How to cite this paper
Yousefi, H., Tavakkoli-Moghaddam, R., Oliaei, M., 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(3), 283-302.
Refrences
Afshar-Bakeshloo, M, Mehrabi, A, Safari, H, Maleki, M, & Jolai, F. (2016). A green vehicle routing problem with customer satisfaction criteria. Journal of Industrial Engineering International, 12(4), 529-544.
Aouni, B., Martel, J. M., & Hassaine, A. (2009). Fuzzy goal programming model: an overview of the current state‐of‐the art. Journal of Multi‐Criteria Decision Analysis, 16(5‐6), 149-161.
Archetti, C., Speranza, M. G., & Vigo, D. (2014). Vehicle routing problems with profits. Vehicle Routing: Problems, Methods, and Applications, 18, 273.
Barkaoui, M., Berger, J., & Boukhtouta, A. (2015). Customer satisfaction in dynamic vehicle routing problem with time windows. Applied Soft Computing, 35, 423-432.
Belhaiza, S., Hansen, P., & Laporte, G. (2014). A hybrid variable neighborhood tabu search heuristic for the vehicle routing problem with multiple time windows. Computers & Operations Research, 52, 269-281.
Calvete, H. I., Galé, C., Oliveros, M. J., & Sánchez-Valverde, B. (2007). A goal programming approach to vehicle routing problems with soft time windows. European Journal of Operational Research, 177(3), 1720-1733.
Castro-Gutierrez, J., Landa-Silva, D., & Pérez, J. M. (2011, October). Nature of real-world multi-objective vehicle routing with evolutionary algorithms. In Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on (pp. 257-264). IEEE.
Chang, C. T. (2007). Multi-choice goal programming. Omega, 35(4), 389-396.
Chang, C. T. (2008). Revised multi-choice goal programming. Applied Mathematical Modelling, 32(12), 2587-2595.
Charnes, A., & Cooper, W. W. (1957). Management models and industrial applications of linear programming. Management Science, 4(1), 38-91.
da Silva, A. F., Marins, F. A. S., & Montevechi, J. A. B. (2013). Multi-choice mixed integer goal programming optimization for real problems in a sugar and ethanol milling company. Applied Mathematical Modelling, 37(9), 6146-6162.
Dantzig, G. B., & Ramser, J. H. (1959). The truck dispatching problem. Management science, 6(1), 80-91.
Delage, E., Bostel, N., Dejax, P., & Gendreau, M. (2010). Re-optimization of technician tours in dynamic environments with stochastic service time. Rapport de stage du Master ORO.
Dubois, D., & Perny, P. (2016). A Review of Fuzzy Sets in Decision Sciences: Achievements, Limitations and Perspectives. In Multiple Criteria Decision Analysis (pp. 637-691). Springer New York.
Eksioglu, B., Vural, A. V., & Reisman, A. (2009). The vehicle routing problem: A taxonomic review. Computers & Industrial Engineering, 57(4), 1472-1483.
Forslund, H., & Jonsson, P. (2010). Integrating the performance management process of on-time delivery with suppliers. International Journal of Logistics: Research and Applications, 13(3), 225-241.
Gehring, H., & Homberger, J. (2002). Parallelization of a two-phase metaheuristic for routing problems with time windows. Journal of heuristics, 8(3), 251-276.
Ghannadpour, S. F., Noori, S., & Tavakkoli-Moghaddam, R. (2014). A multi-objective vehicle routing and scheduling problem with uncertainty in customers’ request and priority. Journal of Combinatorial Optimization, 28(2), 414-446.
Ghoseiri, K., & Ghannadpour, S. F. (2010). Multi-objective vehicle routing problem with time windows using goal programming and genetic algorithm. Applied Soft Computing, 10(4), 1096-1107.
Goncalves, G., Hsu, T., & Xu, J. (2009). Vehicle routing problem with time windows and fuzzy demands: an approach based on the possibility theory. International Journal of Advanced Operations Management, 1(4), 312-330.
Hashimoto, H., Ibaraki, T., Imahori, S., & Yagiura, M. (2006). The vehicle routing problem with flexible time windows and traveling times. Discrete Applied Mathematics, 154(16), 2271-2290.
HO, C. J. (1989). Evaluating the impact of operating environments on MRP system nervousness. The International Journal of Production Research, 27(7), 1115-1135.
Hong, S. C., & Park, Y. B. (1999). A heuristic for bi-objective vehicle routing with time window constraints. International Journal of Production Economics, 62(3), 249-258.
Huang, M., & Hu, X. (2012). Large scale vehicle routing problem: An overview of algorithms and an intelligent procedure. Int. J. Innov. Comput. Inf. Control, 8, 5809-5819.
