Majid Yousefikhoshbakht Articles List

List of Articles Majid Yousefikhoshbakht

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1 - A Mixed Integer Programming Formulation for the Heterogeneous Fixed Fleet Open Vehicle Routing Problem
Majid Yousefikhoshbakht Frazad Didehvar Farhad Rahmati
20.1001.1.22519904.2015.8.18.4.7

The heterogeneous fixed fleet open vehicle routing problem (HFFOVRP) is one of the most significant extension problems of the open vehicle routing problem (OVRP). The HFFOVRP is the problem of designing collection routes to a number of predefined nodes by a fixed fleet More

The heterogeneous fixed fleet open vehicle routing problem (HFFOVRP) is one of the most significant extension problems of the open vehicle routing problem (OVRP). The HFFOVRP is the problem of designing collection routes to a number of predefined nodes by a fixed fleet number of vehicles with various capacities and related costs. In this problem, the vehicle doesn’t return to the depot after serving the last customer. Because of its numerous applications in industrial and service problems, a new model of the HFFOVRP based on mixed integer programming is proposed in this paper. Furthermore, due to its NP-hard nature, an ant colony system (ACS) algorithm was proposed. Since there were no existing benchmarks, this study generated some test problems. From the comparison with the results of exact algorithm, the proposed algorithm showed that it can provide better solutions within a comparatively shorter period of time. Manuscript profile
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2 - Solving the Multiple Traveling Salesman Problem by a Novel Meta-heuristic Algorithm
Hossein Larki Majid Yousefikhoshbakht
20.1001.1.22519904.2014.7.16.5.7

The multiple traveling salesman problem (MTSP) is a generalization of the famous traveling salesman problem (TSP), where more than one salesman is used in the solution. Although the MTSP is a typical kind of computationally complex combinatorial optimization problem, it More

The multiple traveling salesman problem (MTSP) is a generalization of the famous traveling salesman problem (TSP), where more than one salesman is used in the solution. Although the MTSP is a typical kind of computationally complex combinatorial optimization problem, it can be extended to a wide variety of routing problems. This paper presents an efficient and evolutionary optimization algorithm which has been developed through combining Modified Imperialist Competitive Algorithm and Lin-Kernigan Algorithm (MICA) in order to solve the MTSP. In the proposed algorithm, an absorption function and several local search algorithms as a revolution operator are used. The performance of our algorithm was tested on several MTSP benchmark problems and the results confirmed that the MICA performs well and is quite competitive with other meta-heuristic algorithms. Manuscript profile
Open Access Article
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3 - An Effective Genetic Algorithm for Solving the Multiple Traveling Salesman Problem
Mohammad Sedighpour Majid Yousefikhoshbakht Narges Mahmoodi Darani

The multiple traveling salesman problem (MTSP) involves scheduling m > 1 salesmen to visit a set of n > m nodes so that each node is visited exactly once. The objective is to minimize the total distance traveled by all the salesmen. The MTSP is an example of combi More

The multiple traveling salesman problem (MTSP) involves scheduling m > 1 salesmen to visit a set of n > m nodes so that each node is visited exactly once. The objective is to minimize the total distance traveled by all the salesmen. The MTSP is an example of combinatorial optimization problems, and has a multiplicity of applications, mostly in the areas of routing and scheduling. In this paper, a modified hybrid metaheuristic algorithm called GA2OPT for solving the MTSP is proposed. In this algorithm, at the first stage, the MTSP is solved by the modified genetic Algorithm (GA) in each iteration, and, at the second stage, the 2-Opt local search algorithm is used for improving solutions for that iteration. The proposed algorithm was tested on a set of 6 benchmark instances from the TSPLIB and in all but four instances the best known solution was improved. For the rest instances, the quality of the produced solution deviates less than 0.01% from the best known solutions ever. Manuscript profile

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List of Articles Majid Yousefikhoshbakht