Job-shop scheduling problem (JSSP) is one of the well-known hardest combinatorial optimization problems; lacking efficient exact solutions. In this paper, we propose a hybrid algorithm with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), called as Fuzzy Genetic Swarm Optimization (FGSO) for solving the scheduled JSSP problems with fuzzy processing time. The objective of JSSP is to minimize the makespan from a job sequence selected by ranking fuzzy numbers in the (λ,1) interval based on signed distance. The hybrid algorithm is modeled on the concepts of Darwin's theory based on natural selection and evolution, and on cultural and social rules derived from the swarm intelligence. The approach is tested on a set of 162 standard instances obtained from the OR literature and Taillard benchmarks. The feasibility and efficiency of the proposed method is evaluated in comparison with other state-of-the-art approaches. The computational results validate the effectiveness of the proposed algorithm.
P. Surekha and Sumathi, S., “Solution to the Job Shop Scheduling Problem using Hybrid Genetic Swarm Optimization based on (λ,1) Fuzzy Processing Time”, European Journal on Scientific Research, vol. 64, no. 2, pp. 168-188, 2011.