A Highly Effective Optimization Approach for Managing Reverse Warehouse System Capacity Across Diverse Scenarios

Advanced technologies are gaining more attention in every industry sector. Therefore, to develop a logistics network that can adjust to effectively manage inventories for managing logistics while maximizing profit for all systems involved. The main objective of this research is to calculate the number of products to be dispatched at different intervals within a logistics network, aiming to minimize the overall costs of a reverse warehouse system called an automatic reverse storage system (ARWS). A mathematical model is presented to optimize the total cost and the delay in transporting rankings in a warehouse system network, considering that some scenarios are uncertain with capacity. To address the mathematical approach for both standard and extensive sizes, various meta-heuristics algorithms are utilized within the MATLAB software, and the outcomes are determined with the globally optimal solution to handle better responses to several scenarios to allocate items to shelves and complete orders and routing. The results indicate that the suggested algorithm performs well, with the total quantities sent to the warehouse equal to those derived from the precise solution. Additionally, the value of the objective function decreases with an increase in the number of iterations.