A new optimization algorithm for the vehicle routing problem with time windows
Operations Research
The vehicle routing problem
The vehicle routing problem
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Evolutionary Algorithms for the Vehicle Routing Problem with Time Windows
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A cooperative parallel meta-heuristic for the vehicle routing problem with time windows
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Comparison of similarity measures for the multi-objective vehicle routing problem with time windows
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Preserving population diversity for the multi-objective vehicle routing problem with time windows
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The vehicle routing problem with time windows is a complex combinatorial problem with many real-world applications in transportation and distribution logistics. Its main objective is to find the lowest distance set of routes to deliver goods, using a fleet of identical vehicles with restricted capacity, to customers with service time windows. However, there are other objectives, and having a range of solutions representing the trade-offs between objectives is crucial for many applications. Although previous research has used evolutionary methods for solving this problem, it has rarely concentrated on the optimization of more than one objective, and hardly ever explicitly considered the diversity of solutions. This paper proposes and analyzes a novel multi-objective evolutionary algorithm, which incorporates methods for measuring the similarity of solutions, to solve the multi-objective problem. The algorithm is applied to a standard benchmark problem set, showing that when the similarity measure is used appropriately, the diversity and quality of solutions is higher than when it is not used, and the algorithm achieves highly competitive results compared with previously published studies and those from a popular evolutionary multi-objective optimizer.