A multi-objective analytics approach for supply chain optimization in flexible manufacturing systems

This study contributes to the advancement of supply chain scheduling (SCS) through the development of a comprehensive mathematical framework that unifies multi-objective supplier selection, demand allocation, production scheduling, and inventory management within flexible manufacturing systems (FMS). Amidst the rapid progress of FMS and Industry 4.0 technologies, integrating these supply-chain decisions has become indispensable for ensuring timely and cost-efficient delivery of customized products. Yet research remains limited when supply, inventory, flexible routing and sequencing decisions must be handled simultaneously, often yielding conflicting objectives. We therefore propose a bi-objective SCS model that jointly optimizes supply, inventory and production portfolios to meet heterogeneous customer orders under due-date constraints. The shop floor is modeled as a flexible job shop with sequence-dependent setup times and inventory constraints, and the framework embeds supplier selection and demand allocation decisions for critical parts. Since the resulting problem is NP-hard, a multi-objective JAYA algorithm (MOJAYA) is devised, featuring a Pareto-cluster update rule and a problem-specific co-evaluated local search. Extensive experiments on 15 benchmark instances show MOJAYA, against four established algorithms, consistently yields wider, more uniform Pareto fronts, lowering mean inverted generational distance by up to 48% and increasing hyper-volume by up to 23% within the same computational budget; Friedman and Wilcoxon tests confirm these gains are statistically significant (p<0.05). In a representative instance, the decision schedule costs 357448 with 44 cumulative tardiness, and supplies managers with detailed production, supply, and inventory portfolios. The proposed approach therefore enhances decision-making flexibility across supply-chain stages, offering a data-driven tool for SCS problems.