Memory Based Green Supply Chain Management Using Metaheuristic Optimization Techniques

In this paper, we emphasize environmental cooperation, which has been viewed as a crucial relational competence to enable the design and execution of green supply chain management (GSCM) based on a strategic plan. Caputo type fractional order derivative-based three echelon supply chain model is developed to establish memory-based inventory-related metrics. Differential evolution (DE) and particle swarm optimization (PSO) algorithms are used to construct and solve the nonlinear optimization problem associated with GSCM. The numerical simulation is carried out to determine the best GSCM policies based on statistical indices. The numerical results demonstrate that the optimal selling price, production time, and level of green investment all play an important role in increasing profitability by lowering carbon emissions. We present managerial insights that use prior information and can be used to increase the profit of the concerned business that supports green technology and contribute significantly to economic growth