A Study on Sharing Logistics Network Design Considering Demand Uncertainty - Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems Access content directly
Conference Papers Year : 2021

A Study on Sharing Logistics Network Design Considering Demand Uncertainty

Asumi Ito
  • Function : Author
  • PersonId : 1235618
Toshiya Kaihara
  • Function : Author
Daisuke Kokuryo
  • Function : Author
Nobutada Fujii
  • Function : Author

Abstract

The evolution of e-retailing is driving a rise in logistics costs and risk of late delivery. Collaborative logistics has become the key to help businesses eliminate inefficiencies, improve responsiveness to market changes, and reduce overall supply chain costs by adjusting transportation capacity efficiently. In this study, we propose a stochastic mixed integer linear programming model that incorporates shipper’s transportation operations via truck sharing service. The model supports strategic network design decisions in uncertain market environments by optimizing the number of trucks under uncertain demand. Through several case studies on a small-scale truck sharing network, we show the influence of demand uncertainty on the network performance in terms of the on-time delivery ratio and the gross profit margin ratio. We also show the influence of the sharing platform features such as the transaction price and the number of available trucks, on shippers as well as a platformer in terms of the turnover of trucks.
Fichier principal
Vignette du fichier
520759_1_En_71_Chapter.pdf (427.51 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-04022120 , version 1 (09-03-2023)

Licence

Attribution

Identifiers

Cite

Asumi Ito, Toshiya Kaihara, Daisuke Kokuryo, Nobutada Fujii. A Study on Sharing Logistics Network Design Considering Demand Uncertainty. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2021, Nantes, France. pp.655-662, ⟨10.1007/978-3-030-85906-0_71⟩. ⟨hal-04022120⟩
10 View
0 Download

Altmetric

Share

Gmail Facebook X LinkedIn More