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Fixed fleet open vehicle routing problem: Mathematical model and a modified ant colony optimization

Yousefikhoshbakht Majid, Didehvar Farzad, Rahmati Farhad, Ahmed Zakir Hussain

Bulletin of the Polish Academy of Sciences. Technical Sciences

2024

Vol. 72, nr 1

art. no. e148253

The fixed fleet heterogeneous open vehicle routing problem (HFFOVRP) is one of the most practical versions of the vehicle routing problem (VRP) defined because the use of rental vehicles reduces the cost of purchasing and routing for shipping companies nowadays. Also, applying a heterogeneous fleet is recommended due to the physical limitations of the streets and efforts to reduce the running costs of these companies. In this paper, a mixed-integer linear programming is proposed for HFFOVRP. Because this problem, like VRP, is related to NP-hard issues, it is not possible to use exact methods to solve real-world problems. Therefore, in this paper, a hybrid algorithm based on the ant colony algorithm called MACO is presented. This algorithm uses only global updating pheromones for a more efficient search of feasible space and considers a minimum value for pheromones on the edges. Also, pheromones of some best solutions obtained so far are updated, based on the quality of the solutions at each iteration, and three local search algorithms are used for the intensification mechanism. This method was tested on several standard instances, and the results were compared with other algorithms. The computational results show that the proposed algorithm performs better than these methods in cost and CPU time. Besides, not only has the algorithm been able to improve the quality of the best-known solutions in nine cases but also the high-quality solutions are obtained for other instances.

Exponential and chaotic neurodynamical tabu searches for quadratic assignment problems

Hasegawa M., Ikeguchi T., Aihara K.

Control and Cybernetics

2000

Vol. 29, no 3

773-788

We propose a chaotic neurodynamical searching method for the Quadratic Assignment Problems (QAPs). First, we construct a neural network whose behavior is the same as that of the conventional tabu search. Using the dynamics of the tabu search neural network, we realize the exponential tabu search, whose tabu effect decreases exponentially with time, and we show the effectiveness of this type of exponential tabu search. Next, we extend this novel tabu search to a chaotic version. This chaotic method includes both effects of the chaotic dynamical search and the exponential tabu search, and exhibits better performance than the conventional and exponential tabu searches. Last, we propose an automatic parameter tuning method and show that the proposed method exhibits high performance even on large QAPs.