TY - GEN
T1 - Solution algorithm for the vehicle routing problem with stochastic demands
AU - Omori, Ryota
AU - Shiina, Takayuki
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/5
Y1 - 2020/12/5
N2 - The main purpose of the Vehicle Routing Problem (VRP) is to minimize the total cost of delivery. When customer demand is not considered, the total cost is only travel cost. This is obtained by solving the Traveling Salesman Problem (TSP). However, when customers' demands are stochastic (Vehicle Routing Problem with Stochastic Demands, SVRP), the vehicle may thus be unable to load the customer's demand, even if the expected demand along the route does not exceed the vehicle capacity. This situation is referred to as a failure. Therefore, in SVRP, it is necessary to minimize the sum of the travel cost obtained by solving the TSP and the additional cost incurred when delivering along the route. In previous studies, it was common to use the lower bound instead of exactly calculating the value of the additional cost. In this study, we focused on calculating additional costs exactly without using lower bounds. The method used here considers additional costs of multiple edges: edges that pass through the depot and edges that do not pass through the depot, for all edges connecting the depot to the customer. The method provides a new solution to find the exact value.
AB - The main purpose of the Vehicle Routing Problem (VRP) is to minimize the total cost of delivery. When customer demand is not considered, the total cost is only travel cost. This is obtained by solving the Traveling Salesman Problem (TSP). However, when customers' demands are stochastic (Vehicle Routing Problem with Stochastic Demands, SVRP), the vehicle may thus be unable to load the customer's demand, even if the expected demand along the route does not exceed the vehicle capacity. This situation is referred to as a failure. Therefore, in SVRP, it is necessary to minimize the sum of the travel cost obtained by solving the TSP and the additional cost incurred when delivering along the route. In previous studies, it was common to use the lower bound instead of exactly calculating the value of the additional cost. In this study, we focused on calculating additional costs exactly without using lower bounds. The method used here considers additional costs of multiple edges: edges that pass through the depot and edges that do not pass through the depot, for all edges connecting the depot to the customer. The method provides a new solution to find the exact value.
KW - Optimization
KW - Stochastic programming
KW - Vehicle routing
UR - http://www.scopus.com/inward/record.url?scp=85100372243&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85100372243&partnerID=8YFLogxK
U2 - 10.1109/SCISISIS50064.2020.9322773
DO - 10.1109/SCISISIS50064.2020.9322773
M3 - Conference contribution
AN - SCOPUS:85100372243
T3 - 2020 Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2020
BT - 2020 Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2020
Y2 - 5 December 2020 through 8 December 2020
ER -