How to cite this paper
Nosrati, M & Khamseh, A. (2020). Bi objective hybrid vehicle routing problem with alternative paths and reliability.Decision Science Letters , 9(2), 145-162.
Refrences
Adenso-Diaz, B., Mena, C., García-Carbajal, S., & Liechty, M. (2012). The impact of supply network characteristics on reliability. Supply Chain Management: An International Journal, 17(3), 263-276.
Affi, M., Derbel, H., & Jarboui, B. (2018). Variable neighborhood search algorithm for the green vehicle routing problem. International Journal of Industrial Engineering Computations, 9(2), 195-204.
Avila, C., & Valdez, F. (2015). An improved simulated annealing algorithm for the optimization of mathematical functions. In Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization (pp. 241-251). Springer, Cham.
Baños, R., Ortega, J., Gil, C., MáRquez, A. L., & De Toro, F. (2013). A hybrid meta-heuristic for multi-objective vehicle routing problems with time windows. Computers & Industrial Engineering, 65(2), 286-296.
Beamon, B. M. (1998). Supply chain design and analysis:: Models and methods. International journal of production economics, 55(3), 281-294.
Bektaş, T., & Laporte, G. (2011). The pollution-routing problem. Transportation Research Part B: Methodological, 45(8), 1232-1250.
Bérubé, J. F., Gendreau, M., & Potvin, J. Y. (2009). An exact ϵ-constraint method for bi-objective combinatorial optimization problems: Application to the Traveling Salesman Problem with Profits. European journal of operational research, 194(1), 39-50.
Bruglieri, M., Pezzella, F., Pisacane, O., & Suraci, S. (2015). A variable neighborhood search branching for the electric vehicle routing problem with time windows. Electronic Notes in Discrete Mathematics, 47, 221-228.
Castillo, O., Neyoy, H., Soria, J., Melin, P., & Valdez, F. (2015). A new approach for dynamic fuzzy logic parameter tuning in ant colony optimization and its application in fuzzy control of a mobile robot. Applied Soft Computing, 28, 150-159.
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.
Chen, P., Huang, H. K., & Dong, X. Y. (2010). Iterated variable neighborhood descent algorithm for the capacitated vehicle routing problem. Expert Systems with Applications, 37(2), 1620-1627.
Ćirović, G., Pamučar, D., & Božanić, D. (2014). Green logistic vehicle routing problem: Routing light delivery vehicles in urban areas using a neuro-fuzzy model. Expert Systems with Applications, 41(9), 4245-4258.
Conrad, R. G., & Figliozzi, M. A. (2011). The recharging vehicle routing problem. In Proceedings of the 2011 industrial engineering research conference (p. 8). IISE Norcross, GA.
Cordeau, J. F., & Maischberger, M. (2012). A parallel iterated tabu search heuristic for vehicle routing problems. Computers & Operations Research, 39(9), 2033-2050.
Dekker, R., Bloemhof, J., & Mallidis, I. (2012). Operations Research for green logistics–An overview of aspects, issues, contributions and challenges. European Journal of Operational Research, 219(3), 671-679.
Demir, E., Bektaş, T., & Laporte, G. (2012). An adaptive large neighborhood search heuristic for the pollution-routing problem. European Journal of Operational Research, 223(2), 346-359.
Demir, E., Bektaş, T., & Laporte, G. (2014). The bi-objective pollution-routing problem. European Journal of Operational Research, 232(3), 464-478.
Dorigo, M., & Stützle, T. (2003). The ant colony optimization metaheuristic: Algorithms, applications, and advances. In Handbook of metaheuristics (pp. 250-285). Springer, Boston, MA.
Eberhart, R., & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science (pp. 39-43). IEEE.
Ehrgott, M., & Gandibleux, X. (2003). Multiobjective combinatorial optimization—theory, methodology, and applications. In Multiple criteria optimization: State of the art annotated bibliographic surveys (pp. 369-444). Springer, Boston, MA.
Erdoğan, S., & Miller-Hooks, E. (2012). A green vehicle routing problem. Transportation Research Part E: Logistics and Transportation Review, 48(1), 100-114.
Farahani, R.Z., Rezapour, S., & Kardar, L. (2011). Logistics operations and management: concepts and models. Elsevier.
Felipe, Á., Ortuño, M. T., Righini, G., & Tirado, G. (2014). A heuristic approach for the green vehicle routing problem with multiple technologies and partial recharges. Transportation Research Part E: Logistics and Transportation Review, 71, 111-128.
