Revised solution technique for a bi-level location-inventory-routing problem under uncertainty of demand and perishability of products

Bi-level programming is an efficient tool to tackle decentralized decision-making processes in supply chains with upper level (i.e., leader) and lower level (i.e., follower). The leader makes the first decision while the follower makes the second decision. In this research, a bi-level programming formulation for the problem of location-inventory-routing in a two-echelon supply chain, including a number of central warehouses in the first echelon and retailers in the second echelon with perishable products under uncertain demand, is proposed. The total operational costs at both levels are minimized considering capacity constraints. Due to the uncertain nature of the problem, a scenario-based programming is utilized. The economic condition or unforeseen events such as COVID-19 or Russia-Ukraine war can be good examples for uncertainty sources in today's world. The model determines the optimal locations of warehouses, the routes between warehouses and retailers, number of received shipments and the amount of inventory held at each retailer. A revised solution method is designed by using multi-choice goal programming for solving the problem. The given revised method attempts to minimize the deviations of each decision maker's solution from its ideal value assuming that the upper level is satisfied higher than the lower level. Base on some numerical analysis, the proposed solution technique is more sensitive to the upper bounds of the goals rather than the lower bounds. © 2022 Elsevier B.V.

Integrated supplier selection, scheduling, and routing problem for perishable product supply chain: A distributionally robust approach

Due to the global outbreak of COVID-19, the perishable product supply chains have been impacted in different ways, and consequently, the risks of food insecurity have been increased in many affected countries. The uncertainty in supply and demand of perishable products, are among the most influential factors impacting the supply chain networks. Accordingly, the provision and distribution of food and other perishable commodities have become much more important than in the past. In this study, a bi-objective optimization model is proposed for a three-echelon perishable food supply chain (PFSC) network with multiple products to formulate an integrated supplier selection, production scheduling, and vehicle routing problem. The proposed model aims to mitigate the risks of demand and supply uncertainties and reinforce the distribution-related decisions by simultaneously optimizing the total network costs and suppliers' reliability. Using the distributionally robust modeling paradigm, the probability distribution of uncertain demand is assumed to belong to an ambiguity set with given moment information. Accordingly, distributionally robust chance-constrained approach is applied to ensure that the demands of retailers and capacity of vehicles are satisfied with high probability. Leveraging duality and linearization techniques, the proposed model is reformulated as a mixed-integer linear program. Then, the weighted goal programming approach is adopted to address the multi-objectiveness of the proposed optimization model. To certify the performance and applicability of the model, a real-world case study in the poultry industry is investigated. Finally, the sensitivity analysis is conducted to evaluate the impacts of influential parameters on the objective functions and optimal decisions, and then some managerial insights are provided based on the obtained results. © 2022 Elsevier Ltd

A causality analysis of risks to perishable product supply chain networks during the COVID-19 outbreak era: An extended DEMATEL method under Pythagorean fuzzy environment.

In nowadays world, firms are encountered with many challenges that can jeopardize business continuity. Recently, the coronavirus has brought some problems for supply chain networks. Remarkably, perishable product supply chain networks, such as pharmaceutical, dairy, blood, and food supply chains deal with more sophisticated situations. Generally, during pandemic outbreaks, the activities of these industries can play an influential role in society. On the one hand, products of these industries are considered to be daily necessities for living. However, on the other hand, there are many new restrictions to control the coronavirus prevalence, such as closing down all official gatherings and lessening the work hours, which subsequently affect the economic growth and gross domestic product. Therefore, risk assessment can be a useful tool to forestall side-effects of the coronavirus outbreaks on supply chain networks. To that aim, the decision-making trial and evaluation laboratory approach is used to evaluate the risks to perishable product supply chain networks during the coronavirus outbreak era. Feedback from academics was received to identify the most important risks. Then, experts in pharmaceutical, food, and dairy industries were inquired to specify the interrelations among risks. Then, Pythagorean fuzzy sets are employed in order to take the uncertainty of the experts' judgments into account. Finally, analyses demonstrated that the perishability of products, unhealthy working conditions, supply-side risks, and work-hours are highly influential risks that can easily affect other risk factors. Plus, it turned out that competitive risks are the most susceptive risk in the effect category. In other words, competition among perishable product supply chain networks has become even more fierce during the coronavirus outbreak era. The practical outcomes of this study provide a wide range of insights for managers and decision-makers in order to prevent risks to perishable product supply chain networks during the coronavirus outbreak era.

Coordinated inventory control and pricing policies for online retailers with perishable products in the presence of social learning

During the period of COVID-19, restrictive social distancing measures have stimulated retailers to increasingly turn to online selling platforms and apply social technologies in marketing even for food and grocery products. Social technologies create opportunities for online retailers to effectively facilitate word-of-mouth communication by allowing consumers to share their consumption experiences. It makes consumers’ behavior more observable to potential customers and leads to social learning. Social learning has a notable impact on purchasing decisions of potential customers, influencing the demand for the product. In this regard, this paper aims to examine the coordinated dynamic pricing and inventory control problem for a perishable product under social learning. The idea is that online retailers who sell a perishable product under Expiration Date-Based Pricing (EDBP) policy counteract the negative quality inference of this practice through social learning. To formulate the problem, a mathematical model is developed and its structural properties are analyzed for a two-period lifetime product. Furthermore, numerical analysis is conducted in a real case study to derive some managerial insights. The obtained results show that the online retailer can promote the EDBP by adopting a consumer-generated online review system. As well, to better exploit the system, the online retailer should adjust the product pricing and inventory control policies with the evolution of consumers’ ratings. Finally, the firm’s profit and waste avoidance are improved by incorporating consumers’ social learning behavior into the pricing and inventory policies.

Exploring Critical Events of Perishable Product Supply Chain Using Fault Tree Methodology to Deal with Impacts of COVID-19

This research investigates the impact of pandemic COVID-19, on the perishable product supply chain (PPSC). Thematic analysis for the cause of failure in PPSC has been identified through the NVivo application. It examines the events that cause disruption. Secondly, fault tree methodology has adopted qualitative evaluation using the minimum cut set analysis and importance measures. A case study of the apple supply chain in Shimla, India has been included, collecting data from respondents, research papers, government reports, and newspaper articles published from the period March 2020 to December 2020. The occurrence of failure in the apple supply chain included crop yield loss, unavailability, and inaccessibility of apple products. After analysis, 13 minimum cut sets are obtained. These include critical failure event as: assistance in failure from government and organization, high food prices, labour shortage, and cross border restriction. Potential strategies for resilient PPSC have been proposed for an efficient decision-making process. © 2021 IEEE.