An efficient hybrid particle swarm optimization for the Job Shop Scheduling Problem

Abstract

This paper proposes a hybrid particle swarm optimization algorithm for solving Job Shop Scheduling Problems (JSSP) to minimize the maximum makespan. A new hybrid heuristic, based on Particle Swarm Optimization (PSO), Tabu Search (TS) and Simulated Annealing (SA), is presented. PSO combines local search (by self-experience) with global search (by neighboring experience), achieving a high search efficiency. TS uses a memory function to avoid being trapped at a local minimum, and has emerged as an effective algorithmic approach for the JSSP. This method can also be referred to as calculation of the horizontal direction. SA employs certain probability to avoid becoming trapped in a local optimum and the search process can be controlled by the cooling schedule (also known as calculation of vertical direction). By reasonably combining these three different search algorithms, we develop a robust, fast and simply implemented hybrid optimization algorithm HPTS (Hybrid of Particle swarm optimization, Tabu search and Simulated annealing). This hybrid algorithm is applied to the standard benchmark sets and compared with other approaches. The experimental results show that the proposed algorithm could obtain the high-quality solutions within relatively short computation time. For 6 of 43 instances, new upper bounds among the unsolved problems are found in a short time in HPTS.