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Author (up) L. Calvet; A. Ferrer; M. Gomes; A. Juan; David Masip edit   pdf
doi  openurl
  Title Combining Statistical Learning with Metaheuristics for the Multi-Depot Vehicle Routing Problem with Market Segmentation Type Journal Article
  Year 2016 Publication Computers & Industrial Engineering Abbreviated Journal CIE  
  Volume 94 Issue Pages 93-104  
  Keywords Multi-Depot Vehicle Routing Problem; market segmentation applications; hybrid algorithms; statistical learning  
  Abstract In real-life logistics and distribution activities it is usual to face situations in which the distribution of goods has to be made from multiple warehouses or depots to the nal customers. This problem is known as the Multi-Depot Vehicle Routing Problem (MDVRP), and it typically includes two sequential and correlated stages: (a) the assignment map of customers to depots, and (b) the corresponding design of the distribution routes. Most of the existing work in the literature has focused on minimizing distance-based distribution costs while satisfying a number of capacity constraints. However, no attention has been given so far to potential variations in demands due to the tness of the customerdepot mapping in the case of heterogeneous depots. In this paper, we consider this realistic version of the problem in which the depots are heterogeneous in terms of their commercial o er and customers show di erent willingness to consume depending on how well the assigned depot ts their preferences. Thus, we assume that di erent customer-depot assignment maps will lead to di erent customer-expenditure levels. As a consequence, market-segmentation strategiesneed to be considered in order to increase sales and total income while accounting for the distribution costs. To solve this extension of the MDVRP, we propose a hybrid approach that combines statistical learning techniques with a metaheuristic framework. First, a set of predictive models is generated from historical data. These statistical models allow estimating the demand of any customer depending on the assigned depot. Then, the estimated expenditure of each customer is included as part of an enriched objective function as a way to better guide the stochastic local search inside the metaheuristic framework. A set of computational experiments contribute to illustrate our approach and how the extended MDVRP considered here di ers in terms of the proposed solutions from the traditional one.  
  Address  
  Corporate Author Thesis  
  Publisher PERGAMON-ELSEVIER SCIENCE LTD Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title CIE  
  Series Volume Series Issue Edition  
  ISSN 0360-8352 ISBN Medium  
  Area Expedition Conference  
  Notes OR;MV; Approved no  
  Call Number Admin @ si @ CFG2016 Serial 2749  
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