Solving a multi-objective master planning problem with substitution and a recycling process for a capacitated multi-commodity supply chain network

This study focuses on solving the multi-objective master planning problem for supply chains by considering product structures with multiple final products using substitutions, common components, and recycled components. This study considers five objectives in the planning process: (1) minimizing the delay cost, (2) minimizing the substitution priority, (3) minimizing the recycling penalty, (4) minimizing the substitution cost, and (5) minimizing the cost of production, processing, inventory holding and transportation. This study proposes a heuristic algorithm, called the GA-based Master Planning Algorithm (GAMPA), to solve the supply-chain master planning problem efficiently and effectively. GAMPA first transforms the closed-loop supply chain into an open-loop supply chain that plans and searches the sub-networks for each final product. GAMPA then uses a genetic algorithm to sort and sequence the demands. GAMPA selects the chromosome that generates the best planning result according to the priority of the objectives. GAMPA plans each demand sequentially according to the selected chromosome and a randomly-selected production tree. GAMPA tries different production trees for each demand and selects the best planning result at the end. To show the effectiveness and efficiency of GAMPA, a prototype was constructed and tested using complexity analysis and computational analysis to demonstrate the power of GAMPA.