Abstract
A novel multiobjective memetic algorithm based on decomposition (MOMAD) is proposed to solve multiobjective flexible job shop scheduling problem (MOFJSP), which simultaneously minimizes makespan, total workload, and critical workload. Firstly, a population is initialized by employing an integration of different machine assignment and operation sequencing strategies. Secondly, multiobjective memetic algorithm based on decomposition is presented by introducing a local search to MOEA/D. The Tchebycheff approach of MOEA/D converts the three-objective optimization problem to several single-objective optimization subproblems, and the weight vectors are grouped by K-means clustering. Some good individuals corresponding to different weight vectors are selected by the tournament mechanism of a local search. In the experiments, the influence of three different aggregation functions is first studied. Moreover, the effect of the proposed local search is investigated. Finally, MOMAD is compared with eight state-of-the-art algorithms on a series of well-known benchmark instances and the experimental results show that the proposed algorithm outperforms or at least has comparative performance to the other algorithms.
Highlights
The job shop scheduling problem (JSP) is one of the most important and difficult problems in the field of manufacturing which processes a set of jobs on a set of machines
Different from JSP which one operation is merely allowed to process on a specific machine, the flexible job shop scheduling problem (FJSP) permits one operation processed by any machine from its available machine set
With the purpose of making the proposed algorithm more applicable, four aspects are studied: (1) integration of different machine assignment and operation sequencing strategies are presented to construct the initial population; (2) objective normalization is incorporated into Tchebycheff approach to convert an multiobjective optimization problems (MOPs) into a number of single-objective optimization subproblems; (3) all weight vectors are divided into a few groups based on K-means clustering: some superior individuals corresponding to different weight vector groups are selected by using a selection mechanism; (4) local search based on moving critical operations is applied on selected individuals
Summary
The job shop scheduling problem (JSP) is one of the most important and difficult problems in the field of manufacturing which processes a set of jobs on a set of machines. Multiobjective flexible job shop scheduling problem (MOFJSP) has received much attention, and, until now, many algorithms have been developed to solve this kind of problem These methods can be classified into two groups: one is a priori approach and the other is Pareto approach. With the purpose of making the proposed algorithm more applicable, four aspects are studied: (1) integration of different machine assignment and operation sequencing strategies are presented to construct the initial population; (2) objective normalization is incorporated into Tchebycheff approach to convert an MOP into a number of single-objective optimization subproblems; (3) all weight vectors are divided into a few groups based on K-means clustering: some superior individuals corresponding to different weight vector groups are selected by using a selection mechanism; (4) local search based on moving critical operations is applied on selected individuals.
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