How to cite this paper
Veeresh, M., Kumar, T & Thangaraj, M. (2024). Solving the single depot open close multiple travelling salesman problem through a multi-chromosome based genetic algorithm.Decision Science Letters , 13(2), 401-414.
Refrences
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Al-Omeer, M. A., & Ahmed, Z. H. (2019, April). Comparative study of crossover operators for the MTSP. In 2019 International Conference on Computer and Information Sciences (ICCIS) (pp. 1–6). IEEE.
Alves, R. M., & Lopes, C. R. (2015, May). Using genetic algorithms to minimize the distance and balance the routes for the multiple traveling salesman problem. In 2015 IEEE Congress on Evolutionary Computation (CEC) (pp. 3171–3178). IEEE.
Bagley, J. D. (1967). The behavior of adaptive systems which employ genetic and correlation algorithms. University of Michigan.
Bao, C., Yang, Q., Gao, X. D., & Zhang, J. (2021, December). A comparative study on population-based evolutionary algorithms for multiple traveling salesman problem with visiting constraints. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 01–08). IEEE.
Belhor, M., El-Amraoui, A., Jemai, A., & Delmotte, F. (2023). Learning-based metaheuristic approach for home healthcare optimization problem. Computer Systems Science and Engineering, 45, 1 –19.
Braekers, K., Ramaekers, K., & Van Nieuwenhuyse, I. (2016). The vehicle routing problem: State of the art classification and review. Computers & Industrial Engineering, 99, 300–313.
Brown, E. C., Ragsdale, C. T., & Carter, A. E. (2007). A grouping genetic algorithm for the multiple traveling salesperson problem. International Journal of Information Technology & Decision Making, 6(02), 333–347.
Carter, A. E., & Ragsdale, C. T. (2006). A new approach to solving the multiple traveling salesperson problem using genetic algorithms. European journal of operational research, 175(1), 246–257.
Changdar, C., Mondal, M., Giri, P. K., Nandi, U., & Pal, R. K. (2023). A two-phase ant colony optimization based approach for single depot multiple travelling salesman problem in Type-2 fuzzy environment. Artificial Intelligence Review, 56(2), 965 –993.
Changdar, C., Pal, R. K., & Mahapatra, G. S. (2017). A genetic ant colony optimization based algorithm for solid multiple travelling salesman problem in fuzzy rough environment. Soft Computing, 21(16), 4661–4675.
Cheikhrouhou, O., & Khoufi, I. (2021). A comprehensive survey on the Multiple Traveling Salesman Problem: Applications, approaches and taxonomy. Computer Science Review, 40, 100369.
Garey, M. R., & Johnson, D. S. (1979). A guide to the theory of NP-completeness, Computers and Intractability. New York.
Goldenberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Newyork.
Gomes, D. E., Iglésias, M. I. D., Proença, A. P., Lima, T. M., & Gaspar, P. D. (2021). Applying a genetic algorithm to a m-TSP: case study of a decision support system for optimizing a beverage logistics vehicles routing problem. Electronics, 10(18), 2298.
Harrath, Y., Salman, A. F., Alqaddoumi, A., Hasan, H., & Radhi, A. (2019). A novel hybrid approach for solving the multiple traveling salesman problem. Arab Journal of Basic and applied sciences, 26(1), 103 –112.
Jiang, C., Wan, Z., & Peng, Z. (2020). A new efficient hybrid algorithm for large scale multiple traveling salesman problems. Expert Systems with Applications, 139, 112867.
Kaliaperumal, R., Ramalingam, A., & Sripriya, J. (2015, March). A modified two part chromosome crossover for solving MTSP using genetic algorithms. In Proceedings of the 2015 International Conference on Advanced Research in Computer Science Engineering & Technology (ICARCSET 2015) (pp. 1–4).
Kara, I., & Bektas, T. (2006). Integer linear programming formulations of multiple salesman problems and its variations. European Journal of Operational Research, 174(3), 1449–1458.
Khan, F. H., Khan, N., Inayatullah, S., &Nizami, S. T. (2009). Solving TSP problem by using genetic algorithm. International Journal of Basic & Applied Sciences, 9(10), 79 –88.
Király, A., & Abonyi, J. (2011). Optimization of multiple traveling salesman problem by a novel representation based genetic algorithm. In Intelligent computational optimization in engineering (pp. 241–269). Springer, Berlin, Heidelberg.