Franceschetti, A., Honhon, D., Van Woensel, T., Bektaş, T., & Laporte, G. (2013). The time-dependent pollution-routing problem. Transportation Research Part B: Methodological, 56, 265-293.
Garaix, T., Artigues, C., Feillet, D., & Josselin, D. (2010). Vehicle routing problems with alternative paths: An application to on-demand transportation. European Journal of Operational Research, 204(1), 62-75.
Garcia-Najera, A., & Bullinaria, J. A. (2011). An improved multi-objective evolutionary algorithm for the vehicle routing problem with time windows. Computers & Operations Research, 38(1), 287-300.
Gendreau, M., Laporte, G., & Potvin, J. Y. (2002). Metaheuristics for the capacitated VRP. In The vehicle routing problem (pp. 129-154). Society for Industrial and Applied Mathematics.
Glover, F. (1999). Scatter search and path relinking. New ideas in optimization, 297316.
Glover, F., & Laguna, M. (2013). Tabu search: effective strategies for hard problems in analytics and computational science. Handbook of Combinatorial Optimization, 21, 3261-3362.
Goldberg, D. E. (1989), Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, New York.
Gonçalves, F., Cardoso, S. R., Relvas, S., & Barbosa-Póvoa, A. P. F. D. (2011). Optimization of a distribution network using electric vehicles: A VRP problem. In Proceedings of the IO2011-15 Congresso da associação Portuguesa de Investigação Operacional, Coimbra, Portugal (pp. 18-20).
Ha, C., Jun, H. B., & Ok, C. (2018). A mathematical definition and basic structures for supply chain reliability: A procurement capability perspective. Computers & Industrial Engineering, 120, 334-345.
Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor, Michigan, USA: The University of Michigan Press.
Jozefowiez, N., Semet, F., & Talbi, E. G. (2008). Multi-objective vehicle routing problems. European journal of operational research, 189(2), 293-309.
Jozefowiez, N., Semet, F., & Talbi, E. G. (2009). An evolutionary algorithm for the vehicle routing problem with route balancing. European Journal of Operational Research, 195(3), 761-769.
Kara, I., Kara, B. Y., & Yetis, M. K. (2007, August). Energy minimizing vehicle routing problem. In International Conference on Combinatorial Optimization and Applications(pp. 62-71). Springer, Berlin, Heidelberg.
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680.
Klimov, R., & Merkuryev, Y. (2008). Simulation model for supply chain reliability evaluation. Technological and Economic Development of Economy, 14(3), 300-311.
Kuo, Y. (2010). Using simulated annealing to minimize fuel consumption for the time-dependent vehicle routing problem. Computers & Industrial Engineering, 59(1), 157-165.
Kytöjoki, J., Nuortio, T., Bräysy, O., & Gendreau, M. (2007). An efficient variable neighborhood search heuristic for very large scale vehicle routing problems. Computers & Operations Research, 34(9), 2743-2757.
Laporte, G. (2009). Fifty years of vehicle routing. Transportation Science, 43(4), 408-416.
Lin, C., Choy, K. L., Ho, G. T., Chung, S. H., & Lam, H. Y. (2014). Survey of green vehicle routing problem: past and future trends. Expert Systems With Applications, 41(4), 1118-1138.
Lin, S. W., & Vincent, F. Y. (2012). A simulated annealing heuristic for the team orienteering problem with time windows. European Journal of Operational Research, 217(1), 94-107.
Lin, S. W., Lee, Z. J., Ying, K. C., & Lee, C. Y. (2009). Applying hybrid meta-heuristics for capacitated vehicle routing problem. Expert Systems with Applications, 36(2), 1505-1512.
Lin, S. W., Vincent, F. Y., & Lu, C. C. (2011). A simulated annealing heuristic for the truck and trailer routing problem with time windows. Expert Systems with Applications, 38(12), 15244-15252.
Liu, S., So, K. C., & Zhang, F. (2010). Effect of supply reliability in a retail setting with joint marketing and inventory decisions. Manufacturing & Service Operations Management, 12(1), 19-32.
Lourenço, H. R., Martin, O. C., & Stützle, T. (2003). Iterated local search. In Handbook of metaheuristics (pp. 320-353). Springer, Boston, MA.
Mancini, S. (2017). The hybrid vehicle routing problem. Transportation Research Part C: Emerging Technologies, 78, 1-12.
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.
