TY - GEN
T1 - Multi-product Inventory Routing Problem Considering Demand Uncertainty
AU - Kawamura, T.
AU - Sato, T.
AU - Shiina, Takayuki
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The inventory routing problem simultaneously considers both the inventory problem and the delivery problem; it determines the amount of delivery of inventory and the delivery route such that the total cost is minimized. In this study, we use stochastic programming to consider a multi-product inventory routing problem that considers the demand variation throughout multiple periods. Determining the delivery route for each period is difficult. Therefore, we propose a model that fixes the delivery route throughout the planning period and compare the calculation results to prove its practicality. In addition, the problem in this study is an integer programming problem, and solving a large-scale problem using the direct method would be time-consuming. Therefore, we apply the accelerated Benders decomposition method, which combines two cuts: the optimality cut with the solution of the linear relaxation problem, and the Pareto-optimal cut. We demonstrate its effectiveness through numerical experiments.
AB - The inventory routing problem simultaneously considers both the inventory problem and the delivery problem; it determines the amount of delivery of inventory and the delivery route such that the total cost is minimized. In this study, we use stochastic programming to consider a multi-product inventory routing problem that considers the demand variation throughout multiple periods. Determining the delivery route for each period is difficult. Therefore, we propose a model that fixes the delivery route throughout the planning period and compare the calculation results to prove its practicality. In addition, the problem in this study is an integer programming problem, and solving a large-scale problem using the direct method would be time-consuming. Therefore, we apply the accelerated Benders decomposition method, which combines two cuts: the optimality cut with the solution of the linear relaxation problem, and the Pareto-optimal cut. We demonstrate its effectiveness through numerical experiments.
KW - Benders decomposition method
KW - Optimization
KW - Stochastic programming
UR - http://www.scopus.com/inward/record.url?scp=85139557439&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85139557439&partnerID=8YFLogxK
U2 - 10.1109/IIAIAAI55812.2022.00123
DO - 10.1109/IIAIAAI55812.2022.00123
M3 - Conference contribution
AN - SCOPUS:85139557439
T3 - Proceedings - 2022 12th International Congress on Advanced Applied Informatics, IIAI-AAI 2022
SP - 621
EP - 626
BT - Proceedings - 2022 12th International Congress on Advanced Applied Informatics, IIAI-AAI 2022
A2 - Matsuo, Tokuro
A2 - Takamatsu, Kunihiko
A2 - Ono, Yuichi
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Congress on Advanced Applied Informatics, IIAI-AAI 2022
Y2 - 2 July 2022 through 7 July 2022
ER -