An Adaptive MO-HGA for Resource-Constrained Transport Task Scheduling

Authors:
Jian Wang;Hongwei Wang
Affiliations:
Systems Engineering Institute, Huazhong University of Science and Technology, Wuhan Hubei, China 430074 and Key Laboratory of Image Processing and Intelligent Control, Wuhan Hubei, China 430074;Systems Engineering Institute, Huazhong University of Science and Technology, Wuhan Hubei, China 430074 and Key Laboratory of Image Processing and Intelligent Control, Wuhan Hubei, China 430074
Venue:
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Year:
2009

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Abstract

This paper proposes an adaptive multi-objective hybrid genetic algorithm (MO-HGA) based on the serial scheduling method to solve the resource-constrained transport task scheduling problem (RCTTSP) with two optimal objectives. The proposed algorithm uses the serial scheduling method to initialize the population and evaluate the individual, and use the weighted sum method and the rank-based fitness assignment method to assign the individual fitness. Furthermore, an adaptive GA parameters tuning method based on fuzzy logic controller is implemented to improve the performance of the algorithm. Firstly, this paper describes the multi-objective RCTTSP and presents the principle of the adaptive MO-HGA, and then develops the algorithm to implement several experimental cases with different problem sizes, lastly the effectiveness and efficiency of the algorithm are compared. The numerical result indicates that the proposed adaptive MO-HGA can resolve the proposed multi-objective resource-constrained transport task scheduling problem efficiently.