Papers by Author: Guo Hui Zhang

Abstract: The multi objective job shop scheduling problem is well known as one of the most complex optimization problems due to its very large search space and many constraint between machines and jobs. In this paper, an evolutionary approach of the memetic algorithm is used to solve the multi objective job shop scheduling problems. Memetic algorithm is a hybrid evolutionary algorithm that combines the global search strategy and local search strategy. The objectives of minimizing makespan and mean flow time are considered while satisfying a number of hard constraints. The computational results demonstrate the proposed MA is significantly superior to the other reported approaches in the literature.

Abstract: Flexible job shop scheduling problem (FJSP) is a well known NP-hard combinatorial optimization problem due to its very large search space and many constraint between jobs and machines. Evolutionary algorithms are the most widely used techniques in solving FJSP. Memetic algorithm is a hybrid evolutionary algorithm that combines the local search strategy and global search strategy. In this paper, an effective memetic algorithm is proposed to solve the FJSP. In the proposed algorithm, variable neighborhood search is adopted as local search algorithm. The neighborhood functions is generated by exchanging and inserting the key operations which belong to the critical path. The optimization objective is to minimize makespan. The experimental results obtained from proposed algorithm show that the proposed algorithm is very efficient and effective for all tested problems.

Abstract: Flexible job shop scheduling problem (FJSP) is an important extension of the classical job shop scheduling problem, where the same operation could be processed on more than one machine. It is quite difficult to achieve optimal or near-optimal solutions with single traditional optimization approach because the multi objective FJSP has the high computational complexity. An novel hybrid algorithm combined variable neighborhood search algorithm with genetic algorithm is proposed to solve the multi objective FJSP in this paper. An external memory is adopted to save and update the non-dominated solutions during the optimization process. To evaluate the performance of the proposed hybrid algorithm, benchmark problems are solved. Computational results show that the proposed algorithm is efficient and effective approach for the multi objective FJSP.