How to cite this paper
Poohoi, R., Puntusavase, K & Ohmori, S. (2024). Stas crossover with K-mean clustering for vehicle routing problem with time window.Decision Science Letters , 13(3), 525-534.
Refrences
Ahmed, Z.H., Maleki, F., Yousefikhoshbakht, M., & Haron, H. (2023). Solving the vehicle routing problem with time windows using modified football game algorithm. Egyptian Informatics Journal, 24, 1 – 13.
Alfiyatin, A.N., Mahmudy, W.F., & Anggodo, Y.P. (2018). K-Means Clustering and Genetic Algorithm to Solve Vehicle Routing Problem with Time Windows Problem. Indonesian Journal of Electrical Engineering and Computer Science, 11(2), 462 – 468, ISSN: 2502-4752, doi: 10.11591/ijeecs.v11.i2.
Amini, Sh. (2011). A Novel PSO For Solving The VRPTW With Real Case Study. Proceedings of the 2011 International Conference on Industrial Engineering and Operations Management, 562 – 567, ISBN: 978-0-9808251-0-7.
Ariyani, A.K., Mahmudy, W.F., & Anggodo, Y.P. (2018). Hybrid Genetic Algorithms and Simulated Annealing for Multi-trip Vehicle Routing Problem with Time Windows. International Journal of Electrical and Computer Engineering (IJECE), 8(6), 4713 – 4723, ISSN: 2088-8708, doi: 10.11591/ijece.v8i6.
Berger, J., Barkaoui, M., & Bräysy,O. (2001). A Parallel Hybrid Genetic Algorithm for the Vehicle Routing Problem with Time Windows. Working paper, Defense Research Establishment Valcartier, Canada.
Cordeau, J.-F., Laporte, G., & Mercier, A. (2000). A Unified Tabu Search Heuristic for Vehicle Routing Problems with Time Windows. Working Paper CRT-00-03, Centre for Research on Transportation, Montreal, Canada.
Gambardella, L.M. (2000). MACS-VRPTW: A Multiple Ant Colony Optimization System for Vehicle Routing Problems with Time Windows (VRPTW). Retrieved from https://people.idsia.ch/~luca/macs-vrptw/solutions/welcome.htm
Gambardella, L. M., Taillard, E., & Agazzi, G. (1999). MACS-VRPTW: A Multiple Ant Colony System for Vehicle Routing Problems with Time Windows. in New Ideas in Optimization, Corne, D., Dorigo, M., & Glover, F. (eds), 63-76, McGraw-Hill, London.
Ghani, N.E.A., Shariff, S. S.R., & Zahari, S.M. (2016). An Alternative Algorithm for Vehicle Routing Problem with Time Windows for Daily Deliveries. Advances in Pure Mathematics, 6, 342-350. http://dx.doi.org/10.4236/apm.2016.65025
Goldberg, D.E., & Holland, J.H. (1988). Genetic Algorithms and Machine Learning. Machine Learning, 3, 95 – 99.
Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization & Machine Learning. Pearson Education Pvt. Ltd., Singapore
Holland, J.H. (1992). Genetic Algorithm. Scientific American, 267(1), 66 – 73.
Homberger, J., & Gehring, H. (1999). Two Evolutionary Metaheuristics For The Vehicle Routing Problem With Time Windows. Infor, 37, 297-318.
Kallehauge, B., Larsen, J., Madsen O.B.G., & Solomon, M.M. (2005). The Vehicle Routing Problem with Time Windows. Column Generation, 67 – 98, Springer, New York. ISBN 978-0-387-25485-2.
Kinoshita, T., & Uchiya, T. (2021). Diversity Maintenance Method Using Multiple Crossover in Genetic Algorithm for VRPTW. 2021 IEEE 10th Global Conference on Consumer Electronics (GCCE), 563 – 565, doi: 10.1109/GCCE53005.2021.9621864.
Kosolsombat, S., Ratanavilisagul, Ch. (2022). Modified ant colony optimization with selecting and elimination customer and re-initialization for VRPTW. Bulletin of Electrical Engineering and Informatics, 11(6), 3471 – 3482, ISSN: 2302-9285, doi: 10.11591/eei.v11i6.3943.
May, A.T., Jariyavajee, Ch., & Polvichai, J. (2021). An Improved Genetic Algorithm for Vehicle Routing Problem with Hard Time Windows. Proc. of the International Conference on Electrical, Computer and Energy Technologies (ICECET), 1 – 6, DOI: 10.1109/ICECET52533.2021.9698698.
Mester, D., Braysy, O., & Dullaert, W. (2007). A multi-parametric evolution strategies algorithm for vehicle routing problems. Expert Systems with Applications, 32(2), 508-517. Advance online publication. https://doi.org/10.1016/j.eswa.2005.12.014
Mohammadi, M., & Mahmoodian, N. (2022). A Simulated Annealing Approach (SA) to Vehicle Routing Problem with Time Windows (VRPTW). 2022 8th International Conference on Control, Instrumentation and Automation (ICCIA), 1 – 6, doi: 10.1109/ICCIA54998.2022.9737187.