Király, A., & Abonyi, J. (2015). Redesign of the supply of mobile mechanics based on a novel genetic optimization algorithm using Google Maps API. Engineering Applications of Artificial Intelligence, 38, 122 –130.
Larranaga, P., Kuijpers, C. M. H., Murga, R. H., Inza, I., &Dizdarevic, S. (1999). Genetic algorithms for the travelling salesman problem: A review of representations and operators. Artificial Intelligence Review, 13(2), 129 –170.
Lou, P., Xu, K., Yan, J., & Xiao, Z. (2021, April). An Improved Partheno-Genetic Algorithm for Open Path Multi-Depot Multiple Traveling Salesman Problem. In Journal of Physics: Conference Series (Vol. 1848, No. 1, p. 012002). IOP Publishing.
Malik, W., Rathinam, S., & Darbha, S. (2007). An approximation algorithm for a symmetric generalized multiple depot, multiple travelling salesman problem. Operations Research Letters, 35(6), 747–753.
Mzili, I., Mzili, T., & Riffi, M. E. (2023). Efficient routing optimization with discrete penguins search algorithm for MTSP. Decision Making: Applications in Management and Engineering, 6(1), 730 –743.
Öncan, T. (2007). A survey of the generalized assignment problem and its applications. INFOR: Information Systems and Operational Research, 45(3), 123–141.
Riazi, A. (2019). Genetic algorithm and a double-chromosome implementation to the traveling salesman problem. SN Applied Sciences, 1(11), 1397.
Sarin, S. C., Sherali, H. D., Judd, J. D., & Tsai, P. F. J. (2014). Multiple asymmetric traveling salesman problem with and without precedence constraints: Performance comparison of alternative formulations. Computers & Operations Research, 51, 64–89.
Shuai, Y., Yunfeng, S., & Kai, Z. (2019). An effective method for solving multiple travelling salesman problem based on NSGA-II. Systems Science & Control Engineering, 7(2), 108 –116.
Singamsetty, P., & Thenepalle, J. (2021). An efficient genetic algorithm for solving open multiple travelling salesman problem with load balancing constraint. Decision Science Letters, 10(4), 525–534.
Singh, D. R., Singh, M. K., Singh, T., & Prasad, R. (2018). Genetic algorithm for solving multiple traveling salesman problem using a new crossover and population generation. Computación y Sistemas, 22(2), 491 –503.
Sofge, D., Schultz, A., & De Jong, K. (2002, April). Evolutionary computational approaches to solving the multiple traveling salesman problem using a neighborhood attractor schema. In Workshops on Applications of Evolutionary Computation (pp. 153–162). Springer, Berlin, Heidelberg.
Tang, L., Liu, J., Rong, A., & Yang, Z. (2000). A multiple traveling salesman problem model for hot rolling scheduling in Shanghai Baoshan Iron & Steel Complex. European Journal of Operational Research, 124(2), 267–282.
Thenepalle, J., & Singamsetty, P. (2019). An open close multiple travelling salesman problem with single depot. Decision Science Letters, 8(2), 121–136.
Tjandra, S. S., Setiawan, F., & Salsabila, H. (2022). Application of Genetic Algorithms to Solve MTSP Problems with Priority (Case Study at the Jakarta Street Lighting Service). Journal on Optimization of Systems in Industries, 21(2), 75 –86.
Wang, M., Ma, T., Li, G., Zhai, X., & Qiao, S. (2020). Ant colony optimization with an improved pheromone model for solving MTSP with capacity and time window constraint. IEEE Access, 8, 106872–106879.
Wang, Y. D., Lu, X. C., & Shen, J. R. (2021). Improved Genetic Algorithm (VNS-GA) using polar coordinate classification for workload balanced multiple Traveling Salesman Problem (mTSP). Advances in Production Engineering & Management, 16(2), 173 –184.
Xu, X., Yuan, H., Liptrott, M., & Trovati, M. (2018). Two phase heuristic algorithm for the multiple-travelling salesman problem. Soft Computing, 22(19), 6567–6581.
Yan, X., Zhang, C., Luo, W., Li, W., Chen, W., & Liu, H. (2012). Solve traveling salesman problem using particle swarm optimization algorithm. International Journal of Computer Science Issues (IJCSI), 9(6), 264.
Yousefikhoshbakht, M., Didehvar, F., & Rahmati, F. (2013). Modification of the ant colony optimization for solving the multiple traveling salesman problem. Romanian Journal of Information Science and Technology, 16(1), 65–80.
