On the Dial-a-Ride with time windows (DARPTW) customer transportation problem, there is a set of requests from customers to be transported from an origin place to a delivery place through a locations network, under several constraints like the time windows. The problem complexity (NP-Hard) forces the use of heuristics on its resolution. In this context, the application of Genetic Algorithms (GA) on DARPTW was not largely considered, with the exception of a few researches. In this work, under a restrictive scenario, a GA model for the problem was developed based on the adaptation of a generic GA model from literature. Our solution applies data pre-processing techniques to reduce the search space to points that are feasible regarding time windows constraints. Tests show competitive results on Cordeau & Laporte benchmark datasets while improving processing times.
The dial-a-ride problem with time windows (DARPTW) is a combinatorial optimization problem related to transportation, in which a set of customers must be picked up from an origin location and they have to be delivered to a destination location. A transportation schedule must be constructed for a set of available vehicles, and several constraints have to be considered, particularly time windows, which define an upper and lower time bound for each customer request in which a vehicle must arrive to perform the service. Because of the complexity of DARPTW, a number of algorithms have been proposed for solving the problem, mainly based on metaheuristics such as Genetic Algorithms and Simulated Annealing. In this work, a different approach for solving DARPTW is proposed, designed, and evaluated: hyperheuristics, which are alternative heuristic methods that operate at a higher abstraction level than metaheuristics, because rather than searching in the problem space directly, they search in a space of low-level heuristics to find the best strategy through which good solutions can be found. Although the proposed hyperheuristic uses simple and easy-to-implement operators, the experimental results demonstrate efficient and competitive performance on DARPTW when compared to other metaheuristics from the literature.
This work presents the development of MABAP, a decision support system based on the agent technology that helps in solving the problem of berth allocation for ships within a port. The Berth Allocation Problem (BAP) regards the logistics involved in planning and controlling the berthing of vessels. A software architecture in terms of agents is presented; Berths and Ships representing the actors in the system, BerthRequest and BerthPlanner as representatives of ships and berths in the planning process, and finally the Dock and Central agents representing the dock or pier. The architecture modeling was done using PASSI methodology for the design of agent-oriented systems, and the implementation was done in JADE, a Javabased development environment for multiagent systems. To validate the resulting support system, tests were carried out in which the user can choose different portpolicy scenarios, ranging from maximizing vessels throughput to maximize berths use.
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