Multi-objective evolutionary algorithm based on multiple neighborhoods local search for multi-objective distributed hybrid flow shop scheduling problem

Abstract

In order to be competitive in today’s rapidly changing business world, enterprises have transformed a centralized to a decentralized structure in many areas of decision. It brings a critical problem that is how to schedule the production resources efficiently among these decentralized production centers. This paper studies a multi-objective distributed hybrid flow shop scheduling problem (MDHFSP) with the objectives of minimizing makespan, total weighted earliness and tardiness, and total workload. In the MDHFSP, a set of jobs have to be assigned to several factories, and each factory contains a hybrid flow shop scheduling problem with several parallel machines in each stage. A multi-objective evolutionary algorithm based on multiple neighborhoods local search (MOEA-LS) is proposed to solve the MDHFSP. In the initialization phase, a weighting mechanism is used to decide which position is the best one for each job when constructing a new sequence. Several multiple neighborhoods local search operators based on the three objectives are designed to generate offsprings. Some worse neighboring solutions are replaced by the solutions in the achieve set with a simulated annealing probability. In order to avoid trapping into local optimum, an adaptive weight updating mechanism is utilized when the achieve set has no change. The comprehensive comparison with other classic multi-objective optimization algorithms shows the proposed algorithm is very efficient for the MDHFSP.