alternative suppliers, which considers both the minimum distance from the positive ideal point and the maximum distance from the negative ideal point. The practicability and effectiveness of the proposed method is verified in an illustrative application in Company L, a well-known international forklift truck manufacturer in China. [ABSTRACT FROM AUTHOR]

• A multi-state network (MSN) model is constructed for expert systems. • The MSN is evaluated with both maximal and minimal capacity vectors. • Reliability evaluation is formulated by the sum of disjoint products. • A procedure is built with a recursive function and simplified processes. • Experiments show the procedure efficiency by a real project management case. This paper applies a multi-state network (MSN) model with multi-state capacity to evaluate network reliability with both maximal and minimal capacity vectors for real-life applications. To be specific, the proposed MSN model can be embedded in the practically related expert systems, especially in project networks, manufacturing, and supply chain. In the early works, the estimated network reliability with maximal and minimal capacity vectors and the exact value for the MSNs were measured based on an interval approach and inclusion and exclusion (IE) principle, respectively. Owing to the computational inefficiency, the early works are inappropriate to be integrated into practical expert systems. In this paper, a more efficient procedure based on the sum of disjoint products (SDP) principle calculates the exact network reliability. Additionally, a recursive function with simplified processes is developed to achieve the efficiency of reliability evaluation. By using a practical project network case with several numerical experiments, efficiency investigation reveals that the SDP-based procedure outperforms the IE-based approach in terms of efficiency under all the experiments. The proposed SDP-based procedure can achieve over 30 times (and reach up to 5,000 times) faster than the IE-based approach. That is, based on the proposed procedure, the reliability evaluation of practical applications such as project management can be implemented. [ABSTRACT FROM AUTHOR]

This paper formulates supplier evaluation and selection as a multi-criteria decision-making (MCDM) problem with subjective and fuzzy preferences of decision makers over evaluation criteria. As an outcome, this paper provides decision makers with a decision support system that presents the Pareto fronts, a set of best possible high-quality suppliers and optimized business operation levels from such suppliers. In addition, this paper quantifies the importance of the agility criterion and its sub-criteria in the process of evaluating and selecting agile suppliers by measuring the magnitude of bullwhip effect and inventory costs. The proposed system uses a fuzzy analytic hierarchy process (fuzzy AHP) and fuzzy technique for order of preference by similarity to ideal solution (fuzzy TOPSIS) to successfully determine the priority weights of multiple criteria and selects the fittest suppliers by taking the vagueness and imprecision of human assessments into consideration. More importantly, it presents approximated Pareto fronts of the resulting supplier chains for varying priority weights of the agility criterion and its sub-criteria. Finally, we compare business costs of agile and non-agile supply chains before and after reconfigurations of original supply chains in response to unexpected disruptions under two order allocation strategies, a skewed order allocation (SOA) strategy and an even order allocation (EOA) strategy. [ABSTRACT FROM AUTHOR]

Carbon emission reduction has become increasingly important for limiting global warming throughout the world. Cap-and-trade regulation as an effective market-based mechanism is adopted to motivate firms to invest in decarbonization technologies toward sustainable supply chains. In practice, sustainable supply chains operate in the long term and decarbonization investment decisions could evolve as time passes. Distinguished from previous research, this paper formulates an evolutionary game model to study the investment decisions of suppliers and manufacturers under cap-and-trade regulation. We first analyze the strategic stability of suppliers and manufacturers and subsequently discuss the evolutionary stable strategies (ESSs) of the game with related conditions. The free-rider problem might occur in the investment game, and discourages one who would have invested from making efforts into carbon emission reduction. As a result, we explore the impact of key parameters on the ESSs for reducing free-riding behaviors. Numerical studies are conducted to simulate the evolution and stability of the game. The results show that revenue-related coefficients have a great impact on free-riding behaviors and cap-and-trade-related parameters affect their willingness to invest in carbon emission reduction. Based on findings and observations, some useful managerial implications are provided for regulators to make policies. [ABSTRACT FROM AUTHOR]

