| dc.description.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. | en_US |