Yuan, S., Skinner, B., Huang, S., & Liu, D. (2013). A new crossover approach for solving the multiple travelling salesman problem using genetic algorithms. European journal of operational research, 228(1), 72–82.
Al-Omeer, M. A., & Ahmed, Z. H. (2019, April). Comparative study of crossover operators for the MTSP. In 2019 International Conference on Computer and Information Sciences (ICCIS) (pp. 1–6). IEEE.
Alves, R. M., & Lopes, C. R. (2015, May). Using genetic algorithms to minimize the distance and balance the routes for the multiple traveling salesman problem. In 2015 IEEE Congress on Evolutionary Computation (CEC) (pp. 3171–3178). IEEE.
Bagley, J. D. (1967). The behavior of adaptive systems which employ genetic and correlation algorithms. University of Michigan.
Bao, C., Yang, Q., Gao, X. D., & Zhang, J. (2021, December). A comparative study on population-based evolutionary algorithms for multiple traveling salesman problem with visiting constraints. In 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 01–08). IEEE.
Belhor, M., El-Amraoui, A., Jemai, A., & Delmotte, F. (2023). Learning-based metaheuristic approach for home healthcare optimization problem. Computer Systems Science and Engineering, 45, 1 –19.
Braekers, K., Ramaekers, K., & Van Nieuwenhuyse, I. (2016). The vehicle routing problem: State of the art classification and review. Computers & Industrial Engineering, 99, 300–313.
Brown, E. C., Ragsdale, C. T., & Carter, A. E. (2007). A grouping genetic algorithm for the multiple traveling salesperson problem. International Journal of Information Technology & Decision Making, 6(02), 333–347.
Carter, A. E., & Ragsdale, C. T. (2006). A new approach to solving the multiple traveling salesperson problem using genetic algorithms. European journal of operational research, 175(1), 246–257.
Changdar, C., Mondal, M., Giri, P. K., Nandi, U., & Pal, R. K. (2023). A two-phase ant colony optimization based approach for single depot multiple travelling salesman problem in Type-2 fuzzy environment. Artificial Intelligence Review, 56(2), 965 –993.
Changdar, C., Pal, R. K., & Mahapatra, G. S. (2017). A genetic ant colony optimization based algorithm for solid multiple travelling salesman problem in fuzzy rough environment. Soft Computing, 21(16), 4661–4675.
Cheikhrouhou, O., & Khoufi, I. (2021). A comprehensive survey on the Multiple Traveling Salesman Problem: Applications, approaches and taxonomy. Computer Science Review, 40, 100369.
Garey, M. R., & Johnson, D. S. (1979). A guide to the theory of NP-completeness, Computers and Intractability. New York.
Goldenberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Newyork.
Gomes, D. E., Iglésias, M. I. D., Proença, A. P., Lima, T. M., & Gaspar, P. D. (2021). Applying a genetic algorithm to a m-TSP: case study of a decision support system for optimizing a beverage logistics vehicles routing problem. Electronics, 10(18), 2298.
Harrath, Y., Salman, A. F., Alqaddoumi, A., Hasan, H., & Radhi, A. (2019). A novel hybrid approach for solving the multiple traveling salesman problem. Arab Journal of Basic and applied sciences, 26(1), 103 –112.
Jiang, C., Wan, Z., & Peng, Z. (2020). A new efficient hybrid algorithm for large scale multiple traveling salesman problems. Expert Systems with Applications, 139, 112867.
Kaliaperumal, R., Ramalingam, A., & Sripriya, J. (2015, March). A modified two part chromosome crossover for solving MTSP using genetic algorithms. In Proceedings of the 2015 International Conference on Advanced Research in Computer Science Engineering & Technology (ICARCSET 2015) (pp. 1–4).
Kara, I., & Bektas, T. (2006). Integer linear programming formulations of multiple salesman problems and its variations. European Journal of Operational Research, 174(3), 1449–1458.
Khan, F. H., Khan, N., Inayatullah, S., &Nizami, S. T. (2009). Solving TSP problem by using genetic algorithm. International Journal of Basic & Applied Sciences, 9(10), 79 –88.
Király, A., & Abonyi, J. (2011). Optimization of multiple traveling salesman problem by a novel representation based genetic algorithm. In Intelligent computational optimization in engineering (pp. 241–269). Springer, Berlin, Heidelberg.
Király, A., & Abonyi, J. (2015). Redesign of the supply of mobile mechanics based on a novel genetic optimization algorithm using Google Maps API. Engineering Applications of Artificial Intelligence, 38, 122 –130.