This paper presents a novel tri-objective robust Mixed-Integer Linear Programming (MILP) to a two-echelon supply chain configuration. It simultaneously considers the Vendor-Managed Inventory (VMI) policy and the Supply Chain Visibility (SCV), under uncertain demand. The objectives are to concurrently maximize the total visibility, minimize the number of deficient products and minimize the total cost. The LP-metric is then applied to deal with the tri-objectiveness of the model. The results show that the proposed approach successfully establishes robustness by deteriorating the optimal values of the objective functions as the cost of robustness. A sensitivity analysis reveals that the level of uncertainty has the greatest impact on the network's cost and deficiency of objective functions. However, the greatest effect on the SCV function is the change in the lower visibility threshold parameter. Moreover, the value of the deficiency rate parameter has the least impact on all three objective functions and has no impact on the visibility of the supply chain. [ABSTRACT FROM AUTHOR]

Highlights • The proposed algorithm gives better solutions than the direct solution approach. • The operational cost decreases with the increase of the production quantity. • We quantify the tradeoff between the duration and the total cost of clinical trials. • The tradeoff can be used to determine the optimal production quantity. Abstract Under increasingly challenging circumstances, pharmaceutical companies try to reduce the overproduction of clinical drugs, which is commonly seen in the pharmaceutical industry. When the overproduction is simply reduced without an efficient coordination of the inventories in the supply chain, the stock-outs at clinical sites and clinical trial delay can hardly be avoided. In this study, we propose a multi-objective model to optimize the production quantity, where the clinical trial duration and the total production and operational costs are minimized. The problem is formulated as a multi-stage stochastic programming model to capture the dynamic inventory allocation process in the supply chains. Since this problem's solving time and required memory can increase significantly with the increase of the stage and scenario numbers, the progressive hedging algorithm is applied as the solution approach in this paper. In the numerical experiments, we study this algorithm's performance and compare the solving efficiency with the direct solution approach. In addition, we identify the optimal production quantity of clinical drugs and give a discussion about the tradeoffs between the clinical trial delay and total cost. [ABSTRACT FROM AUTHOR]

Abstract: This paper is to resolve the VRPTW and the inventory control decision problem simultaneously since both the vehicle routing decision with time windows and the inventory control decision affect each other and must be considered together. A mathematical model of inventory routing problem with time windows (IRPTW) is proposed. Since finding the optimal solution(s) for IRPTW is a NP-hard problem, this paper proposes a two-phase heuristic method. The first phase is to find the initial solution. The second phase is to improve the solution adopting the variable neighborhood tabu search (VNTS) selecting better neighborhood solutions, to obtain the optimal solution. Moreover, the proposed method was compared with three other heuristic methods. The experimental results indicate that the proposed method is better than the three other methods in terms of average supply chain cost (transportation cost, time window violation penalty cost and inventory cost). [Copyright &y& Elsevier]

Abstract: This paper is aimed to present a fuzzy decision making approach to deal with the performance measurement in supply chain systems. In the manufacturing environment, performance measurement is based on different quantitative and qualitative factors. Some of these factors may have a larger effect on the performance measure than others. Units of measure of the quantitative factors are different such as time, money, percentage, ratio, and counts. Thus, this paper presents a performance measurement approach based on fuzzy set theory and the pair-wise comparison of Analytical Hierarchy Process (AHP), which ensures the consistency of the designer’s assignments of importance of one factor over another to find the weight of each of the manufacturing activity in the departmental organization. In the proposed model, various input factors have been selected, and treated as a linear membership function of fuzzy type. It is tested on a numerical example. The approach provides an effective decision tool for the performance measurement of a supply chain in manufacturing environment. [Copyright &y& Elsevier]

Abstract: Production quality in the food production supply chain is studied in this paper. The deficiencies of quality monitoring that exist in traceability systems are analyzed. An abnormality diagnosis algorithm, pre-warning method and structure of pre-warning system are presented. Four abnormal data types in supply chain are analyzed, they are substandard abnormality, over-range abnormality, abnormal distribution and abnormal tendency. All the detection data of the whole supply chain are monitored timely and pre-warned. The production abnormality of the logistics unit is diagnosed and automatically warned, and the decision support information is given. A standard hierarchy evaluation indicator system for abnormalities is developed in this paper. A mathematical model for abnormality detection is developed by combining radial base function (RBF) neural network, fuzzy control, and statistical analysis methods. This model is used in detecting and recognizing different types of abnormalities in the food production supply chain, especially hidden problems. The simulation results show that the proposed pre-warning system can effectively identify abnormal data types, and accurately determine whether a warning should be issued, depending on the warning level when an abnormality is detected by the system. The pre-warning system for food production supply chain performs well and effectively. [ABSTRACT FROM AUTHOR]