Mitra, D., Romeo, F., & Sangiovanni-Vincentelli, A. (1986). Convergence and finite-time behavior of simulated annealing. Advances in Applied Probability, 18(3), 747-771.
Mladenović, N., & Hansen, P. (1997). Variable neighborhood search. Computers & Operations Research, 24(11), 1097-1100.
Moscato, P. (1989). On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms. Caltech Concurrent Computation Program.
Moscato, P., & Cotta, C. (2010). A modern introduction to memetic algorithms. In Handbook of metaheuristics (pp. 141-183). Springer, Boston, MA.
Nam, D., & Park, C. H. (2000). Multiobjective simulated annealing: A comparative study to evolutionary algorithms. International Journal of Fuzzy Systems, 2(2), 87-97.
Nishiguchi, T. & Beaudet, A., (1998). The Toyota Group and the Aisin Fire. Sloan Management Review, 40, 49–59.
Norouzi, N., Tavakkoli-Moghaddam, R., Salamatbakhsh, A., & Alinaghian, M. (2009). Solving a novel bi-objective open vehicle routing problem in a competitive situation by multi objective particle swarm optimization. Journal of Applied Operations Research, 1(1), 15-29.
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.
Osman, I. H. (1993). Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem. Annals of Operations Research, 41(4), 421-451.
Ping, L. (2009). Strategy of green logistics and sustainable development. In 2009 International Conference on Information Management, Innovation Management and Industrial Engineering (Vol. 1, pp. 339-342). IEEE.
Poli, R., Kennedy, J., & Blackwell, T. (2007). Particle swarm optimization. Swarm Intelligence, 1(1), 33-57.
Reed, B. D., Smas, M. J., Rzepka, R. A., & Guiffrida, A. L. (2010). Introducing green transportation costs in supply chain modeling. In Proceedings of the First Annual Kent State International Symposium on Green Supply Chains (pp. 189-197).
Resende, M. G., Ribeiro, C. C., Glover, F., & Martí, R. (2010). Scatter search and path-relinking: Fundamentals, advances, and applications. In Handbook of metaheuristics (pp. 87-107). Springer, Boston, MA.
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.
Rochat, Y., & Taillard, É. D. (1995). Probabilistic diversification and intensification in local search for vehicle routing. Journal of Heuristics, 1(1), 147-167.
Ropke, S., & Pisinger, D. (2006). An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transportation Science, 40(4), 455-472.
Schneider, M., Stenger, A., & Goeke, D. (2014). The electric vehicle-routing problem with time windows and recharging stations. Transportation Science, 48(4), 500-520.
Schott, J. R. (1995). Fault Tolerant Design Using Single and Multi-Criteria Genetic Algorithms. Master's Thesis, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology.
Serafini, P. (1994). Simulated annealing for multi objective optimization problems. In Multiple criteria decision making (pp. 283-292). Springer, New York, NY.
Taillard, É. (1993). Parallel iterative search methods for vehicle routing problems. Networks, 23(8), 661-673.
Tan, K. C., Chew, Y. H., & Lee, L. H. (2006). A hybrid multiobjective evolutionary algorithm for solving vehicle routing problem with time windows. Computational Optimization and Applications, 34(1), 115.
Tiwari, A., & Chang, P. C. (2015). A block recombination approach to solve green vehicle routing problem. International Journal of Production Economics, 164, 379-387.
Tseng, Y. Y., Yue, W. L., & Taylor, M. A. (2005). The role of transportation in logistics chain. Eastern Asia Society for Transportation Studies.
Ulungu, E. L., Teghem, J. F. P. H., Fortemps, P. H., & Tuyttens, D. (1999). MOSA method: a tool for solving multiobjective combinatorial optimization problems. Journal of Multi‐Criteria Decision Analysis, 8(4), 221-236.
Vidal, T., Crainic, T. G., Gendreau, M., & Prins, C. (2013). Heuristics for multi-attribute vehicle routing problems: A survey and synthesis. European Journal of Operational Research, 231(1), 1-21.
Vincent, F. Y., Jewpanya, P., & Redi, A. P. (2016). Open vehicle routing problem with cross-docking. Computers & Industrial Engineering, 94, 6-17.
Vincent, F. Y., Lin, S. W., Lee, W., & Ting, C. J. (2010). A simulated annealing heuristic for the capacitated location routing problem. Computers & Industrial Engineering, 58(2), 288-299.
