Abstract
This paper addresses the Vehicle Routing Problem with Time Windows (VRPTW) and shows that implementing algorithms for solving various instances of VRPs can significantly reduce transportation costs that occur during the delivery process. Two metaheuristic algorithms were developed for solving VRPTW: Simulated Annealing and Iterated Local Search. Both algorithms generate initial feasible solution using constructive heuristics and use operators and various strategies for an iterative improvement. The algorithms were tested on Solomon’s benchmark problems and real world vehicle routing problems with time windows. In total, 44 real world problems were optimized in the case study using described algorithms. Obtained results showed that the same distribution task can be accomplished with savings up to 40% in the total travelled distance and that manually constructed routes are very ineffective.
Highlights
Vehicle routing problem (VRP) is interesting to the scientific community because of the possibility of greatly reducing transportation costs by successfully solving the problem
This paper addresses the Vehicle Routing Problem with Time Windows (VRPTW) and shows that implementing algorithms for solving various instances of VRPs can significantly reduce transportation costs that occur during the delivery process
Two metaheuristic algorithms were developed for solving VRPTW: Simulated Annealing and Iterated Local Search
Summary
Vehicle routing problem (VRP) is interesting to the scientific community because of the possibility of greatly reducing transportation costs by successfully solving the problem. Optimization of delivery routes is important because transport costs influence the overall cost of the delivered goods. In this field of combinatorial optimization many scientific papers and surveys were published [1, 2, 3, 4, 5]. Constructed routes are very inefficient and can be significantly improved by algorithms for solving VRP
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