Integrated scheduling and self-reconfiguration for assembly job shop in knowledgeable manufacturing

Abstract

The problems of integrated assembly job shop (AJS) scheduling and self-reconfiguration in knowledgeable manufacturing are studied with the objective of minimising the weighted sum of completion cost of products, the earliness penalty of operations and the training cost of workers. In AJS, each workstation consists of a certain number of teams of workers. A product is assumed to have a tree structure consisting of components and subassemblies. The assembly of components, subassemblies and final products are optimised with the capacity of workstations simultaneously. A heuristic algorithm is developed to solve the problem. Dominance relations of operations are derived and applied in the development of the heuristic. A backward insertion search strategy is designed to locally optimise the operation sequence. Once the optimal schedule is acquired, the teams are reconfigured by transferring them from workstations of lower utilisation to those of higher utilisation. Effectiveness of the proposed algorithm is tested by a number of numerical experiments. The results show that the proposed algorithm promises lower total cost and desirable simultaneous self-reconfiguration in accordance with scheduling.

Keywords:

• knowledgeable manufacturing
• assembly job shop scheduling
• earliness penalty
• self-reconfiguration

Acknowledgements

We thank Editor-in-Chief & Professor Dolgui, the anonymous referees and Professor Li Lu for their valuable comments and suggestions.

Additional information

Funding

Funding. This research is supported by a key programme of the National Natural Science Foundation of China [grant number 60934008].