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A Grouping Genetic Algorithm for Multi Depot Pickup and Delivery Problems with Time Windows and Heterogeneous Vehicle Fleets

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 12102)

Abstract

The Multi Depot Pickup and Delivery Problem with Time Windows and Heterogeneous Vehicle Fleets is a Rich Vehicle Routing Problem as it combines many real-world problems and is therefore relevant to practice. In this paper a new mathematical two-index model formulation for the MDPDPTWHV is developed as well as a Grouping Genetic Algorithm (GGA), which features a grouping-oriented individual representation. Therefore, each chromosome contains only the assignment of requests to vehicles, i.e., no information about the customer sequence is included. In order to compare different variants of the GGA to each other as well as the best one to solutions calculated by Cplex, 120 MDPDPTWHV datasets are created through a generator implemented by the authors. In a benchmark study, it can be shown that the way in which population management is performed is important to enhance the solution quality of the GGA. On average, the best GGA variant is 2.43% worse than the best known solution.

Keywords

  • Multi-Depot Pickups and Deliveries
  • Combinatorial optimization model
  • Rich Vehicle Routing

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Correspondence to Cornelius Rüther .

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Rüther, C., Rieck, J. (2020). A Grouping Genetic Algorithm for Multi Depot Pickup and Delivery Problems with Time Windows and Heterogeneous Vehicle Fleets. In: Paquete, L., Zarges, C. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2020. Lecture Notes in Computer Science(), vol 12102. Springer, Cham. https://doi.org/10.1007/978-3-030-43680-3_10

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  • DOI: https://doi.org/10.1007/978-3-030-43680-3_10

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