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Simulation-based Optimization of a Real-world Travelling Salesman Problem Using an Evolutionary Algorithm with a Repair Function
Anna Syberfeldt, Joel Rogstrom, Andre Geertsen
Pages - 27 - 39     |    Revised - 30-09-2015     |    Published - 31-10-2015
Volume - 6   Issue - 3    |    Publication Date - October 2015  Table of Contents
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KEYWORDS
Evolutionary Algorithm, Simulation-based Optimization, Travelling Salesman Problem, Waste Collection, Real-world Case Study.
ABSTRACT
This paper presents a real-world case study of optimizing waste collection in Sweden. The problem, involving approximately 17,000 garbage bins served by three bin lorries, is approached as a travelling salesman problem and solved using simulation-based optimization and an evolutionary algorithm. To improve the performance of the evolutionary algorithm, it is enhanced with a repair function that adjusts its genome values so that shorter routes are found more quickly. The algorithm is tested using two crossover operators, i.e., the order crossover and heuristic crossover, combined with different mutation rates. The results indicate that the order crossover is superior to the heuristics crossover, but that the driving force of the search process is the mutation operator combined with the repair function.
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Mr. Anna Syberfeldt
Engineering Science University of Skövde Skövde, SE-54148 - Sweden
anna.syberfeldt@his.se
Dr. Joel Rogstrom
Engineering Science University of Skövde Skövde, SE-54148 - Sweden
Dr. Andre Geertsen
Engineering Science University of Skövde Skövde, SE-54148 - Sweden


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