Rochat, Y., & Taillard, É.D. (1995) Probabilistic diversification and intensification in local search for vehicle routing. J Heuristics 1, 147–167. https://doi.org/10.1007/BF02430370
Rousseau, LM., Gendreau, M. & Pesant, G. (2002). Using Constraint-Based Operators to Solve the Vehicle Routing Problem with Time Windows. Journal of Heuristics 8, 43–58, https://doi.org/10.1023/A:1013661617536
Shaw, P. (1997). A new local search algorithm providing high quality solutions to vehicle routing problems. APES Group, Dept of Computer Science, University of Strathclyde, Glasgow, Scotland, UK, 46.
Shi, W., & Weise, T. (2013). An Initialized ACO for the VRPTW. Intelligent Data Engineering and Automated Learning – IDEAL 2013, LNCS 8206, 93–100, Springer-Verlag Berlin Heidelberg 2013.
Solomon, M.M. (1987). Algorithms for the vehicle routing and scheduling problems with time window constraints. Operations Research, 35, 254 – 265.
Solomon, M.M., & Desrosiers, J. (1998). Time Window Constrained Routing and Scheduling Problems. Transportation Science,22(1), 1 – 11.
Solomon, M.M. (2005). Best Known Solutions Identified by Heuristics, Northeastern University, Massachusetts, Boston. Retrieved from http://web.cba.neu.edu/~solomon/ heuristic.htm.
Taillard, E., Badeau, P., Gendreau, M., Guertin, F., & Potvin, J.-Y. (1997. A Tabu Search Heuristic for the Vehicle Routing Problem with Soft Time Windows. Transportation Science, 31(2), 170-186.https://doi.org/10.1287/trsc.31.2.170
Thangiah, S.R. (1995). Vehicle Routing with Time Windows using Genetic Algorithms. Applications Handbook of Genetic Algorithms: New Frontiers, 253 – 278, doi:10.1201/9781420050073.ch11
Villalba, A.F.L., & Rotta, E.C.G.L. (2022). Clustering and heuristics algorithm for the vehicle routing problem with time windows. International Journal of Industrial Engineering Computations, 13, 165 – 184, doi: 10.5267/j.ijiec.2021.12.002.
Yousefi, H., Tavakkoli-Moghaddam, R., Oliaei, M.T.B., Mohammadi, M., & Mozaffari, A. (2017). Solving a bi-objective vehicle routing problem under uncertainty by a revised multi choice goal programming approach. International Journal of Industrial Engineering Computations, 8, 283 – 302, doi: 10.5267/j.ijiec.2017.1.003.
Alfiyatin, A.N., Mahmudy, W.F., & Anggodo, Y.P. (2018). K-Means Clustering and Genetic Algorithm to Solve Vehicle Routing Problem with Time Windows Problem. Indonesian Journal of Electrical Engineering and Computer Science, 11(2), 462 – 468, ISSN: 2502-4752, doi: 10.11591/ijeecs.v11.i2.
Amini, Sh. (2011). A Novel PSO For Solving The VRPTW With Real Case Study. Proceedings of the 2011 International Conference on Industrial Engineering and Operations Management, 562 – 567, ISBN: 978-0-9808251-0-7.
Ariyani, A.K., Mahmudy, W.F., & Anggodo, Y.P. (2018). Hybrid Genetic Algorithms and Simulated Annealing for Multi-trip Vehicle Routing Problem with Time Windows. International Journal of Electrical and Computer Engineering (IJECE), 8(6), 4713 – 4723, ISSN: 2088-8708, doi: 10.11591/ijece.v8i6.
Berger, J., Barkaoui, M., & Bräysy,O. (2001). A Parallel Hybrid Genetic Algorithm for the Vehicle Routing Problem with Time Windows. Working paper, Defense Research Establishment Valcartier, Canada.
Cordeau, J.-F., Laporte, G., & Mercier, A. (2000). A Unified Tabu Search Heuristic for Vehicle Routing Problems with Time Windows. Working Paper CRT-00-03, Centre for Research on Transportation, Montreal, Canada.
Gambardella, L.M. (2000). MACS-VRPTW: A Multiple Ant Colony Optimization System for Vehicle Routing Problems with Time Windows (VRPTW). Retrieved from https://people.idsia.ch/~luca/macs-vrptw/solutions/welcome.htm
Gambardella, L. M., Taillard, E., & Agazzi, G. (1999). MACS-VRPTW: A Multiple Ant Colony System for Vehicle Routing Problems with Time Windows. in New Ideas in Optimization, Corne, D., Dorigo, M., & Glover, F. (eds), 63-76, McGraw-Hill, London.