Abstract: Global enterprises require extended logistics operations that integrate channel intermediaries into the network. The method for deriving integrated models for logistics hubs that improve the efficiency of manufacturers’ global operations is the focus of this paper. Under government sponsorship, six industrial logistics hubs were implemented by leading manufacturing sectors across Taiwan over a five year period from 2004 until 2008. Each industry sector had unique industry characteristics, business strategies, and logistics models. Therefore, this paper describes how generalized and quick to implement integrated logistics hubs are developed by studying the successful reference models and systems used by six independent industrial sectors. The research results provide a field tested method for deriving integrated logistics hub models in different manufacturing economies with notes that provide sufficient methodological detail for repeating the construction of logistics hubs in other manufacturing economies. [ABSTRACT FROM AUTHOR]

In this paper, a fuzzy, closed-loop supply chain (CLSC), with one manufacturer, one retailer and one collector, is investigated. The optimal decision problem is analyzed based on a fuzzy price and sales effort-dependent demand to evaluate how members decide wholesale price, collection rate, retail price, and sales effort under different decision-making structures. Six game theory models are established and optimal solutions are extracted and compared by applying game and fuzzy theories. According to the results, collaboration between a manufacturer and retailer is favorable for both customers and the chain as a whole; the most effective model for collecting used products is collaboration between the manufacturer and collector. Finally, a practical, new channel coordination scheme is explored to match the performance of a decentralized CLSC to that of a centralized CLSC. The results illustrate that the proposed contract can effectively coordinate decentralized CLSCs. It is also beneficial from both consumer and environmental perspectives. [ABSTRACT FROM AUTHOR]

Abstract: In practice, in purchasing decision-making, many quantitative and qualitative factors, with vagueness and imprecision, have to be considered. This makes the decision process very complicated and unstructured. Besides the fuzzy sets theory, vague sets theory is one of the methods used to deal with uncertain information. Since vague sets can provide more information than fuzzy sets, it is considered superior in mathematical analysis of uncertain information. In this paper, a new approach based on vague sets group decision is proposed to deal with the supplier selection problem in supply chain systems. The work procedure is shown briefly, as follows: First, linguistic values are used to assess the ratings and weights for quantitative or qualitative factors. Second, degree of similarity and probability of vague sets are used to determine the ranking order of all alternatives. Finally, a numerical example of the selection problem of suppliers is shown, to highlight the procedure of the proposed approach, at the end of this paper. [Copyright &y& Elsevier]

Abstract: As competition abounds, the efficient solution on inventory control of a DRP’s (Distribution Requirement Planning) supply chain management is a vital success factor for companies in today’s business world. A stochastic program of market distribution and its deterministic equivalent control program is approximated by a multi-echelon lot-sizing model based on “risk inflated effective demands”. The DRP-decomposition of this approximate model, which can be used with allocation application of Fuzzy Set Theory, is then introduced. The aim of this paper is to find methods to address traditional DRP’s weaknesses and to improve the performances of DRP systems. In this paper, the field of continuous review model will be focused in, and a new method on the model with triangular fuzzy numbers (input data) will be presented. By using the method, the maximum of order quantity under a minimum of total cost can be obtained. In many previous research, authors take a precise number approximately as the representative of a fuzzy number. But the precise number can not reflect the property of fuzzy inventory control number fully. Therefore, in a numerical example of this paper, in addition to providing a transformation for reducing a fuzzy number into a closed interval by introducing the interval mean value concept proposed by Dubios and Prude, this fuzzy system can be transformed into a more precise diagnosis system for channel members in the supply chain distribution organization. [Copyright &y& Elsevier]

