Abstract
In this article, we investigate a novel dual-resource constrained flexible job shop scheduling problem with consideration of worker’s learning ability and develop an efficient hybrid genetic algorithm to solve the problem. To begin with, a comprehensive mathematical model with the objective of minimizing the makespan is formulated. Then, a hybrid algorithm which hybridizes genetic algorithm and variable neighborhood search is developed. In the proposed algorithm, a three-dimensional chromosome coding scheme is employed to represent the individuals, a mixed population initialization method is designed for yielding the initial population, and advanced crossover and mutation operators are proposed according to the problem characteristic. Moreover, variable neighborhood search is integrated to improve the local search ability. Finally, to evaluate the effectiveness of the proposed algorithm, computational experiments are performed. The results demonstrate that the proposed algorithm can solve the problem effectively and efficiently.
Highlights
The flexible job shop scheduling problem (FJSP)[1] is a classic abstraction of actual production process and has been well known as one of the most important NPhard combinatorial optimization problems
We propose an encoding mechanism in which each individual consists of three chromosomes: operation sequence chromosome (OSC) represents the processing sequence of operations; machine assignment chromosome (MAC) represents the allocation of machines; worker assignment chromosome (WAC) represents the assignment of workers
Check all genes in the MACs, if the machine is illegal, select a new one from the available machine set with the shortest basic processing time, and select a new worker if the current worker is illegal
Summary
The flexible job shop scheduling problem (FJSP)[1] is a classic abstraction of actual production process and has been well known as one of the most important NPhard combinatorial optimization problems. A new mathematical model considering worker’s learning ability in DRCFJSP is established This model considers operation-sequence-constraint as well as machine and worker resources constraints and considers the improvement of worker’s efficiency. Check all genes in the MACs, if the machine is illegal (this is possible because the OSC is unchanged and a different operation may have a different alternative machine set), select a new one from the available machine set with the shortest basic processing time, and select a new worker if the current worker is illegal. The first part deals with the mutation of OSCs and the implementation detail is as follows: select two genes at different positions randomly in the parent OSC and exchange the selected genes as shown, and adjust the MAC and WAC correspondingly to keep the allocations of machine and worker unchanged. Exchange, Replace, and Change are used in VNS: 1. Exchange is to make an exchange on the OSC: Select two genes with different values randomly in the OSC and exchange them, and reallocate the machines and workers to guarantee that the new solution is feasible
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