A tabu search heuristic for the multi-depot vehicle routing problem
Computers and Operations Research
Modeling and solving several classes of arc routing problems as traveling salesman problems
Computers and Operations Research
Memetic algorithms: a short introduction
New ideas in optimization
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Genetic Algorithm for the Capacitated Arc Routing Problem and Its Extensions
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
A Tabu Search Heuristic for the Capacitated Arc Routing Problem
Operations Research
Lower and upper bounds for the mixed capacitated arc routing problem
Computers and Operations Research
New lower bound for the capacitated arc routing problem
Computers and Operations Research
A deterministic tabu search algorithm for the capacitated arc routing problem
Computers and Operations Research
A hybrid genetic algorithm for the multi-depot vehicle routing problem
Engineering Applications of Artificial Intelligence
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The double layer optimization problem to express logistics systems and its heuristic algorithm
Expert Systems with Applications: An International Journal
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The multi-depot capacitated arc routing problem (MD-CARP) generalises the well-known capacitated arc routing problem (CARP) by extending the single depot to a multi-depot network. The CARP consists of designing a set of vehicle trips, so that each vehicle starts and ends at the single depot. The MD-CARP involves the assignment of edges, which have to be served, to depots and the determination of vehicle trips for each depot. The first proposed work is based on ant colony optimisation (ACO) combined with an insertion heuristic: the ACO is used to optimise the order of insertion of the edges and the heuristic is devoted to inserting each edge in the solution. The second one is a memetic algorithm based on a special crossover. The computational results on benchmark instances show the satisfactory quality of the proposed methods and the superiority of the memetic algorithm compared to the ACO method.