Nowadays, intensified globalization and consequent competitive pressures have reemphasized the importance of third party logistics (3PL) in managing logistics processes as well as customer and supplier relationships within the supply chain management (SCM). However, 3PL providers and their upstream and downstream parties within the whole chain have usually interacted as disconnected entities in the logistics performance management process. Despite the increased interest in using and improving logistics information systems, the number of researchers investigating the design and implementation of this process in close detail is still very small. This paper proposes an intelligent performance measurement system (K-LPMS) for measuring the performance of 3PL providers and their upstream and downstream supply chain partners. With the help of K-LPMS, 3PL providers are capable to fulfil different customer’s supply chain service requirement through accessing the capability of each SC partners and reconfigure SC network. This paper describes the structure of K-LPMS and its features. The results of testing and applying the tool in companies are presented. This paper concludes by discussing the implications of this research for managers, and identifying directions for future research. [Copyright &y& Elsevier]

Today's business environment has created a high level of uncertainty and disturbed procedures in supply chains. Suppliers have been often identified as the main source of risks in creating the massive levels of disruptions in supply chains. That is why resilient supplier selection can greatly reduce purchase costs and time delays and can create stability in business practices, thereby increasing competitiveness and customer satisfaction. Pharmaceutical companies play an important key role in the health of society, and these companies are frequently exposed to this disorder. Hence, this paper tries to propose a new integrated approach based on traditional (delivery, quality, price, technology level) and resilient criteria for supplier selection in pharmaceutical companies using the Z-number data envelopment analysis (Z-DEA) model and artificial neural network (ANN). In the proposed approach, expert opinions have been provided based on Z-numbers due to the inherent ambiguity and uncertainty in the evaluation process. This is the first study that evaluates the pharmaceutical industry based on traditional and resilience factors by presenting a methodological structure under the uncertainty environment. Here, a fuzzy mathematical model is used. A real case study is utilized to indicate the applicability of the proposed approach to resilient supplier selection in the pharmaceutical industry. Finally, the suppliers are ranked and the best supplier is selected regarding the reliable level of α. To indicate the features and capabilities of the selected approach, the performance analysis is presented in three parts. First, the obtained results are compared with a fuzzy DEA (FDEA) method in the form of validation and verification. Second, a sensitivity analysis is executed to show the effects of different criteria on ranking results, and the price index is identified as the most important evaluation criteria. Third, a predictive model is presented based on ANN that is able to detect the efficiency or inefficiency of suppliers with an 83% accuracy. [ABSTRACT FROM AUTHOR]

• Proposing the defuzzification relations of equality constraints with Nec measure. • Offering a new fuzzy network slack based model on the basis of necessity measure. • The proposed relations combine the Nec equality constraints and network SBM. • Using agile supply chain theory to validate the proposed approach. With respect to the necessity (Nec) measure, suggesting by Dubois and Prade, there is a dearth of any procedure or formula to deal with equality chance constraints. Therefore, this study attempted to introduce (Nec) and possibility (Pos) equality constraints in a real-case network non-oriented slack based model problem via agile supply chain (ASC) theory. The efficiency of different levels of agility of a dairy supply chain (SC), using a new fuzzy network Date Envelopment Analysis slack based model (FNDEA-SBM) on the basis of (Pos)/(Nec) measures, has been measured in this paper. The measurement took place in a multi-stage process. Since the agility features were rather qualitative in nature, the measures were likely to be delineated subjectively using linguistic expressions and fuzzy data. The application of (Pos)/(Nec) measures and different confidence levels made decision makers capable of getting information of all efficiency and sub efficiency scores through optimistic-pessimistic lenses and taking appropriate decisions on the basis of different optimistic-pessimistic points of view and confidence levels. The validity of the suggested approach was demonstrated through a real-life case study related to Iranian top twenty dairy firms. The results indicated the success of the suggested approach and computations were straightforward. [ABSTRACT FROM AUTHOR]

Profit allocation is an important research topic in supply chain enterprises, where the rationality of allocation determines the stability of their cooperative relationships. This paper focuses on the profit allocation of the supply chain dominated by upstream enterprises. To achieve this goal, a general four-echelon supply chain is constructed. By analyzing the drawbacks of the existing profit allocation methods, we propose three new Shapley values to deal with the different risk attitudes of players. Through theoretical discussions, we can see that the proposed new Shapley values endow upstream participants with profit priority. It shows that the new profit distribution mechanisms suit supply chains dominated by upstream enterprises. We also provide an application example to check the concrete distribution results of the proposed new Shapley values and to compare the experimental results of the proposed new Shapley values with the existing Shapley values to deal with Profit allocation on a four-echelon supply chain. As a result, the proposed new Shapley values outperform the previous Shapley values to deal with the profit allocation on a four-echelon supply chain from the perspective of cooperative games on augmenting systems. [ABSTRACT FROM AUTHOR]

