Adaptive and Hybrid Genetic Algorithms for Production Scheduling Problems in Manufacturing


EPSRC Overseas Travel Grant GR/S79718/01, GPB 6,700, and Waseda University, Japan, GBP 1,750


November 2003 - January 2004


This project is supported by EPSRC Overseas Travel Grants for the investigator to carry out research cooperation with Prof. Mitsuo Gen at Graduate School of Information, Production and Systems, Waseda University, Japan for three months while he is on sabbatical. Prof. Gen's Artificial Intelligence Lab is internationally recognized in the areas of applying intelligent methods such as genetic algorithms and artificial neural networks for solving practical optimization problems in industry. This project will combine the Intelligent Optimisation expertise of the investigator and Prof. Gen's Artificial Intelligence Lab into an interesting application research area: Adaptive and Hybrid Genetic Algorithms for Production Scheduling Problems in Manufacturing. The production scheduling problem concerns the allocation of limited resources (or machines) to perform a collection of tasks (or jobs) over time in order to optimise one or more objectives, such as minimizing the completion time of the last job. It plays an important role in manufacturing systems. During the 3 months' visit, based on the investigator's recent work in adaptive genetic algorithms, we will develop new adaptive genetic algorithms specific for static production scheduling problems in manufacturing systems. In addition, we will also investigate new hybrid intelligent methods that combine genetic algorithms with artificial neural networks for solving dynamic production scheduling problems under manufacturing environments. In this project we will mainly concentrate on the job-shop scheduling problem, one of the most complicated and typical production scheduling problems. However the results can be easily extended to solve other production scheduling problems.


S Yang.


Prof. Mitsuo Gen (Waseda University, Japan)

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