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In today's fast-paced world, delays or prolonged customer waiting times pose a threat to the firm's profitability. This study utilizes the mean-CVaR metric to incorporate the risk associated with prolonged customer waiting times into the optimal trade-off decisions. For this purpose, we consider a single inventory system that faces Poisson demand and utilizes a base-stock policy to replenish its inventory, which takes a fixed amount of time. The firm implements a preorder strategy, encouraging customers to place their orders a fixed amount of time in advance of their actual needs, a period referred to as the commitment lead time. The firm rewards customers with a bonus termed the commitment cost, which increases with the length of the commitment lead time. We aim to determine the optimal control policy, including the optimal base-stock level and optimal commitment lead time, that minimizes the long-run average cost. The cost includes inventory holding, commitment, and customer waiting costs, with the latter adjusted for the firm's degree of risk aversion. The optimal policy depends on the interdependence of the decisions, with the optimal commitment lead time following a "bang-bang" pattern, and the corresponding optimal base-stock level taking an "all-or-nothing" form. For linear commitment costs with a cost factor per time unit, we identify a threshold that increases with the firm's risk aversion degree. Firms with greater risk aversion typically favor the buy-to-order strategy, while those with lower risk aversion may opt for either buy-to-stock or buy-to-order depending on their assessment of waiting costs. [ABSTRACT FROM AUTHOR]

Supply or service networks are vulnerable to hazards that can stem from both unintentional and intentional human actions, as well as natural calamities. To ensure vital infrastructure resilience in these networks, address the interdiction facility location problem. Currently, diverse groups of attackers target supply and service systems to cause maximum disruption. Collaboration among attackers improves system vulnerability detection accuracy and realism. This research examines the challenges of interdiction location with different defense systems and heterogeneous attackers. To address this challenge, a mixed-integer non-linear bi-level programming model was considered. Heuristic optimization methods including variable neighborhood search, simulated annealing, and hybrid variable neighborhood search are used to efficiently solve the suggested model. The outcomes of our investigation suggest that implementing various protective methods leads to an escalation in system damage when attackers collaborate. Furthermore, the findings illustrate the efficacy of the suggested algorithms in resolving interdiction location issues within supply or service networks. [ABSTRACT FROM AUTHOR]

Recently, environmental concerns have drawn worldwide attention. As a result, the international communities have put pressure on both governments and industries to develop environmental conservation policies and green products, respectively. Also, the consumers' environmental awareness has increased to the point that today's consumers consider the greenness of the products in addition to the purchase price. In this study, we consider an industry consisting of two manufacturers that compete over selling price and carbon emissions, while the government levies a carbon emissions tax. We formulate the problem using a game-theoretic approach assuming the leader–follower relationship between the government and industry. The manufacturers maximize their profits, while the government maximizes three components: social welfare, environmental conservation, and revenues. We study the problem in two different settings: one in which the manufacturers have similar powers (Nash) and one in which one manufacturer is the leader in the market (Stackelberg). We find that superiority in production technology or a lower unit production cost does not necessarily lead to a lower selling price and that the outcome depends on the carbon emission tax rate. Based on a numerical experiment, government intervention reduces the total carbon emissions by more than almost half in both settings. We also found that an industry with Nash structure is preferred from the perspective of the consumers. [ABSTRACT FROM AUTHOR]

The purpose of this article is to develop a competitive supply chain network (SCN) in the face of uncertainty. The objective of the leader chain is to maximize total network profits by strategically locating suppliers, manufacturers, distribution centers, and retailers. Additionally, the follower chain seeks to maximize the network's profit. Both factors, optimal flow allocation to different echelons of the SCN and product pricing, are examined in the leader chain and follower chain. The KKT conditions are used in this article to convert a bi-level model to a one-level model. Additionally, a fuzzy programming technique is used to control the problem's uncertain parameters. According to the results obtained using the fuzzy programming technique, increasing the uncertainty rate increases demand while decreasing the OBFV and average selling price of products. Finally, the problem was untangled using a novel hybrid genetic and ant-lion optimization algorithm (HGALO). The results of problem solving in larger sizes demonstrate HGALO's superior efficiency in comparison with the other algorithm. [ABSTRACT FROM AUTHOR]

The food industry is a major source of greenhouse gases (GHG). Given that consumers in this industry are aware of the negative consequences of GHG emissions, such as global warming and climate change, members of the food supply chain should consider mechanisms to reduce GHG emissions. The purpose of this paper is to examine the effects of supply chain structure and cooperation methods on the objectives and decisions of a sustainable food supply chain composed of one manufacturer and two suppliers. In the first scenario, a single-level problem is solved for a centralized supply chain. Other scenarios consider the decentralized structure, in which supply chain members face constraints such as maximum additive use and GHG emission, budget, and capacity. The bi-level programming is used to model competition between members of the sustainable food supply chain. It is demonstrated first that the lower-level models in decentralized scenarios can be converted to a single-level model, and then the proposed bi-level model is converted to a single-level one using the KKT method. Computational results show that the alliance of the manufacturer and the first supplier yields the highest total profit for all decentralized scenarios. Because the lowest GHG emission rate among decentralized scenarios is achieved through alliance and cost-sharing mechanisms, the use of these mechanisms concurrently is appropriate for environmental purposes. In scenarios where supply chain members compete with each other, it is found to be cost-effective to increase the budget. Additional considerations can be made regarding the effects of other variables such as distance and advertising on demand as well as alternative forms of the demand function. [ABSTRACT FROM AUTHOR]