A New Hybrid Particle Swarm Optimization for Solving Flow Shop Scheduling Problem with Fuzzy due Date

Abstract

Coping with the characteristic of flow shop scheduling problem with uncertain due date, fuzzy arithmetic on fuzzy numbers is applied to describe the problem, and then a new hybrid algorithm model which integrate particle swarm optimization into the evolutionary mechanism of the knowledge evolution algorithm is presented to solve the problem. By the evolutionary mechanism of knowledge evolution algorithm, we can exploit the global search ability. By the operating characteristic of PSO, we can enhance the local search ability. The algorithm is tested with MATLAB simulation. The result, compared with Genetic algorithm and modified particle swarm optimization, shows the feasibility and effectiveness of the proposed algorithm.

On the exact solution of the no-wait flow shop problem with due date constraints

“…Table 1 summarizes some of the early efforts to solve the scheduling problems with a form of due date constraints using a non-exact method. More recently, Tang et al (2011) developed a metaheuristic to deal with fuzzy due dates in a flow shop environment. Panwalkar and Koulamas (2012) considered a twomachine flow shop problem with the objective of minimizing the total tardy jobs and finding a common due date for the jobs, and developed a heuristic algorithm with computational complexity of 2 () On for a special case of the problem.…”