A particle swarm optimization using random keys for flexible flow shop scheduling problem with sequence dependent setup times

In this research, a particle swarm optimization algorithm (PSO) using random keys is developed to schedule flexible flow lines with sequence dependent setup times to minimize makespan. The flexible flow line scheduling problem is a branch of production scheduling and is found in industries such as printed circuit board and automobile manufacturing. It is well known that this problem is NP-hard. For this reason, we approach the problem by implementing a particle swarm optimization (PSO), a metaheuristic which is inspired by the motion of a flock of birds or a school of fish searching for food. The proposed PSO has many features, such as the use of random keys for encoding the solution, "bounceback" of particles into the solution space and tuning of learning and weighting factors. The proposed PSO algorithm is implemented in C and tested on a large set of data found in the literature. Extensive computational experiments are facilitated through the use of high-throughput computing via Clemson's Condor grid. The solution qualities are compared and evaluated with the help of lower bound developed by Kurz and Askin [16]. Unfortunately, we conclude that the proposed PSO does not perform well for the problem examined. Areas for future work are identified to improve the overall performance of proposed PSO.