Larranaga, P., Kuijpers, C. M. H., Murga, R. H., Inza, I., &Dizdarevic, S. (1999). Genetic algorithms for the travelling salesman problem: A review of representations and operators. Artificial Intelligence Review, 13(2), 129 –170.
Lou, P., Xu, K., Yan, J., & Xiao, Z. (2021, April). An Improved Partheno-Genetic Algorithm for Open Path Multi-Depot Multiple Traveling Salesman Problem. In Journal of Physics: Conference Series (Vol. 1848, No. 1, p. 012002). IOP Publishing.
Malik, W., Rathinam, S., & Darbha, S. (2007). An approximation algorithm for a symmetric generalized multiple depot, multiple travelling salesman problem. Operations Research Letters, 35(6), 747–753.
Mzili, I., Mzili, T., & Riffi, M. E. (2023). Efficient routing optimization with discrete penguins search algorithm for MTSP. Decision Making: Applications in Management and Engineering, 6(1), 730 –743.
Öncan, T. (2007). A survey of the generalized assignment problem and its applications. INFOR: Information Systems and Operational Research, 45(3), 123–141.
Riazi, A. (2019). Genetic algorithm and a double-chromosome implementation to the traveling salesman problem. SN Applied Sciences, 1(11), 1397.
Sarin, S. C., Sherali, H. D., Judd, J. D., & Tsai, P. F. J. (2014). Multiple asymmetric traveling salesman problem with and without precedence constraints: Performance comparison of alternative formulations. Computers & Operations Research, 51, 64–89.
Shuai, Y., Yunfeng, S., & Kai, Z. (2019). An effective method for solving multiple travelling salesman problem based on NSGA-II. Systems Science & Control Engineering, 7(2), 108 –116.
Singamsetty, P., & Thenepalle, J. (2021). An efficient genetic algorithm for solving open multiple travelling salesman problem with load balancing constraint. Decision Science Letters, 10(4), 525–534.
Singh, D. R., Singh, M. K., Singh, T., & Prasad, R. (2018). Genetic algorithm for solving multiple traveling salesman problem using a new crossover and population generation. Computación y Sistemas, 22(2), 491 –503.
Sofge, D., Schultz, A., & De Jong, K. (2002, April). Evolutionary computational approaches to solving the multiple traveling salesman problem using a neighborhood attractor schema. In Workshops on Applications of Evolutionary Computation (pp. 153–162). Springer, Berlin, Heidelberg.
Tang, L., Liu, J., Rong, A., & Yang, Z. (2000). A multiple traveling salesman problem model for hot rolling scheduling in Shanghai Baoshan Iron & Steel Complex. European Journal of Operational Research, 124(2), 267–282.
Thenepalle, J., & Singamsetty, P. (2019). An open close multiple travelling salesman problem with single depot. Decision Science Letters, 8(2), 121–136.
Tjandra, S. S., Setiawan, F., & Salsabila, H. (2022). Application of Genetic Algorithms to Solve MTSP Problems with Priority (Case Study at the Jakarta Street Lighting Service). Journal on Optimization of Systems in Industries, 21(2), 75 –86.
Wang, M., Ma, T., Li, G., Zhai, X., & Qiao, S. (2020). Ant colony optimization with an improved pheromone model for solving MTSP with capacity and time window constraint. IEEE Access, 8, 106872–106879.
Wang, Y. D., Lu, X. C., & Shen, J. R. (2021). Improved Genetic Algorithm (VNS-GA) using polar coordinate classification for workload balanced multiple Traveling Salesman Problem (mTSP). Advances in Production Engineering & Management, 16(2), 173 –184.
Xu, X., Yuan, H., Liptrott, M., & Trovati, M. (2018). Two phase heuristic algorithm for the multiple-travelling salesman problem. Soft Computing, 22(19), 6567–6581.
Yan, X., Zhang, C., Luo, W., Li, W., Chen, W., & Liu, H. (2012). Solve traveling salesman problem using particle swarm optimization algorithm. International Journal of Computer Science Issues (IJCSI), 9(6), 264.
Yousefikhoshbakht, M., Didehvar, F., & Rahmati, F. (2013). Modification of the ant colony optimization for solving the multiple traveling salesman problem. Romanian Journal of Information Science and Technology, 16(1), 65–80.
Yuan, S., Skinner, B., Huang, S., & Liu, D. (2013). A new crossover approach for solving the multiple travelling salesman problem using genetic algorithms. European journal of operational research, 228(1), 72–82.