IMPLEMENTATION OF OPTIMIZATION ALGORITHMS IN PRODUCTION PLANNING AND RESOURCE ALLOCATION

Abstract

This article investigates the implementation of advanced optimization algorithms in production planning and resource allocation within modern manufacturing environments. As production systems become increasingly complex and dynamic, traditional heuristic methods and standard Material Requirements Planning (MRP) systems fail to provide optimal solutions for the Flexible Job Shop Scheduling Problem (FJSSP). This study proposes and evaluates a hybrid metaheuristic approach that integrates Genetic Algorithms (GA) with Particle Swarm Optimization (PSO) to simultaneously minimize makespan, reduce machine idle time, and balance workload distribution. Through extensive discrete-event simulation using standard benchmark datasets, the proposed hybrid algorithm is compared against standalone metaheuristics and exact mathematical programming methods. The results demonstrate that the hybrid GA-PSO approach achieves a 12.4% reduction in overall makespan and improves machine utilization rates by 8.7% compared to traditional scheduling methods, while maintaining computational tractability for large-scale instances. Furthermore, the article analyzes the practical challenges of integrating these algorithms into legacy Enterprise Resource Planning (ERP) systems and proposes architectural solutions for real-time dynamic rescheduling in stochastic environments.