Designing a sustainable fish closed-loop supply chain network under uncertainty.

There is increasing attention to the sustainable development of supply chain (SC) and reverse logistics (RL) in the contemporary competitive economy, notably in the food sector, by scholars and stakeholders. This study investigates a sustainable closed-loop supply chain (CLSC) for fish due to its high value in the family food basket, its perishability, and the importance of waste product recycling. A multi-objective mathematical model is developed under uncertainty and sustainability criteria to optimize production rates with the aim of better distribution among different demand markets, total costs, social issues, and negative environmental effects (e.g., CO2 emissions and unused/waste products). A combination of exact, meta-heuristic, and hybrid meta-heuristic algorithms are used to solve the suggested model. Then, the optimal solutions are found using the Taguchi method by evaluating the best initial replies. The solutions are evaluated based on various performance metrics. The analysis of variance (ANOVA) and the "filtering/displaced ideal solution" methods determine the best solution approach. Moreover, a case study with a trout CLSC in Northern Iran is examined. In addition, the Lingo software utilizes the ε-constraint method to evaluate and check the performance of the algorithms under different levels of uncertainty. Finally, sensitivity analyses are carried out to confirm the efficacy of the proposed algorithms. The findings demonstrate the proposed network's outstanding consistency with the algorithms used and its application and efficiency.

An enhanced PSO algorithm to configure a responsive-resilient supply chain network considering environmental issues: a case study of the oxygen concentrator device.

In recent years, the hyper-competitive marketplace has led to a drastic enhancement in the importance of the supply chain problem. Hence, the attention of managers and researchers has been attracted to one of the most crucial problems in the supply chain management area called the supply chain network design problem. In this regard, this research attempts to design an integrated forward and backward logistics network by proposing a multi-objective mathematical model. The suggested model aims at minimizing the environmental impacts and the costs while maximizing the resilience and responsiveness of the supply chain. Since uncertainty is a major issue in the supply chain problem, the present paper studies the research problem under the mixed uncertainty and utilizes the robust possibilistic stochastic method to cope with the uncertainty. On the other side, since configuring a supply chain is known as an NP-Hard problem, this research develops an enhanced particle swarm optimization algorithm to obtain optimal/near-optimal solutions in a reasonable time. Based on the achieved results, the developed algorithm can obtain high-quality solutions (i.e. solutions with zero or a very small gap from the optimal solution) in a reasonable amount of time. The achieved results demonstrate the negative impact of the enhancement of the demand on environmental damages and the total cost. Also, according to the outputs, by increasing the service level, the total cost and environmental impacts have increased by 41% and 10%, respectively. On the other hand, the results show that increasing the disrupted capacity parameters has led to a 17% increase in the total costs and a 7% increase in carbon emissions. Supplementary Information: The online version contains supplementary material available at 10.1007/s00521-022-07739-8.