TY - JOUR
T1 - Bi-objective inventory routing problem with uncertain demand
T2 - a data-driven robust optimisation approach
AU - Feng, Yuqiang
AU - Che, Ada
AU - Lei, Jieyu
N1 - Publisher Copyright:
© 2024 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2024
Y1 - 2024
N2 - This study addresses the single-period inventory routing problem (SIRP) with uncertain demands. We employ the support vector clustering technique to construct a data-driven uncertainty set to characterise demands uncertainty rather than imposing stochastic or fuzzy distribution. We propose a comprehensive expression to granularly calculate the inventory cost of products. Besides minimising the total cost from economics, we also consider the objective of minimising the total deviation level of delivery quantities to match supplies and uncertain demands and further to enhance service quality. We develop a data-driven robust bi-objective SIRP (RBSIRP) model that seeks a trade-off between these two perspectives. We apply the dual theory to obtain equivalent tractable forms of robust counterparts and employ the augmented ε-constraint approach to handle the developed objectives. The experimental results show the practical implications of our model and method. The RBSIRP model based on the constructed data-driven uncertainty set can reduce the conservatism of the delivery solution compared with the classical Budgeted and Box+Ball uncertainty sets while ensuring robustness. The trade-off delivery solution provided by the RBSIRP model is better than the one generated by the model minimising only the total cost.
AB - This study addresses the single-period inventory routing problem (SIRP) with uncertain demands. We employ the support vector clustering technique to construct a data-driven uncertainty set to characterise demands uncertainty rather than imposing stochastic or fuzzy distribution. We propose a comprehensive expression to granularly calculate the inventory cost of products. Besides minimising the total cost from economics, we also consider the objective of minimising the total deviation level of delivery quantities to match supplies and uncertain demands and further to enhance service quality. We develop a data-driven robust bi-objective SIRP (RBSIRP) model that seeks a trade-off between these two perspectives. We apply the dual theory to obtain equivalent tractable forms of robust counterparts and employ the augmented ε-constraint approach to handle the developed objectives. The experimental results show the practical implications of our model and method. The RBSIRP model based on the constructed data-driven uncertainty set can reduce the conservatism of the delivery solution compared with the classical Budgeted and Box+Ball uncertainty sets while ensuring robustness. The trade-off delivery solution provided by the RBSIRP model is better than the one generated by the model minimising only the total cost.
KW - bi-objective optimisation
KW - data-driven robust optimisation
KW - Inventory routing problem
KW - matching supplies and uncertain demands
KW - support vector clustering
UR - http://www.scopus.com/inward/record.url?scp=85211110134&partnerID=8YFLogxK
U2 - 10.1080/00207543.2024.2432464
DO - 10.1080/00207543.2024.2432464
M3 - 文章
AN - SCOPUS:85211110134
SN - 0020-7543
JO - International Journal of Production Research
JF - International Journal of Production Research
ER -