An Improved Genetic Algorithm For Just-In-Time Job-Shop Scheduling Problem

Abstract

This paper studies a just-in-time job-shop scheduling problem (JITJSSP) in which each operation has an earliness cost or a tardiness cost if it is completed before or after its due date and the objective function is to minimize the total earliness and tardiness costs of all operations. In order to solve this problem, an improved genetic algorithm (IGA) is introduced in this paper. IGA utilizes an operation-based scheme to represent schedules as chromosomes. Then, each chromosome is processed through a three-stage mechanism. Firstly, the semi-active decoding process is employed to expand the search space of solutions and guarantee comprehensive solutions. Secondly, the greedy insertion mechanism for tardy operations is executed to move the tardy operations left to the appropriate idle time to reduce the tardiness costs. Finally, the greedy insertion mechanism for early operations is proposed to shift the early operations right to the suitable idle time to decrease the earliness costs. After the maximum number of generations is reached, IGA continues with selection, crossover and mutation. The experimental results finally show that most of solutions on the benchmarks are improved by our algorithm.

Minimizing the total weighted earliness and tardiness for a sequence of operations in job shops

“…The mathematical programming model is used to determine the optimal schedule for a given sequence of operations using a commercial optimization solver. Yang et al [31] implemented an improved genetic algorithm that utilizes an operation-based scheme to represent the chromosomes. Each chromosome is decoded to generate the schedules using a three-stage decoding mechanism which initially generates a semi-active schedule and then improves the schedule by reducing earliness cost using greedy insertion mechanisms.…”