A MILP model and memetic algorithm for the Hub Location and Routing problem with distinct collection and delivery tours

•Solutions of Hub Location Routing Problems with distinct pickup and delivery tours.•MILP model finds solutions of small to medium size instances.•Memetic Algorithm obtains high quality solutions of large problems efficiently.•Sensitivity analysis proves robustness of MA and usefulness for decision-...

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Veröffentlicht in:Computers & industrial engineering Jg. 135; S. 105 - 119
Hauptverfasser: Yang, Xiao, Bostel, Nathalie, Dejax, Pierre
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.09.2019
Elsevier
Schlagworte:
Computer Science
Hub Location-Routing Problem
LTL transport
Memetic algorithm
Mixed integer programming model
Operations Research
Memetic algorithm
Hub Location-Routing Problem
LTL transport
Mixed integer programming model
ISSN:0360-8352, 1879-0550
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Zusammenfassung:•Solutions of Hub Location Routing Problems with distinct pickup and delivery tours.•MILP model finds solutions of small to medium size instances.•Memetic Algorithm obtains high quality solutions of large problems efficiently.•Sensitivity analysis proves robustness of MA and usefulness for decision-making. In this paper, we study the capacitated single allocation Hub Location-Routing Problem (CSAHLRP) with independent collection and delivery processes. We focus on the design of less-than-truck load (LTL) transport networks for the transport of goods between many shippers and their clients using flow concentration hubs. We seek to integrate the hub location and vehicle routing strategies such that the location of the hubs, the allocation of supplier/client nodes to hubs, the design of routes between nodes allocated to the same hub, as well as the inter-hub freight transportations, can be determined efficiently. We propose a mixed integer linear programming model for the problem with the aim of minimizing the total fixed and variable costs. Computational experiments based on the Australian Post (AP) data set geographical network are conducted with the CPLEX solver. Furthermore, we propose a memetic algorithm (MA) to solve large problems. Computational results show that the exact method can find optimal solutions for small instances and good feasible solutions for some medium-sized tests. In addition to also solving small problems to optimality, the MA succeeds in finding high quality solutions for medium and large CSAHLRPs efficiently.
ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2019.05.038