Jiménez, M., Arenas, M., Bilbao, A., & Rodrı, M. V. (2007). Linear programming with fuzzy parameters: an interactive method resolution. European Journal of Operational Research, 177(3), 1599-1609.
Jones, D. F., Mirrazavi, S. K., & Tamiz, M. (2002). Multi-objective meta-heuristics: An overview of the current state-of-the-art. European journal of operational research, 137(1), 1-9.
Jones, D., & Tamiz, M. (2016). A review of goal programming. In Multiple Criteria Decision Analysis (pp. 903-926). Springer New York.
Kirkpatrick, S. (1984). Optimization by simulated annealing: Quantitative studies. Journal of statistical physics, 34(5-6), 975-986.
Lai, K. K., Liu, B., & Peng, J. (2003). Vehicle routing problem with fuzzy travel times and its genetic algorithm. Technical Report.
Lai, Y. J., & Hwang, C. L. (1992). A new approach to some possibilistic linear programming problems. Fuzzy sets and systems, 49(2), 121-133.
Lee, C., Lee, K., & Park, S. (2012). Robust vehicle routing problem with deadlines and travel time/demand uncertainty. Journal of the Operational Research Society, 63(9), 1294-1306.
Liao, C. N., & Kao, H. P. (2010). Supplier selection model using Taguchi loss function, analytical hierarchy process and multi-choice goal programming. Computers & Industrial Engineering, 58(4), 571-577.
Melián-Batista, B., De Santiago, A., AngelBello, F., & Alvarez, A. (2014). A bi-objective vehicle routing problem with time windows: A real case in Tenerife. Applied Soft Computing, 17, 140-152.
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The journal of chemical physics, 21(6), 1087-1092.
Moghadam, B., & Seyedhosseini, S. (2010). A particle swarm approach to solve vehicle routing problem with uncertain demand: A drug distribution case study. International Journal of Industrial Engineering Computations, 1(1), 55-64.
Montoya-Torres, J. R., Franco, J. L., Isaza, S. N., Jiménez, H. F., & Herazo-Padilla, N. (2015). A literature review on the vehicle routing problem with multiple depots. Computers & Industrial Engineering, 79, 115-129.
Ombuki-Berman, B., & Hanshar, F. T. (2009). Using genetic algorithms for multi-depot vehicle routing. In Bio-inspired algorithms for the vehicle routing problem (pp. 77-99). Springer Berlin Heidelberg.
Ombuki, B., Ross, B. J., & Hanshar, F. (2006). Multi-objective genetic algorithms for vehicle routing problem with time windows. Applied Intelligence, 24(1), 17-30.
Pillac, V., Gendreau, M., Guéret, C., & Medaglia, A. L. (2013). A review of dynamic vehicle routing problems. European Journal of Operational Research, 225(1), 1-11.
Pishvaee, M. S., & Razmi, J. (2012). Environmental supply chain network design using multi-objective fuzzy mathematical programming. Applied Mathematical Modelling, 36(8), 3433-3446.
Rath, S., Gendreau, M., & Gutjahr, W. J. (2015). Bi‐objective stochastic programming models for determining depot locations in disaster relief operations. International Transactions in Operational Research.
Rincon-Garcia, N., Waterson, B., & Cherrett, T. (2017). A hybrid metaheuristic for the time-dependent vehicle routing problem with hard time windows. International Journal of Industrial Engineering Computations, 8(1), 141-160.
Chávez, J., Escobar, J & Echeverri, M. (2016). A multi-objective Pareto ant colony algorithm for the Multi-Depot Vehicle Routing problem with Backhauls.International Journal of Industrial Engineering Computations , 7(1), 35-48.
Sivaramkumar, V., Thansekhar, M. R., Saravanan, R., & Amali, S. M. J. (2015). Multi-objective vehicle routing problem with time windows: Improving customer satisfaction by considering gap time. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 0954405415586608.
Subramanian, P., Ramkumar, N., Narendran, T. T., & Ganesh, K. (2013). PRISM: PRIority based SiMulated annealing for a closed loop supply chain network design problem. Applied Soft Computing, 13(2), 1121-1135.
Taş, D., Dellaert, N., Van Woensel, T., & De Kok, T. (2013). Vehicle routing problem with stochastic travel times including soft time windows and service costs. Computers & Operations Research, 40(1), 214-224.
Torabi, S. A., & Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets and Systems, 159(2), 193-214.
Zheng, Y., & Liu, B. (2006). Fuzzy vehicle routing model with credibility measure and its hybrid intelligent algorithm. Applied mathematics and computation, 176(2), 673-683.
