METAHEURISTIC OPTIMIZATION FOR RESILIENT AND SUSTAINABLE SUPPLY CHAIN MODEL TO OVERCOME DISRUPTIONS

Abstract

Inventory control in supply chain is often hampered because of uncertain disruption which leads to lengthening delivery times and reduces output. This results in rise in demand, and in some cases, demand drops also. This change in demand pattern causes large fluctuations which is why it became a very difficult problem for retail managers to manage inventory properly. The solution lies within changing the inventory managing strategies. Recent studies have shown how changing inventory strategies can help to overcome disruption and help to build a resilient and sustainable supply chain. Therefore, this study is based on evaluating a supplier-retailer partnership through Vendor Managed Inventory (VMI) approach. VMI is an integrated inventory policy where supplier uses buyer’s spaces to store product maintaining the ownership of it. This strategy reduces the requirement of keeping safety stock thus leads to significant cost savings. An analytical model is formed with metaheuristic algorithm approach to evaluate if VMI system really affect inventory cost when it is implemented. As no benchmark is available to evaluate the performance obtained by implementing metaheuristic algorithm, Brute Force algorithm is also conducted to measure the efficacy of the model. The study will evaluate the implementation of VendorManaged Inventory (VMI) and explore some underlying factors that might affect the effectiveness of VMI on cost reduction and tackling disruption. Sensitivity analysis is done on essential parameters like fuzzy cut, resilience coefficient, sustainability factors. Each algorithm is employed to calibrate the performance of the model and the findings are compared in terms of the solution quality.