A tabu search heuristic for the vehicle routing problem
Management Science
A tabu search heuristic for the multi-depot vehicle routing problem
Computers and Operations Research
The Granular Tabu Search and Its Application to the Vehicle-Routing Problem
INFORMS Journal on Computing
A compact model and tight bounds for a combined location-routing problem
Computers and Operations Research
Very large-scale vehicle routing: new test problems, algorithms, and results
Computers and Operations Research
A Metaheuristic to Solve a Location-Routing Problem with Non-Linear Costs
Journal of Heuristics
A GRASP×ELS approach for the capacitated location-routing problem
Computers and Operations Research
A Branch-and-Cut method for the Capacitated Location-Routing Problem
Computers and Operations Research
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
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In this paper, we propose a two-phase hybrid heuristic algorithm to solve the capacitated location-routing problem (CLRP). The CLRP combines depot location and routing decisions. We are given on input a set of identical vehicles (each having a capacity and a fixed cost), a set of depots with restricted capacities and opening costs, and a set of customers with deterministic demands. The problem consists of determining the depots to be opened, the customers and the vehicles to be assigned to each open depot, and the routes to be performed to fulfill the demand of the customers. The objective is to minimize the sum of the costs of the open depots, of the fixed cost associated with the used vehicles, and of the variable traveling costs related to the performed routes. In the proposed hybrid heuristic algorithm, after a Construction phase (first phase), a modified granular tabu search, with different diversification strategies, is applied during the Improvement phase (second phase). In addition, a random perturbation procedure is considered to avoid that the algorithm remains in a local optimum for a given number of iterations. Computational experiments on benchmark instances from the literature show that the proposed algorithm is able to produce, within short computing time, several solutions obtained by the previously published methods and new best known solutions.