1. A multi-start local search heuristic for the Green Vehicle Routing Problem based on a multigraph reformulation
- Author
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Enrico Bartolini, Juho Andelmin, Department of Mathematics and Systems Analysis, RWTH Aachen University, Aalto-yliopisto, and Aalto University
- Subjects
0209 industrial biotechnology ,Mathematical optimization ,General Computer Science ,Heuristic (computer science) ,Computer science ,local search ,0211 other engineering and technologies ,02 engineering and technology ,Management Science and Operations Research ,020901 industrial engineering & automation ,Vehicle routing problem ,multigraph ,Local search (optimization) ,ta113 ,021103 operations research ,business.industry ,Heuristic ,ta111 ,Multigraph ,Construct (python library) ,Modeling and Simulation ,Benchmark (computing) ,alternative fuel vehicles ,business ,Heuristics ,vehicle routing - Abstract
We consider the Green Vehicle Routing Problem (G-VRP) which is an extension of the classical vehicle routing problem for alternative fuel vehicles. In the G-VRP, vehicles' driving autonomy and possible refueling stops en-route are explicitly modeled. We propose a multi-start local search algorithm that consists of three phases. The first two phases iteratively construct new solutions, improve them by local search, and store all vehicle routes forming these solutions in a route pool. Phase three optimally combines vehicle routes in the route pool by solving a set partitioning problem and improves the final solution by local search. The algorithm is based on a multigraph reformulation of the G-VRP in which nodes correspond to customers and a depot, and arcs correspond to possible sequences of refueling stops for vehicles traveling between two nodes. All local search operators used by our algorithm are tailored to exploit this reformulation and do not explicitly deal with refueling stations. We report computational results on benchmark instances with up to ~ 470 customers, showing that the algorithm is competitive with state-of-the-art heuristics.
- Published
- 2019
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