This paper proposes a Vendor Managed Inventory model for a three-layer supply chain comprised of multiple suppliers, manufacturers, and retailers. During the production and storage of products, rates of defective and deteriorated products are generated, respectively. The imperfect quality and deteriorating items are removed from the lot size. The decision variables include the replenishment cycles and production rates of finished products, order times, and raw materials production rates. It is assumed that deteriorating rates are constant, and the demand rates of retailers are probabilistic. A joint optimization model is developed to maximize the benefit function of the supply chain. Cost of order, deterioration, holding, screening, production, disposal, fixed, labor, purchasing, and others. A collaborative approach is considered among the chain members where the manufacturers lead the inventory policies to avoid risks associated with deterioration items. Moreover, the Taylor series expansion approximates the exponential terms, and a solution procedure is proposed to find the optimal solutions. A case study of the Dairy Industry for highly perishable products is provided to apply the VMI model and perform a sensitivity analysis. This collaboration scheme is developed according to confidentiality policies to share information related to sales forecast, operational cost, storage strategies, quality conditions, and increased product rotation to reduce deterioration. The results show greater participation of the retailers and manufacturers in the benefits of the chain, which is consistent with the VMI policy. Finally, the conclusion, managerial implications, and future research are presented. [ABSTRACT FROM AUTHOR]

• Systematic literature review based on 136 papers on artificial intelligence applications in supply chain. • The distribution of the different AI techniques used across the SCOR areas is presented. • A classification of the research articles is proposed based on four critical structural dimensions. • Future research perspectives and areas needing further investigations are suggested. Today's supply chains are very different from those of just a few years ago, and they continue to evolve within an extremely competitive economy. Dynamic supply chain processes require a technology that can cope with their increasing complexity. In recent years, several functional supply chain applications based on artificial intelligence (AI) have emerged, yet very few studies have addressed the applications of AI in supply chain processes. Machine learning, natural language processing, and robotics are all potential enablers of supply chain transformation. Aware of the potential advantages of AI implementation in supply chains and of the paucity of work done regarding it, we explore what researchers have done so far with respect to AI and what needs further exploration. We reviewed 136 research papers published between 1996 and 2020 from the Scopus database and provided a classification of the research material according to four critical structural dimensions (level of analytics, AI algorithms or techniques, sector or industry of application, and supply chain processes). This study is the first attempt to study the AI applications in SC from a process perspective and provides a decisional framework for adequate use of AI techniques in the different SC processes. [ABSTRACT FROM AUTHOR]

Closed-loop supply chain (CLSC) is a prominent concept emphasizing on both economic and environmental aspects. Since a CLSC comprises of forward and reverse supply chains, there are a variety of internal and external factors associated with its total expected profit. In a forward flow, volatility in transportation cost, holding cost, and forecasting the market's demand are the most challenging issues for decision makers, while determining the rate of returned products and efficiency in recycling the returned products are crucial parameters to predict in reverse flow. In this paper, it is aimed to develop and apply a fully fuzzy programming (FFP) method to determine the possible upper, middle, and lower ranges of profit for a multi-echelon battery CLSC with multi-components, multi-product in multi-period under imprecise information. In addition, we extend the proposed model to multi-objective to consider the green factors related to plants and battery recovery centers. Fuzzy analytic network process (Fuzzy ANP) is utilized to alter the qualitative measurements to the measurable parameters. Then, distance technique and ε -constraint method are utilized for solving the multi-objective problem. We illustrate the application of the model in Vancouver, Canada using maps. [ABSTRACT FROM AUTHOR]

Inventory allocation decisions in a distribution system concern issues such as how much and where stock should be assigned to orders in a supply chain. When the inventory level of an inventory point is lower than the total number of items ordered by lower echelons in the chain, the decision of how many items to allocate to each ``competing'' order must take into consideration the trade-off between cost and service level. This paper proposes a decision-support system that makes use of fuzzy logic to consider inventory carrying, shortage and ordering costs as well as transportation costs. The proposed system is compared through simulation with three other inventory allocation decision support models in terms of cost and service levels achieved. Conclusions are then drawn. [ABSTRACT FROM AUTHOR]

