Abstract:
This paper presents the capacitated Windy Rural Postman Problem with several vehicles. For this problem, two objectives are considered. One of them is the minimization of the total cost of all vehicle routes expressed by the sum of the total traversing cost and another one is reduction of the maximum cost of vehicle route in order to find a set of equitable tours for the vehicles. Mathematical formulation is provided. The multi-objective simulated annealing (MOSA) algorithm has been modified for solving this bi-objective NP-hard problem. To increase algorithm performance, Taguchi technique is applied to design experiments for tuning parameters of the algorithm. Numerical experiments are proposed to show efficiency of the model. Finally, the results of the MOSA have been compared with MOCS (multi-objective Cuckoo Search algorithm) to validate the performance of the proposed algorithm. The experimental results indicate that the proposed algorithm provides good solutions and performs significantly better than the MOCS.
Machine summary:
"com Capacitated Windy Rural Postman Problem with several vehicles: A hybrid multi-objective simulated annealing algorithm Masoud Rabbani a,1, Safoura Famil Alamdara, Hamed Farrokhi-Aslb a School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran b School of Industrial Engineering, Iran University of Science &Technology, Tehran, Iran Abstract This paper presents the capacitated Windy Rural Postman Problem with several vehicles.
Micó & Soler (2011) considered turn penalties and forbidden turns for the capacitated general windy routing problem which are applicable in many real-life conditions such as in downtown areas and for large vehicles.
According to aforementioned papers, the contribution of this study is designing a metaheuristics (multi-objective simulated annealing (MOSA) algorithm) for solving capacitated Windy Rural Postman Problem with several vehicles.
We use the MOCS to validate the performance of the proposed algorithm because this new algorithm has been shown to be very successful in tackling multi objective optimization problems and also characterized by the reduced number of parameters causing robustness of algorithm and this algorithm provides effective results for multimodal functions in comparison with both genetic algorithms and particle swarm optimization (Yang & Deb, 2010).
The following cases can be mentioned as the future researches: Considering the uncertainties in some parameters, such as demand of required edges which increases problem complexity; using other meta-heuristic methods such as Non-dominated Sorting Genetic Algorithm (NSGA-/), multi-objective particle swarm optimization algorithm (MOPSO) and multi-objective Imperialist Competitive Algorithm and comparing results of these algorithms with results obtained from algorithm presented in this paper."