Robust optimization for integrating preventative maintenance with coal production under demand uncertainty

Abstract

We consider a coal mine producing a catalog of products through multiple pieces of equipment with variable production rates over a multi-period horizon, where each product faces random demand in each period. Each piece of equipment requires Preventative Maintenance (PM) with a given duration. We study a joint PM and production problem that adaptively determines the PM starting time and the production rates for the equipment to minimize the expected total cost. We formulate a multi-period stochastic optimization model that is challenging to solve due to the complexity of adjustable binary decisions. This motivates us to propose a two-phase approach based on robust optimization to solve the problem. Phase 1 determines the binary PM decisions using a target-oriented robust optimization approach. Fixing the PM decisions, Phase 2 adaptively determines the production rates using a linear decision rule. Numerical experiments suggest that our approach outperforms some existing approaches that handle adjustable binary decisions, and performs very close to the expected value given perfect information for varying problem instances. A case study using real data from a major coal mine in China suggests that implementing our approach can potentially yield cost savings in the long run over the status quo policy.

Keywords:

• Scheduling
• preventative maintenance
• coal production
• robust optimization
• demand uncertainty

Acknowledgments

The authors thank the Department Editor, the Associate Editor, and the three anonymous referees for their thoughtful comments that have substantially improved the paper.

Data Availability Statement

Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (No. 72101216, 72101230, 72071164, 71801181) and the National Key R&D Program of China (No. 2018YFB1601401).

Notes on contributors

Song Jiu

Song Jiu is an assistant professor in the School of Economics and Management at Southwest Jiaotong University. His research interests lie in production planning and scheduling, supply chain management, e-commerce logistics, and robust optimization. He has published research papers in peer-refereed journals such as Manufacturing & Service Operations Management, Transportation Research Part E, International Journal of Production Research, and Journal of Heuristics.

Qiang Guo

Qiang Guo is a professor in the School of Economics and Management at Southwest Jiaotong University. His research interests include supply chain management, project management, service quality management, and energy economics. His research appeared in journals such as Energy Economics, Discrete and Continuous Dynamical Systems, and International Journal of Computational Intelligence System, etc.

Chao Liang

Chao Liang is an assistant professor in the School of Economics and Management at Southwest Jiaotong University. His research interests include time series forecasting, empirical finance, machine learning, and energy economics. He is currently an associate editor of Evaluation Review. He has published in journals such as International Journal of Forecasting, Annals of Operations Research, International Review of Financial Analysis, Quantitative Finance, Energy Economics, Journal of Forecasting, Technological Forecasting and Social Change, Pacific-Basin Finance Journal, Economic Modelling, Management Decision and Finance Research Letters, etc.