Parameter tuning of a local search heuristic for a vehicle routing problem with loading constraints

A vehicle routing problem (VRP) with sequence-based pallet loading and axle weight constraints is introduced in the study. An Iterated Local Search (ILS) metaheuristic algorithm is used to solve the problem. Like any metaheuristic, a number of parameters need to be set before running the experiments. Parameter tuning is important because the value of the parameters may have a substantial impact on the efficacy of a heuristic algorithm. While traditionally, parameter values have been set manually using expertise and experimentation, recently several automated tuning methods have been proposed. The performance of the routing algorithm is mostly improved by using parameter tuning, but no single best tuning method for routing algorithms exists. The tuning method, Iterated F-race, is chosen because it seems to be a very robust method and it has been shown to perform well on the ILS metaheuristic and other metaheuristics. The research aims at developing an algorithm, which performs well over a wide range of network sizes.

Publication year:2020

Keywords:Decision making, evolutionary computation, operations research, supply chain management

BOF-keylabel:yes

Accessibility:Open