In the recent advanced information communications and technology (ICT) era, collaborating virtually and temporarily in supply chains (SCs) to receive mutual benefits such as agility while sharing resources and information becomes an important strategy for enterprises that seek to increase their competitiveness and to optimise their processes and resource usage. As a dynamic and temporary form of alliance from the resource perspective, virtual enterprises (VEs) may contribute network resource heterogeneity and sustain competitive advantage. In addition, agility is suggested as a rare, valuable, network resource that is difficult to imitate and that cannot easily be substituted by other attributes. Although many researchers have investigated VEs and their agility, the research pays less attention to the relationship between VEs and agility in complex SC situations. This paper therefore investigates the relationship between VE and agility in SCs (ASCs) and explores drivers and enablers of agility and outcomes. To clarify the relationships between factors a structural equation model (SEM) is adopted to examine the model fit according to the measurement variables and supporting hypotheses. The results provide rich empirical evidence of the beneficial impact of VEs on ASCs, and theoretical and managerial insights that can be used to strengthen the drivers, enablers and capabilities to enhance the effectiveness of VE collaboration in ASCs in a global and dynamic context. Also, the analysis results can aid a decision maker which ones of the factors are the important ones that he or she should devote more resources and efforts on. [ABSTRACT FROM AUTHOR]

• We identify firm-specific technology opportunities in supply chains. • Technology correlations of different echelons of supply chains are considered. • A firm-centered IPC multilayer network is constructed for analysis. • A link prediction algorithm based on multilayer networks is proposed. • Technology opportunities are evaluated by indicators based on multilayer networks. Although technology opportunity discovery (TOD) for a target firm has considered the firm's internal characteristics such as technological capabilities and R&D directions, the firm's external environment, especially the supply chains it is in, is generally ignored. This paper proposes a novel approach to identifying firm-specific technology opportunities in each echelon of a multi-echelon supply chain, with the interactions among the technologies of different echelons considered. The proposed approach comprises four steps: 1) dividing the supply chain echelons and collecting patents for each echelon; 2) constructing a firm-centered International Patent Classification (IPC) multilayer network of the multi-echelon supply chain; 3) recommending IPC codes in each echelon of the supply chain for the target firm using a link prediction algorithm; and 4) evaluating technology opportunities to support the firm's R&D decisions with the indicators of technology importance, technology maturity, and technology impact. Using the General Motors Company (GM) in the fuel cell vehicle (FCV) supply chain as a case study, it is verified that the method can identify technology opportunities for GM efficiently and effectively. This work proposes an innovative method that identifies firm-specific technology opportunities from a supply chain perspective, and pioneers to apply multilayer networks to TOD. [ABSTRACT FROM AUTHOR]

• We identify firm-specific technology opportunities in supply chains. • Technology correlations of different echelons of supply chains are considered. • A firm-centered IPC multilayer network is constructed for analysis. • A link prediction algorithm based on multilayer networks is proposed. • Technology opportunities are evaluated by indicators based on multilayer networks. Although technology opportunity discovery (TOD) for a target firm has considered the firm's internal characteristics such as technological capabilities and R&D directions, the firm's external environment, especially the supply chains it is in, is generally ignored. This paper proposes a novel approach to identifying firm-specific technology opportunities in each echelon of a multi-echelon supply chain, with the interactions among the technologies of different echelons considered. The proposed approach comprises four steps: 1) dividing the supply chain echelons and collecting patents for each echelon; 2) constructing a firm-centered International Patent Classification (IPC) multilayer network of the multi-echelon supply chain; 3) recommending IPC codes in each echelon of the supply chain for the target firm using a link prediction algorithm; and 4) evaluating technology opportunities to support the firm's R&D decisions with the indicators of technology importance, technology maturity, and technology impact. Using the General Motors Company (GM) in the fuel cell vehicle (FCV) supply chain as a case study, it is verified that the method can identify technology opportunities for GM efficiently and effectively. This work proposes an innovative method that identifies firm-specific technology opportunities from a supply chain perspective, and pioneers to apply multilayer networks to TOD. [ABSTRACT FROM AUTHOR]