Genetic algorithm for Job Shop Scheduling with Earliness and Tardiness penalties

Abstract

The just-in-time production philosophy has led to a growing interest in scheduling problems considering both Earliness and Tardiness (E/T) penalties. An EIT schedule model, with the consideration of different penalties for tardiness, earliness and flowtime of jobs, is introduced to minimize total production costs. Based on classical genetic algorithm, a new Enhanced Genetic Algorithm (EGA) is designed to solve tardy and early problems. In order to avoid infeasible decoding solutions and reduce simultaneously tardiness costs, the chromosome-repair operator based on critical paths is put forward to solve the problem that some jobs violate the due date constraints. The objective-value revision operator based on chromosome rescheduling is introduced to reduce in-process and final goods inventory costs by delaying job release date. The proposed approaches have been tested on a wide range of 80 scheduling problems and satisfactory results have been obtained.