TY - JOUR
T1 - Multi-objective robust optimisation model for MDVRPLS in refined oil distribution
AU - Xu, Xiaofeng
AU - Lin, Ziru
AU - Li, Xiang
AU - Shang, Changjing
AU - Shen, Qiang
N1 - 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 Seˆr Cymru II COFUND Fellowship, UK.
PY - 2021/3/30
Y1 - 2021/3/30
N2 - 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.
AB - 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.
KW - MDVRPLS
KW - multi-objective optimisation
KW - particle swarm optimisation algorithm
KW - refined oil distribution
KW - robust optimisation
UR - http://www.scopus.com/inward/record.url?scp=85103368494&partnerID=8YFLogxK
U2 - 10.1080/00207543.2021.1887534
DO - 10.1080/00207543.2021.1887534
M3 - Article
AN - SCOPUS:85103368494
SN - 0020-7543
VL - 60
SP - 6772
EP - 6792
JO - International Journal of Production Research
JF - International Journal of Production Research
IS - 22
ER -