A Bi-Objective Optimization For Production And Distribution Scheduling Problems With Parallel Machines Environment

Abstract

The study addresses the optimization of production scheduling and distribution planning in a supply chain with identical parallel machines. It focuses on a bi-objective problem aimed at improving customer satisfaction and minimizing costs. The first objective seeks to minimize total weighted tardiness and operation time, while the second aims to reduce costs related to reputational damage, earliness penalties, and batch delivery. A mathematical model is developed, and two meta-heuristic algorithms—Multi-Objective Ant Colony Optimization (MOACO) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II)—are implemented to find near-optimal solutions. These algorithms allow decision-makers to balance efficiency and cost, providing strategic insights for enhancing supply chain management. Through comparative analysis, the effectiveness of each algorithm is evaluated, demonstrating their capabilities in handling complex scheduling scenarios.