A sequence learning harmony search algorithm for the flexible process planning problem

Abstract

Flexible process planning involves selecting and sequencing the requisite operations, and assigning the right machine, tool and access direction to each selected operation for minimising the production cost or the completion time. It is one of the challenging combinatorial optimisation problems due to sequencing flexibility, processing flexibility and operation flexibility. A sequence learning harmony search algorithm is accordingly proposed. Distinctively, the well-designed algorithm searches for the optimal process plan by intelligently finding the proper immediate successor for each selected operation in turn rather than resorting to the common shifting and swapping operators in sequencing. The innovative algorithm does not also require extra efforts to plot the operational precedence graph or the AND/OR-network graph. The experimental results indicate that the proposed algorithm significantly outperforms other heuristics in terms of the quality of solution found and the convergence rate of the algorithm. For the large-scale complicated instances, the proposed algorithm establishes a challenging flag.

Keywords:

• Flexible manufacturing systems
• computer-aided manufacturing
• flexible process planning
• harmony search
• sequence learning

Acknowledgements

The authors would like to thank the editors and anonymous reviewers for their valuable comments. This work was supported by the National Natural Science Foundation of China under grant no. 71871004.

Disclosure statement

No potential conflict of interest was reported by the author.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant no. 71871004].

Notes on contributors

Kaiping Luo

He is currently an associate professor in the Department of Management Science and Engineering, Beihang University, Beijing, China. His research interests include operations research, optimisation and computational intelligence. He has published more than 30 articles in various prestigious journals.