Solution methods for vehicle-based inventory routing problems

Yachao Dong, Christos T. Maravelias, Jose M. Pinto, Arul Sundaramoorthy

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

A novel method for solving vehicle-based inventory routing problems (IRPs) under realistic constraints is presented. First, we propose a preprocessing algorithm that reduces the problem size by eliminating customers and network arcs that are irrelevant for the current horizon. Second, we develop a decomposition method that divides the problem into two subproblems. The upper level subproblem considers a simplified vehicle routing problem to minimize the distribution cost while satisfying minimum demands, which are calculated based on consumption rate, initial inventory and safety stock. In the lower level, a detailed schedule with drivers is acquired using a continuous-time MILP model, by adopting the routes selected from the upper level. Finally, an iterative approach based on the upper and lower levels is presented, including the addition of different types of integer cuts and parameter updates. Different options of implementing this iterative approach are discussed, and computational results are presented.

Original languageEnglish (US)
Pages (from-to)259-278
Number of pages20
JournalComputers and Chemical Engineering
Volume101
DOIs
StatePublished - 2017
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Chemical Engineering(all)
  • Computer Science Applications

Keywords

  • Decomposition method
  • Mixed-integer programming
  • Network reduction algorithm
  • Vendor managed inventory

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