Shahed Kazi Safowan, Azeem Abdullahil, Ali Syed Mithun, Moktadir Md Abdul
Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1000, Bangladesh.
Institute of Leather Engineering and Technology, University of Dhaka, Dhaka, 1209, Bangladesh.
Environ Sci Pollut Res Int. 2021 Jan 5:1-16. doi: 10.1007/s11356-020-12289-4.
This study develops a mathematical model to mitigate disruptions in a three-stage (i.e., supplier, manufacturer, retailer) supply chain network subject to a natural disaster like COVID-19 pandemic. This optimization model aims to manage supply chain disruptions for a pandemic situation where disruptions can occur to both the supplier and the retailer. This study proposes an inventory policy using the renewal reward theory for maximizing profit for the manufacturer under study. Tested using two heuristics algorithms, namely the genetic algorithm (GA) and pattern search (PS), the proposed inventory-based disruption risk mitigation model provides the manufacturer with an optimum decision to maximize profits in a production cycle. A sensitivity analysis was offered to ensure the applicability of the model in practical settings. Results reveal that the PS algorithm performed better for such model than a heuristic method like GA. The ordering quantity and reordering point were also lower in PS than GA. Overall, it was evident that PS is more suited for this problem. Supply chain managers need to employ appropriate inventory policies to deal with several uncertain conditions, for example, uncertainties arising due to the COVID-19 pandemic. This model can help managers establish and redesign an inventory policy to maximize the profit by considering probable disruptions in the supply chain network.
本研究建立了一个数学模型，以缓解受 COVID - 19 大流行等自然灾害影响的三阶段（即供应商、制造商、零售商）供应链网络中的中断情况。该优化模型旨在应对大流行情况下的供应链中断问题，此时供应商和零售商都可能出现中断。本研究提出了一种基于更新奖励理论的库存策略，以最大化所研究制造商的利润。使用遗传算法（GA）和模式搜索（PS）这两种启发式算法进行测试，所提出的基于库存的中断风险缓解模型为制造商提供了在一个生产周期内实现利润最大化的最优决策。进行了敏感性分析以确保该模型在实际环境中的适用性。结果表明，对于此类模型，PS 算法比 GA 等启发式方法表现更好。PS 算法中的订货量和再订货点也低于 GA。总体而言，很明显 PS 更适合此问题。供应链管理者需要采用适当的库存策略来应对多种不确定情况，例如因 COVID - 19 大流行而产生的不确定性。该模型可以帮助管理者通过考虑供应链网络中可能的中断来建立和重新设计库存策略，以实现利润最大化。
