An adaptive large neighborhood search heuristic for the Electric Vehicle Scheduling Problem

M. Wen, E. Linde, S. Ropke, P. Mirchandani, A. Larsen

Research output: Contribution to journalArticlepeer-review

173 Scopus citations


This paper addresses the Electric Vehicle Scheduling Problem (E-VSP), in which a set of timetabled bus trips, each starting from and ending at specific locations and at specific times, should be carried out by a set of electric buses or vehicles based at a number of depots with limited driving ranges. The electric vehicles are allowed to be recharged fully or partially at any of the given recharging stations. The objective is to firstly minimize the number of vehicles needed to cover all the timetabled trips, and secondly to minimize the total traveling distance, which is equivalent to minimizing the total deadheading distance. A mixed integer programming formulation as well as an Adaptive Large Neighborhood Search (ALNS) heuristic for the E-VSP are presented. ALNS is tested on newly generated E-VSP benchmark instances. Result shows that the proposed heuristic can provide good solutions to large E-VSP instances and optimal or near-optimal solutions to small E-VSP instances.

Original languageEnglish (US)
Pages (from-to)73-83
Number of pages11
JournalComputers and Operations Research
StatePublished - Dec 1 2016


  • Electric vehicles
  • Large neighborhood search
  • Partial charging
  • Vehicle scheduling

ASJC Scopus subject areas

  • General Computer Science
  • Modeling and Simulation
  • Management Science and Operations Research


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