Dynamic Snow Plow Fleet Management under Uncertain Demand and Service Disruption

Leila Hajibabai, Yanfeng Ouyang

Research output: Contribution to journalArticlepeer-review

Abstract

It is sometimes challenging to plan winter maintenance operations in advance because snow storms are stochastic with respect to, e.g., start time, duration, impact area, and severity. In addition, maintenance trucks may not be readily available at all times due to stochastic service disruptions. A stochastic dynamic fleet management model is developed to assign available trucks to cover uncertain snow plowing demand. The objective is to simultaneously minimize the cost for truck deadheading and repositioning, as well as to maximize the benefits (i.e., level of service) of plowing. The problem is formulated into a dynamic programming model and solved using an approximate dynamic programming algorithm. Piecewise linear functional approximations are used to estimate the value function of system states (i.e., snow plow trucks location over time). We apply our model and solution approach to a snow plow operation scenario for Lake County, Illinois. Numerical results show that the proposed algorithm can solve the problem effectively and outperforms a rolling-horizon heuristic solution.

Original languageEnglish (US)
Article number7412707
Pages (from-to)2574-2582
Number of pages9
JournalIEEE Transactions on Intelligent Transportation Systems
Volume17
Issue number9
DOIs
StatePublished - Sep 2016

Keywords

  • Dynamic fleet management
  • approximate dynamic programming
  • network
  • roadway maintenance
  • snow plow truck
  • stochastic
  • uncertainty

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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