Solving the Team Orienteering Problem: Developing a Solution Tool Using a Genetic Algorithm Approach
Nowadays, the collection of separated solid waste for recycling is still an expensive process, specially when performed in large-scale. One main problem resides in fleet-management, since the currently applied strategies usually have low efficiency. The waste collection process can be modelled as a vehicle routing problem, in particular as a Team Orienteering Problem (TOP). In the TOP, a vehicle fleet is assigned to visit a set of customers, while executing optimized routes that maximize total profit and minimize resources needed. The objective of this work is to optimize the waste collection process while addressing the specific issues around fleet-management. This should be achieved by developing a software tool that implements a genetic algorithm to solve the TOP. We were able to accomplish the proposed task, as our computational tests have produced some challenging results in comparison to previous work around this subject of study. Specifically, our results attained 60% of the best known scores in a selection of 24 TOP benchmark instances, with an average error of 18.7 in the remaining instances. The usage of a genetic algorithm to solve the TOP proved to be an efficient method by outputting good results in an acceptable time.
KeywordsRouting problems Team orienteering problem Optimization Metaheuristics Genetic algorithm
This study was partially supported by the project GATOP - Genetic Algorithms for Team Orienteering Problem (Ref PTDC/EME-GIN/ 120761/2010), financed by national funds by FCT / MCTES, and co-funded by the European Social Development Fund (FEDER) through the COMPETE Programa Operacional Fatores de Competitividade (POFC) Ref FCOMP-01-0124-FEDER-020609.
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