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- W3048458096 abstract "This work explores a complex new vehicle routing problem in which transport times and services derive from probability distributions, and a time window constraint is associated with each client. Vehicles of different capacity are routed from the warehouse to a set of geographically dispersed customers, late delivery of the order is allowed by adding a penalty cost, routes should be determined in such a way that the courses are minimized total system costs, and all customers are satisfied. Such routing problems are NP-hard in deterministic environments, and, in our context, even more given the stochastic nature. The objective of the research is to compare the efficiency of a new metaheuristic (Ebola) adapted to the problem, with the best metaheuristics found in the specific topic literature (a genetic algorithm and an improved taboo search algorithm). For each algorithm, random and mapped encodings were developed in the generation of initial solutions. Several instances were randomly generated, the results show that the genetic algorithm gets the best results; on the contrary taboo and Ebola prove to be more efficient algorithms in computational times." @default.
- W3048458096 created "2020-08-18" @default.
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- W3048458096 date "2020-08-11" @default.
- W3048458096 modified "2023-09-23" @default.
- W3048458096 title "Metaheuristics Applied to the Fleet Size and Mix Vehicle Routing Problems with Soft Time Windows and Stochastic Times" @default.
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- W3048458096 doi "https://doi.org/10.1007/978-3-030-53021-1_65" @default.
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