Profit-oriented distributionally robust chance constrained flowshop scheduling considering credit risk

Abstract

Customer credit risk or payment probability, influenced by factors such as financial conditions and bank policies, has hindered fast Asia-Pacific economic growth. Besides, the working time is usually limited due to regulations and limited resources. Driven by profit, some jobs may be rejected on tactical level and the accepted jobs are scheduled on operational level, respecting the allowed working time. This paper studies a stochastic flowshop scheduling problem, assuming that only the mean and covariance matrix of uncertain payment probabilities and processing times are known. The objective is to maximise the profit level, i.e. the probability of the profit no less than the planned one, while controlling the risk of surpassing the limited working time. A new distributionally robust chance constrained model is proposed. The sample average approximation (SAA) method, the robust SAA method and a hierarchical approach, based on an approximated mixed integer second-order conic program, are developed. Numerical experiments show that the hierarchical approach is more efficient. Moreover, some managerial insights are drawn.

Keywords:

• scheduling
• stochastic
• flowshop
• robust
• credit risk

Acknowledgments

The authors would like to thank the anonymous referees for their constructive comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Science Foundation of China (NSFC) under Grants 71531011, 71771048, 71432007, 71832001, 71871159 and 71571134. This work was also supported by the Fundamental Research Funds for the Central Universities.