Paul Sanjoy Kumar, Chowdhury Priyabrata, Chakrabortty Ripon Kumar, Ivanov Dmitry, Sallam Karam
UTS Business School, University of Technology Sydney, Sydney, Australia.
School of Accounting, Information Systems and Supply Chain, RMIT University, Melbourne, Australia.
Ann Oper Res. 2022 Apr 11:1-46. doi: 10.1007/s10479-022-04650-2.
The COVID-19 pandemic has wreaked havoc across supply chain (SC) operations worldwide. Specifically, decisions on the recovery planning are subject to multi-dimensional uncertainty stemming from singular and correlated disruptions in demand, supply, and production capacities. This is a new and understudied research area. In this study, we examine, SC recovery for high-demand items (e.g., hand sanitizer and face masks). We first developed a stochastic mathematical model to optimise recovery for a three-stage SC exposed to the multi-dimensional impacts of COVID-19 pandemic. This allows to generalize a novel problem setting with simultaneous demand, supply, and capacity uncertainty in a multi-stage SC recovery context. We then developed a chance-constrained programming approach and present in this article a new and enhanced multi-operator differential evolution variant-based solution approach to solve our model. With the optimisation, we sought to understand the impact of different recovery strategies on SC profitability as well as identify optimal recovery plans. Through extensive numerical experiments, we demonstrated capability towards efficiently solving both small- and large-scale SC recovery problems. We tested, evaluated, and analyzed different recovery strategies, scenarios, and problem scales to validate our approach. Ultimately, the study provides a useful tool to optimise reactive adaptation strategies related to how and when SC recovery operations should be deployed during a pandemic. This study contributes to literature through development of a unique problem setting with multi-dimensional uncertainty impacts for SC recovery, as well as an efficient solution approach for solution of both small- and large-scale SC recovery problems. Relevant decision-makers can use the findings of this research to select the most efficient SC recovery plan under pandemic conditions and to determine the timing of its deployment.
新冠疫情在全球范围内对供应链运营造成了严重破坏。具体而言，复苏计划的决策面临着多维度的不确定性，这些不确定性源于需求、供应和生产能力方面的单一及相关干扰。这是一个全新且研究不足的领域。在本研究中，我们考察了高需求物品（如洗手液和口罩）的供应链复苏情况。我们首先建立了一个随机数学模型，以优化受新冠疫情多维度影响的三阶段供应链的复苏。这使得我们能够在多阶段供应链复苏背景下，概括出一个同时存在需求、供应和产能不确定性的新问题设定。然后，我们开发了一种机会约束规划方法，并在本文中提出了一种基于多算子差分进化变体的新的增强型求解方法来求解我们的模型。通过优化，我们试图了解不同复苏策略对供应链盈利能力的影响，并确定最优复苏计划。通过广泛的数值实验，我们展示了有效解决小规模和大规模供应链复苏问题的能力。我们测试、评估并分析了不同的复苏策略、情景和问题规模，以验证我们的方法。最终，该研究提供了一个有用的工具，用于优化与在疫情期间如何以及何时部署供应链复苏行动相关的反应性适应策略。本研究通过开发一个具有多维度不确定性影响的独特问题设定，以及一种有效解决小规模和大规模供应链复苏问题的求解方法，为相关文献做出了贡献。相关决策者可以利用本研究的结果，在疫情条件下选择最有效的供应链复苏计划，并确定其部署时机。
