Comparative Performance Analysis of Meta-Heuristic Algorithms in Distributed Job Shop Scheduling

In the field of Job-Shop Scheduling, complexity increases when production facilities are located in different areas. To effectively carry out operations, it is necessary to make optimal use of available resources. Meta-heuristic algorithms are commonly used to solve NP-hard problems, including Distributed Job-Shop Scheduling. This study aims to tackle the challenge by using meta-heuristic algorithms, including Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Grey Wolf Optimization (GWO). The study employs a workload-based facility ordering approach to minimize the product's makespan by distributing operations among facilities. The scheduling solutions are represented using the Smallest Position Value encoding scheme. The experimental studies used 28 Job-Shop Scheduling benchmark problems, with the number of plants set between 2 and 4. The results consistently show that the GWO algorithm outperforms PSO and ABC in finding optimal or near-optimal solutions for various problem instances.