Abstract
A discrete particle swarm optimization algorithm with adaptive inertia weight (DPSO-AIW) is proposed to solve the multiobjective Flexible Job-shop Scheduling Problem. The algorithm uses a two-layer coding structure to encode the chromosomes, namely operation sequence (OS) and machine assignment (MA). The initial population combined random selection of OS and the global selection based on operation (GSO) of MA. In order to obtain the Pareto optimal solution, non-dominated fronts are obtained by rapid non-dominated sorting. In the evolution process, the discrete particle swarm optimization algorithm is used to directly solve the values of the next generation chromosomes in the discrete domain, and the population diversity is enhanced by adaptively adjusting the variation of the inertia weight $\omega $ , and the Pareto optimal solution obtained in the process is stored in the Pareto optimal solution set (POS). Finally, numerical simulation based on two sets of international standard instances and comparisons with some existing algorithms are carried out. The comparative results demonstrate the effectiveness and practicability of the proposed DPSO-AIW in solving the multiobjective Flexible Job-shop Scheduling Problem.
Highlights
Flexible Job-shop Scheduling Problem (FJSP) is an extension of the Job-shop Scheduling Problem (JSP)
FJSP allows multiple operations of different jobs to be processed on different machines, which changes the uniqueness of the equipment, and selecting the processing machine according to the load conditions of such resources as machines, etc., and the flexibility of processing is enhanced and more in line with the actual enterprise
Based on the above literature research, this paper proposes a method to combine the random selection of operation sequence (OS) and the global selection based on operation (GSO) of machine assignment (MA)
Summary
Flexible Job-shop Scheduling Problem (FJSP) is an extension of the Job-shop Scheduling Problem (JSP). Liu et al [6] proposed a hybrid PSO algorithm with Pareto archives set for the FJSP problem with three targets; Chen et al.[7] designed an extended process coding and automatic scheduling decoding mechanism. [8] proposed a hybrid particle swarm optimization algorithm to study the multiobjective FJSP based on Pareto-dominance. The method of ISX is as follow: Divide all the chromosomes involved in the crossover into n 2 groups, and perform single-point crossover for the two parent chromosomes in each group: randomly select one crossover point, and exchange the machines assigned by the operations included in the two parents before the crossover point, which ensures that the chromosomes obtained after the crossover are all feasible schedulings
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