Zografos, K. G., & Androutsopoulos, K. N. (2004). A heuristic algorithm for solving hazardous materials distribution problems. European Journal of Operational Research, 152(2), 507-519.
Aouni, B., Martel, J. M., & Hassaine, A. (2009). Fuzzy goal programming model: an overview of the current state‐of‐the art. Journal of Multi‐Criteria Decision Analysis, 16(5‐6), 149-161.
Archetti, C., Speranza, M. G., & Vigo, D. (2014). Vehicle routing problems with profits. Vehicle Routing: Problems, Methods, and Applications, 18, 273.
Barkaoui, M., Berger, J., & Boukhtouta, A. (2015). Customer satisfaction in dynamic vehicle routing problem with time windows. Applied Soft Computing, 35, 423-432.
Belhaiza, S., Hansen, P., & Laporte, G. (2014). A hybrid variable neighborhood tabu search heuristic for the vehicle routing problem with multiple time windows. Computers & Operations Research, 52, 269-281.
Calvete, H. I., Galé, C., Oliveros, M. J., & Sánchez-Valverde, B. (2007). A goal programming approach to vehicle routing problems with soft time windows. European Journal of Operational Research, 177(3), 1720-1733.
Castro-Gutierrez, J., Landa-Silva, D., & Pérez, J. M. (2011, October). Nature of real-world multi-objective vehicle routing with evolutionary algorithms. In Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on (pp. 257-264). IEEE.
Chang, C. T. (2007). Multi-choice goal programming. Omega, 35(4), 389-396.
Chang, C. T. (2008). Revised multi-choice goal programming. Applied Mathematical Modelling, 32(12), 2587-2595.
Charnes, A., & Cooper, W. W. (1957). Management models and industrial applications of linear programming. Management Science, 4(1), 38-91.
da Silva, A. F., Marins, F. A. S., & Montevechi, J. A. B. (2013). Multi-choice mixed integer goal programming optimization for real problems in a sugar and ethanol milling company. Applied Mathematical Modelling, 37(9), 6146-6162.
Dantzig, G. B., & Ramser, J. H. (1959). The truck dispatching problem. Management science, 6(1), 80-91.
Delage, E., Bostel, N., Dejax, P., & Gendreau, M. (2010). Re-optimization of technician tours in dynamic environments with stochastic service time. Rapport de stage du Master ORO.
Dubois, D., & Perny, P. (2016). A Review of Fuzzy Sets in Decision Sciences: Achievements, Limitations and Perspectives. In Multiple Criteria Decision Analysis (pp. 637-691). Springer New York.
Eksioglu, B., Vural, A. V., & Reisman, A. (2009). The vehicle routing problem: A taxonomic review. Computers & Industrial Engineering, 57(4), 1472-1483.
Forslund, H., & Jonsson, P. (2010). Integrating the performance management process of on-time delivery with suppliers. International Journal of Logistics: Research and Applications, 13(3), 225-241.
Gehring, H., & Homberger, J. (2002). Parallelization of a two-phase metaheuristic for routing problems with time windows. Journal of heuristics, 8(3), 251-276.
Ghannadpour, S. F., Noori, S., & Tavakkoli-Moghaddam, R. (2014). A multi-objective vehicle routing and scheduling problem with uncertainty in customers’ request and priority. Journal of Combinatorial Optimization, 28(2), 414-446.
Ghoseiri, K., & Ghannadpour, S. F. (2010). Multi-objective vehicle routing problem with time windows using goal programming and genetic algorithm. Applied Soft Computing, 10(4), 1096-1107.
Goncalves, G., Hsu, T., & Xu, J. (2009). Vehicle routing problem with time windows and fuzzy demands: an approach based on the possibility theory. International Journal of Advanced Operations Management, 1(4), 312-330.
Hashimoto, H., Ibaraki, T., Imahori, S., & Yagiura, M. (2006). The vehicle routing problem with flexible time windows and traveling times. Discrete Applied Mathematics, 154(16), 2271-2290.
HO, C. J. (1989). Evaluating the impact of operating environments on MRP system nervousness. The International Journal of Production Research, 27(7), 1115-1135.
Hong, S. C., & Park, Y. B. (1999). A heuristic for bi-objective vehicle routing with time window constraints. International Journal of Production Economics, 62(3), 249-258.
Huang, M., & Hu, X. (2012). Large scale vehicle routing problem: An overview of algorithms and an intelligent procedure. Int. J. Innov. Comput. Inf. Control, 8, 5809-5819.
Jiménez, M., Arenas, M., Bilbao, A., & Rodrı, M. V. (2007). Linear programming with fuzzy parameters: an interactive method resolution. European Journal of Operational Research, 177(3), 1599-1609.
