"Ant Colony Optimization Algorithm for Multi-Depot Vehicle Routing Problem with Time Windows" . "Ant Colony Optimization Algorithm for Multi-Depot Vehicle Routing Problem with Time Windows" . . . "Multi-Depot Vehicle Routing Problem with Time Windows; Ant Colony Optimization; Nature-inspired Algorithm"@en . "Ant Colony Optimization Algorithm for Multi-Depot Vehicle Routing Problem with Time Windows"@en . "G42" . "Kos Island, \u0158ecko" . "3349" . . "Kos Island, \u0158ecko" . . . "2"^^ . "9"^^ . "2014-01-01+01:00"^^ . . . . "National Technical University of Athens" . "RIV/60162694:G42__/14:00519227" . "International Conference on Engineering and Applied Sciences Optimization (OPT-i 2014)" . "http://vavtest.unob.cz/registr" . "Mazal, Jan" . "2"^^ . . "[AF7B0DCB5B18]" . . "Stodola, Petr" . . . "Ant Colony Optimization Algorithm for Multi-Depot Vehicle Routing Problem with Time Windows"@en . "RIV/60162694:G42__/14:00519227!RIV15-MO0-G42_____" . "The Multi-Depot Vehicle Routing Problem with Time Windows (MDVRP-TW) is a well-known optimization problem with many practical applications in a wide range of domains. The prima-ry goal is to find optimal routes for a set of vehicles starting from multiple depots to a number of customers. Each customer should be served only once and each vehicle returns to the origi-nal depot after visiting all customers along its route. Each vehicle can visit a customer only dur-ing its particular predefined time window. Also the condition of maximum vehicles\u2019 load has to be met. There are many heuristic and metaheuristic algorithms proposed for this problem solu-tion (e.g. tabu search, simulated annealing, genetic algorithms) as this is a NP-hard problem and, therefore, exact methods are not feasible for more complex solutions. Another possibility is to apply the Ant Colony Optimization (ACO) theory to this problem. The authors successfully managed to adapt the ACO algorithm for the MDVRP-TW problem. The algorithm has been verified on a set of Cordeau\u2019s test instances. In all tested cases, the solution found by our algo-rithm compared with the best solution known so far is not worse than 8%. In addition, the great advantage is that the algorithm is very fast as the key processes can be executed in parallel (e.g. on multiple cores of a processor). The article presents an original solution of authors to the MDVRP-TW problem via the ACO algorithm in detail. The first part deals with the algorithm including its principles and parameters. Then several examples and experiments are shown. The last part of the article shows conclusions and future work of the authors."@en . "2241-9098" . . "978-960-99994-5-8" . "I" . . "The Multi-Depot Vehicle Routing Problem with Time Windows (MDVRP-TW) is a well-known optimization problem with many practical applications in a wide range of domains. The prima-ry goal is to find optimal routes for a set of vehicles starting from multiple depots to a number of customers. Each customer should be served only once and each vehicle returns to the origi-nal depot after visiting all customers along its route. Each vehicle can visit a customer only dur-ing its particular predefined time window. Also the condition of maximum vehicles\u2019 load has to be met. There are many heuristic and metaheuristic algorithms proposed for this problem solu-tion (e.g. tabu search, simulated annealing, genetic algorithms) as this is a NP-hard problem and, therefore, exact methods are not feasible for more complex solutions. Another possibility is to apply the Ant Colony Optimization (ACO) theory to this problem. The authors successfully managed to adapt the ACO algorithm for the MDVRP-TW problem. The algorithm has been verified on a set of Cordeau\u2019s test instances. In all tested cases, the solution found by our algo-rithm compared with the best solution known so far is not worse than 8%. In addition, the great advantage is that the algorithm is very fast as the key processes can be executed in parallel (e.g. on multiple cores of a processor). The article presents an original solution of authors to the MDVRP-TW problem via the ACO algorithm in detail. The first part deals with the algorithm including its principles and parameters. Then several examples and experiments are shown. The last part of the article shows conclusions and future work of the authors." . . .