Service and transfer selection for freights in a synchromodal network

Arturo Pérez Rivera, Martijn Mes

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademic

Abstract

We study the problem of selecting services and transfers in a synchromodal network to transport freights with different characteristics, over a multi-period horizon. The evolution of the network over time is determined by the decisions made, the schedule of the services, and the new freights that arrive each period. Although freights become known gradually over time, the planner has probabilistic knowledge about their arrival. Using this knowledge, the planner balances current and future costs at each period, with the objective of minimizing the expected costs over the entire horizon. To model this stochastic finite horizon optimization problem, we propose a Markov Decision Process (MDP) model. To overcome the computational complexity of solving the MDP, we propose a heuristic approach based on approximate dynamic programming. Using different problem settings, we show that our look-ahead approach has significant benefits compared to a benchmark heuristic.
Original languageEnglish
Title of host publicationComputational Logistics
Subtitle of host publication7th International Conference, ICCL 2016, Lisbon, Portugal, September 7-9, 2016, Proceedings
EditorsAna Paias, Mario Ruthmair, Stefan Voß
Place of PublicationCham
PublisherSpringer
Pages227-242
ISBN (Electronic)978-3-319-44896-1
ISBN (Print)978-3-319-44896-1
DOIs
Publication statusPublished - 7 Sep 2016
Event7th International Conference on Computational Logistics, ICCL 2016 - Lisbon, Portugal
Duration: 7 Sep 20169 Sep 2016
Conference number: 7

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume9855
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Conference on Computational Logistics, ICCL 2016
Abbreviated titleICCL
CountryPortugal
CityLisbon
Period7/09/169/09/16

Keywords

  • METIS-318326
  • IR-101808

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