Ghani, N.E.A., Shariff, S. S.R., & Zahari, S.M. (2016). An Alternative Algorithm for Vehicle Routing Problem with Time Windows for Daily Deliveries. Advances in Pure Mathematics, 6, 342-350. http://dx.doi.org/10.4236/apm.2016.65025
Goldberg, D.E., & Holland, J.H. (1988). Genetic Algorithms and Machine Learning. Machine Learning, 3, 95 – 99.
Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization & Machine Learning. Pearson Education Pvt. Ltd., Singapore
Holland, J.H. (1992). Genetic Algorithm. Scientific American, 267(1), 66 – 73.
Homberger, J., & Gehring, H. (1999). Two Evolutionary Metaheuristics For The Vehicle Routing Problem With Time Windows. Infor, 37, 297-318.
Kallehauge, B., Larsen, J., Madsen O.B.G., & Solomon, M.M. (2005). The Vehicle Routing Problem with Time Windows. Column Generation, 67 – 98, Springer, New York. ISBN 978-0-387-25485-2.
Kinoshita, T., & Uchiya, T. (2021). Diversity Maintenance Method Using Multiple Crossover in Genetic Algorithm for VRPTW. 2021 IEEE 10th Global Conference on Consumer Electronics (GCCE), 563 – 565, doi: 10.1109/GCCE53005.2021.9621864.
Kosolsombat, S., Ratanavilisagul, Ch. (2022). Modified ant colony optimization with selecting and elimination customer and re-initialization for VRPTW. Bulletin of Electrical Engineering and Informatics, 11(6), 3471 – 3482, ISSN: 2302-9285, doi: 10.11591/eei.v11i6.3943.
May, A.T., Jariyavajee, Ch., & Polvichai, J. (2021). An Improved Genetic Algorithm for Vehicle Routing Problem with Hard Time Windows. Proc. of the International Conference on Electrical, Computer and Energy Technologies (ICECET), 1 – 6, DOI: 10.1109/ICECET52533.2021.9698698.
Mester, D., Braysy, O., & Dullaert, W. (2007). A multi-parametric evolution strategies algorithm for vehicle routing problems. Expert Systems with Applications, 32(2), 508-517. Advance online publication. https://doi.org/10.1016/j.eswa.2005.12.014
Mohammadi, M., & Mahmoodian, N. (2022). A Simulated Annealing Approach (SA) to Vehicle Routing Problem with Time Windows (VRPTW). 2022 8th International Conference on Control, Instrumentation and Automation (ICCIA), 1 – 6, doi: 10.1109/ICCIA54998.2022.9737187.
Rochat, Y., & Taillard, É.D. (1995) Probabilistic diversification and intensification in local search for vehicle routing. J Heuristics 1, 147–167. https://doi.org/10.1007/BF02430370
Rousseau, LM., Gendreau, M. & Pesant, G. (2002). Using Constraint-Based Operators to Solve the Vehicle Routing Problem with Time Windows. Journal of Heuristics 8, 43–58, https://doi.org/10.1023/A:1013661617536
Shaw, P. (1997). A new local search algorithm providing high quality solutions to vehicle routing problems. APES Group, Dept of Computer Science, University of Strathclyde, Glasgow, Scotland, UK, 46.
Shi, W., & Weise, T. (2013). An Initialized ACO for the VRPTW. Intelligent Data Engineering and Automated Learning – IDEAL 2013, LNCS 8206, 93–100, Springer-Verlag Berlin Heidelberg 2013.
Solomon, M.M. (1987). Algorithms for the vehicle routing and scheduling problems with time window constraints. Operations Research, 35, 254 – 265.
Solomon, M.M., & Desrosiers, J. (1998). Time Window Constrained Routing and Scheduling Problems. Transportation Science,22(1), 1 – 11.
Solomon, M.M. (2005). Best Known Solutions Identified by Heuristics, Northeastern University, Massachusetts, Boston. Retrieved from http://web.cba.neu.edu/~solomon/ heuristic.htm.
Taillard, E., Badeau, P., Gendreau, M., Guertin, F., & Potvin, J.-Y. (1997. A Tabu Search Heuristic for the Vehicle Routing Problem with Soft Time Windows. Transportation Science, 31(2), 170-186.https://doi.org/10.1287/trsc.31.2.170
Thangiah, S.R. (1995). Vehicle Routing with Time Windows using Genetic Algorithms. Applications Handbook of Genetic Algorithms: New Frontiers, 253 – 278, doi:10.1201/9781420050073.ch11
Villalba, A.F.L., & Rotta, E.C.G.L. (2022). Clustering and heuristics algorithm for the vehicle routing problem with time windows. International Journal of Industrial Engineering Computations, 13, 165 – 184, doi: 10.5267/j.ijiec.2021.12.002.
Yousefi, H., Tavakkoli-Moghaddam, R., Oliaei, M.T.B., Mohammadi, M., & Mozaffari, A. (2017). Solving a bi-objective vehicle routing problem under uncertainty by a revised multi choice goal programming approach. International Journal of Industrial Engineering Computations, 8, 283 – 302, doi: 10.5267/j.ijiec.2017.1.003.