Grey Wolf Optimizer and Whale Optimization Algorithm for Stochastic Inventory Management of Reusable Products in a two-level Supply Chain

TL;DR

This paper introduces a new stochastic inventory management problem for reusable products in a supply chain, applying novel metaheuristics (GWO and WOA) and statistical validation to find effective solutions.

Contribution

It formulates a complex nonlinear model with uncertainty and proposes GWO and WOA as innovative solution methods, validated against an exact algorithm.

Findings

GWO outperforms WOA in solution quality

Both algorithms perform comparably to the exact method

Metaheuristics effectively handle stochastic constraints

Abstract

Product reuse and recovery is an efficient tool that helps companies to simultaneously address economic and environmental dimensions of sustainability. This paper presents a novel problem for stock management of reusable products in a single-vendor, multi-product, multi-retailer network. Several constraints, such as the maximum budget, storage capacity, number of orders, etc., are considered in their stochastic form to provide a more realistic framework. The presented problem is formulated as a constrained nonlinear mathematical model. The chance-constrained programming method is suggested to deal with the constraints' uncertainty. Regarding the nonlinearity of the model, grey wolf optimizer (GWO) and whale optimization algorithm (WOA) as two novel metaheuristics are presented as solution approaches, and the sequential quadratic programming (SQP) exact algorithm validates their performance. The parameters of algorithms are calibrated using the Taguchi method for the design of experiments. Extensive analysis is established by solving several numerical results in different sizes and utilizing several comparison measures. Also, the results are compared statistically using proper parametric and non-parametric tests. The analysis of the results shows a significant difference between the algorithms, and GWO has a better performance for solving the presented problem. In addition, both algorithms perform well in searching the solution space, where the GWO and WOA differences with the optimal solution of the SQP algorithm are negligible.