A genetic-simulated annealing algorithm for stochastic seru scheduling problem with deterioration and learning effect

ABSTRACT

As a flexible and effective production mode, seru production has been adopted successfully in electronic industry. In practice, the processing time may be affected by many stochastic factors, such as worker absence, shortage of resources, and so on. This paper focuses on seru scheduling problems with stochastic processing time, and considers the influence of dynamic resource allocation, job deterioration, learning effecteffect, and setup time simultaneously to minimize the makespan. A genetic-simulated annealing algorithm is proposed, in which a simulated annealing procedure is constructed to re-optimize the optimal individual obtained by geneticthe genetic algorithm. Experiment results validate the effectiveness of proposedthe proposed seru scheduling model and genetic-simulated annealing algorithm for solving large-scale cases, and indicate that the stochastic processing time has a great influence on the makespan whichmakespan that can help production manager to makeproduce more consistent results according to the actual situation.

Graphical abstract

KEYWORDS:

• Seru scheduling
• genetic-simulated annealing algorithm
• learning effect
• resource allocation
• stochastic processing time

Acknowledgments

This research is sponsored by the Fundamental Research Funds for the Central Universities (No. 30922011406), the System Science and Enterprise Development Research Center (Grant No. Xq22B06). We would like to give our great appreciation to all the reviewers and editors who contributed to this research.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Fundamental Research Funds for the Central Universities [30922011406]; System Science and Enterprise Development Research Center [Xq22B06].