Jones, D. F., Mirrazavi, S. K., & Tamiz, M. (2002). Multi-objective meta-heuristics: An overview of the current state-of-the-art. European journal of operational research, 137(1), 1-9.
Jones, D., & Tamiz, M. (2016). A review of goal programming. In Multiple Criteria Decision Analysis (pp. 903-926). Springer New York.
Kirkpatrick, S. (1984). Optimization by simulated annealing: Quantitative studies. Journal of statistical physics, 34(5-6), 975-986.
Lai, K. K., Liu, B., & Peng, J. (2003). Vehicle routing problem with fuzzy travel times and its genetic algorithm. Technical Report.
Lai, Y. J., & Hwang, C. L. (1992). A new approach to some possibilistic linear programming problems. Fuzzy sets and systems, 49(2), 121-133.
Lee, C., Lee, K., & Park, S. (2012). Robust vehicle routing problem with deadlines and travel time/demand uncertainty. Journal of the Operational Research Society, 63(9), 1294-1306.
Liao, C. N., & Kao, H. P. (2010). Supplier selection model using Taguchi loss function, analytical hierarchy process and multi-choice goal programming. Computers & Industrial Engineering, 58(4), 571-577.
Melián-Batista, B., De Santiago, A., AngelBello, F., & Alvarez, A. (2014). A bi-objective vehicle routing problem with time windows: A real case in Tenerife. Applied Soft Computing, 17, 140-152.
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The journal of chemical physics, 21(6), 1087-1092.
Moghadam, B., & Seyedhosseini, S. (2010). A particle swarm approach to solve vehicle routing problem with uncertain demand: A drug distribution case study. International Journal of Industrial Engineering Computations, 1(1), 55-64.
Montoya-Torres, J. R., Franco, J. L., Isaza, S. N., Jiménez, H. F., & Herazo-Padilla, N. (2015). A literature review on the vehicle routing problem with multiple depots. Computers & Industrial Engineering, 79, 115-129.
Ombuki-Berman, B., & Hanshar, F. T. (2009). Using genetic algorithms for multi-depot vehicle routing. In Bio-inspired algorithms for the vehicle routing problem (pp. 77-99). Springer Berlin Heidelberg.
Ombuki, B., Ross, B. J., & Hanshar, F. (2006). Multi-objective genetic algorithms for vehicle routing problem with time windows. Applied Intelligence, 24(1), 17-30.
Pillac, V., Gendreau, M., Guéret, C., & Medaglia, A. L. (2013). A review of dynamic vehicle routing problems. European Journal of Operational Research, 225(1), 1-11.
Pishvaee, M. S., & Razmi, J. (2012). Environmental supply chain network design using multi-objective fuzzy mathematical programming. Applied Mathematical Modelling, 36(8), 3433-3446.
Rath, S., Gendreau, M., & Gutjahr, W. J. (2015). Bi‐objective stochastic programming models for determining depot locations in disaster relief operations. International Transactions in Operational Research.
Rincon-Garcia, N., Waterson, B., & Cherrett, T. (2017). A hybrid metaheuristic for the time-dependent vehicle routing problem with hard time windows. International Journal of Industrial Engineering Computations, 8(1), 141-160.
Chávez, J., Escobar, J & Echeverri, M. (2016). A multi-objective Pareto ant colony algorithm for the Multi-Depot Vehicle Routing problem with Backhauls.International Journal of Industrial Engineering Computations , 7(1), 35-48.
Sivaramkumar, V., Thansekhar, M. R., Saravanan, R., & Amali, S. M. J. (2015). Multi-objective vehicle routing problem with time windows: Improving customer satisfaction by considering gap time. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 0954405415586608.
Subramanian, P., Ramkumar, N., Narendran, T. T., & Ganesh, K. (2013). PRISM: PRIority based SiMulated annealing for a closed loop supply chain network design problem. Applied Soft Computing, 13(2), 1121-1135.
Taş, D., Dellaert, N., Van Woensel, T., & De Kok, T. (2013). Vehicle routing problem with stochastic travel times including soft time windows and service costs. Computers & Operations Research, 40(1), 214-224.
Torabi, S. A., & Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets and Systems, 159(2), 193-214.
Zheng, Y., & Liu, B. (2006). Fuzzy vehicle routing model with credibility measure and its hybrid intelligent algorithm. Applied mathematics and computation, 176(2), 673-683.
Zografos, K. G., & Androutsopoulos, K. N. (2004). A heuristic algorithm for solving hazardous materials distribution problems. European Journal of Operational Research, 152(2), 507-519.