Orthogonal particle swarm optimization for multi-objective job shop scheduling problems

The multi-objective job shop scheduling problem is a popular topic in manufactural management domain in recent years. This paper presents a multi-objective orthogonal particle swarm optimization (MOOPSO) for this problem. MOOPSO introduces the orthogonal design method from the field of experiment design to prevent the algorithm from being premature and falling into local optima, which in turns improves the global solution space exploring capability. Feasibility and efficiency of MOOPSO are verified through numerical experiments by comparing it with some other algorithms.