Multi-objective robust optimisation model for MDVRPLS in refined oil distribution

ABSTRACT

At depots with refined oil shortage, arranging a reasonable distribution scheme with limited supply affects operation costs, demand satisfaction rate of gasoline stations (hereafter, ‘station satisfaction’), and overtime penalty. This study considers the refined oil distribution problem with shortages using a multi-objective optimisation approach from the perspective of decision makers of oil marketing companies. The modelling and solving process involves (i) formulation of a crisp multi-depot vehicle routing model with limited supply (MDVRPLS) which considers station priority and soft time windows, (ii) development of a robust optimisation model (ROM) to manage uncertainty in demand, and (iii) the proposal of a multi-objective particle swarm optimisation (MOPSO)algorithm. Results of numerical experiments show that (i) the crisp model can better balance operation costs, station satisfaction, and overtime penalty, which produces 3.33% and 4.60% increase in station satisfaction at an increased unit cost and overtime penalty respectively; (ii) ROM successfully addresses uncertainty in demand compared to the crisp model, which requires an additional 8.81% in cost and 12.85% in penalty; and (iii) the MOPSO manages these MDVRPLS models more effectively than other heuristic algorithms. Therefore, applying ROM of refined oil supply shortage to the management significantly improves the efficiency and resists the disturbance caused by external uncertainties, providing scope for efficient distribution of scarce resources.

Keywords:

• MDVRPLS
• refined oil distribution
• robust optimisation
• multi-objective optimisation
• particle swarm optimisation algorithm

Acknowledgements

This research was partially supported by the National Natural Science Foundation of China (under Grant Nos. 71871222, 71722007, and 71931001), partially by China University of Petroleum Funds for ‘Philosophy and Social Sciences Young Scholars Support Project’ (under Grant No. 20CX05002B) and partially by a S$\hat{e}$r Cymru II COFUND Fellowship, UK.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 http://www.china.com.cn/chinese/difang/941592.htm.

2 https://www.sohu.com/a/288026675_115239.

3 https://finance.huanqiu.com/article/9CaKrnJHHRa.

4 http://news.sohu.com/20060219/n241899654.shtml.

5 https://ditu.amap.com/.

Additional information

Funding

This research was supported by the National Natural Science Foundation of China [under grant numbers 71871222, 71722007, and 71931001], the China University of Petroleum Funds for ‘Philosophy and Social Sciences Young Scholars Support Project’ [under grant number 20CX05002B] the key program of NSFC-FRQSC Joint Project (NSFC No. 72061127002 and FRQSC No. 295837).and partially by a S$\hat{e}$r Cymru II COFUND Fellowship, UK.

Notes on contributors

Xiaofeng Xu

Xiaofeng Xu received the Ph.D. degree in Management Science and Engineering from Harbin Engineering University, Harbin, China, in 2009.He is currently an associate professor with School of Economics and Management, China University of Petroleum. His research interests include operations management, smart logistics system and their applications. He has authored more than 30 articles on journals including IEEE Transactions on Fuzzy Systems, Applied Soft Computing, Journal of the Franklin Institute, Resources Policy and so on.

Ziru Lin

Ziru Lin received the bachelor's degree in management from China University of Petroleum, Qingdao, China, in 2018. She is currently working toward the master degree at School of Economics and Management, China University of Petroleum. Her research interests include optimization theory, smart logistics system and their applications. She has authored articles on Chinese Journal of Management Science, Annals of Operations Research and Operations Research and Management Science.

Xiang Li

Xiang Li received the Ph.D. degree from Tsinghua University, Beijing, China, in 2008.He is currently a professor with School of Economics and Management Science, Beijing University of Chemical Technology. His research interests include intelligent transportation system, optimization under uncertainty, big data analysis and so on. He has authored two books and more than 100 articles on international journals including Transportation Research Part B, Transportation Research Part C, Omega, Information Sciences, International Journal of Production Economics, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, IEEE Transactions on Intelligent Transportation Systems, European Journal of Operational Research, Computer and Industrial Engineering and so on. He served as the co-editor-in-chief of Journal of Data, Information and Management, and associate editor or editorial board member of Information Sciences, Transportmetrica B, International Journal of General Systems, Journal of Ambient Intelligence and Humanized Computing, and so on.

Changjing Shang

Changjing Shang received the Ph.D. degree in computing and electrical engineering from Heriot-Watt University, Edinburgh, U.K., in 1995. She is currently a University Research Fellow with the Department of Computer Science, Faculty of Business and Physical Sciences, Aberystwyth University, Aberystwyth, U.K. Prior to joining Aberystwyth, she was with Heriot-Watt, Loughborough and Glasgow Universities. She has published extensively, and supervised more than ten Ph.Ds/PDRAs in the areas of pattern recognition, data mining and analysis, space robotics, and image modelling and classification.

Qiang Shen

Qiang Shen received the Ph.D. degree in computing and electrical engineering from Heriot-Watt University, Edinburgh, U.K., in 1990 and the D.Sc. degree in computational intelligence from Aberystwyth University, Aberystwyth, U.K., in 2013. He is appointed as the Chair of Computer Science and the Pro Vice-Chancellor for Faculty of Business and Physical Sciences, Aberystwyth University, Aberystwyth, U.K. He has authored two research mono graphs and approximately 400 peer-reviewed papers. Dr. Shen was a recipient of the Outstanding Transactions Paper Award from the IEEE.