Heuristic solutions to multi-depot location-routing problems
Computers and Operations Research
A compact model and tight bounds for a combined location-routing problem
Computers and Operations Research
A Memetic Algorithm with Population Management (MA|PM) for the Periodic Location-Routing Problem
HM '08 Proceedings of the 5th International Workshop on Hybrid Metaheuristics
MA|PM: memetic algorithms with population management
Computers and Operations Research
Evolutionary local search for the super-peer selection problem and the p-hub median problem
HM'07 Proceedings of the 4th international conference on Hybrid metaheuristics
A memetic algorithm with population management (MA|PM) for the capacitated location-routing problem
EvoCOP'06 Proceedings of the 6th European conference on Evolutionary Computation in Combinatorial Optimization
HM'10 Proceedings of the 7th international conference on Hybrid metaheuristics
A GRASP + ILP-based metaheuristic for the capacitated location-routing problem
Journal of Heuristics
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The well-known Vehicle Routing Problem (VRP) has been deeply studied over the last decades. Nowadays, generalizations of VRP are developed toward tactical or strategic decision levels of companies. The tactical extension plans a set of trips over a multiperiod horizon, subject to frequency constraints. The related problem is called the Periodic VRP (PVRP). On the other hand, the strategic extension is motivated by interdependent depot location and routing decisions in most distribution systems. Low-quality solutions are obtained if depots are located first, regardless the future routes. In the Location-Routing Problem (LRP), location and routing decisions are simultaneously tackled. The goal here is to combine the PVRP and LRP into an even more realistic problem covering all decision levels: the Periodic LRP or PLRP. A hybrid evolutionary algorithm is proposed to solve large size instances of the PLRP. First, an individual representing an assignment of customers to combinations of visit days is randomly generated. Then, a heuristic based on the Randomized Extended Clarke and Wright Algorithm (RECWA) creates feasible solutions. The evolution operates through an Evolutionary Local Search (ELS) on visit days assignments. The algorithm is hybridized with a Path Relinking between individuals from an elite list. The method is evaluated on three sets of instances and solutions are compared to the literature on particular cases such as one-day horizon (LRP) or one depot (PVRP). This metaheuristic outperforms the previous methods for the PLRP.