Vincent, F. Y., Redi, A. P., Hidayat, Y. A., & Wibowo, O. J. (2017). A simulated annealing heuristic for the hybrid vehicle routing problem. Applied Soft Computing, 53, 119-132.
Xiao, Y., Zhao, Q., Kaku, I., & Xu, Y. (2012). Development of a fuel consumption optimization model for the capacitated vehicle routing problem. Computers & Operations Research, 39(7), 1419-1431.
Zaitsev, E. (2012). Supply chain reliability modelling. LogForum, 8(1), 61-69.
Zitzler, E. (1999). Evolutionary algorithms for multiobjective optimization: Methods and applications (Vol. 63). Ithaca: Shaker.
Affi, M., Derbel, H., & Jarboui, B. (2018). Variable neighborhood search algorithm for the green vehicle routing problem. International Journal of Industrial Engineering Computations, 9(2), 195-204.
Avila, C., & Valdez, F. (2015). An improved simulated annealing algorithm for the optimization of mathematical functions. In Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization (pp. 241-251). Springer, Cham.
Baños, R., Ortega, J., Gil, C., MáRquez, A. L., & De Toro, F. (2013). A hybrid meta-heuristic for multi-objective vehicle routing problems with time windows. Computers & Industrial Engineering, 65(2), 286-296.
Beamon, B. M. (1998). Supply chain design and analysis:: Models and methods. International journal of production economics, 55(3), 281-294.
Bektaş, T., & Laporte, G. (2011). The pollution-routing problem. Transportation Research Part B: Methodological, 45(8), 1232-1250.
Bérubé, J. F., Gendreau, M., & Potvin, J. Y. (2009). An exact ϵ-constraint method for bi-objective combinatorial optimization problems: Application to the Traveling Salesman Problem with Profits. European journal of operational research, 194(1), 39-50.
Bruglieri, M., Pezzella, F., Pisacane, O., & Suraci, S. (2015). A variable neighborhood search branching for the electric vehicle routing problem with time windows. Electronic Notes in Discrete Mathematics, 47, 221-228.
Castillo, O., Neyoy, H., Soria, J., Melin, P., & Valdez, F. (2015). A new approach for dynamic fuzzy logic parameter tuning in ant colony optimization and its application in fuzzy control of a mobile robot. Applied Soft Computing, 28, 150-159.
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.
Chen, P., Huang, H. K., & Dong, X. Y. (2010). Iterated variable neighborhood descent algorithm for the capacitated vehicle routing problem. Expert Systems with Applications, 37(2), 1620-1627.
Ćirović, G., Pamučar, D., & Božanić, D. (2014). Green logistic vehicle routing problem: Routing light delivery vehicles in urban areas using a neuro-fuzzy model. Expert Systems with Applications, 41(9), 4245-4258.
Conrad, R. G., & Figliozzi, M. A. (2011). The recharging vehicle routing problem. In Proceedings of the 2011 industrial engineering research conference (p. 8). IISE Norcross, GA.
Cordeau, J. F., & Maischberger, M. (2012). A parallel iterated tabu search heuristic for vehicle routing problems. Computers & Operations Research, 39(9), 2033-2050.
Dekker, R., Bloemhof, J., & Mallidis, I. (2012). Operations Research for green logistics–An overview of aspects, issues, contributions and challenges. European Journal of Operational Research, 219(3), 671-679.
Demir, E., Bektaş, T., & Laporte, G. (2012). An adaptive large neighborhood search heuristic for the pollution-routing problem. European Journal of Operational Research, 223(2), 346-359.
Demir, E., Bektaş, T., & Laporte, G. (2014). The bi-objective pollution-routing problem. European Journal of Operational Research, 232(3), 464-478.
Dorigo, M., & Stützle, T. (2003). The ant colony optimization metaheuristic: Algorithms, applications, and advances. In Handbook of metaheuristics (pp. 250-285). Springer, Boston, MA.
Eberhart, R., & Kennedy, J. (1995, October). A new optimizer using particle swarm theory. In MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science (pp. 39-43). IEEE.
Ehrgott, M., & Gandibleux, X. (2003). Multiobjective combinatorial optimization—theory, methodology, and applications. In Multiple criteria optimization: State of the art annotated bibliographic surveys (pp. 369-444). Springer, Boston, MA.
Erdoğan, S., & Miller-Hooks, E. (2012). A green vehicle routing problem. Transportation Research Part E: Logistics and Transportation Review, 48(1), 100-114.
Farahani, R.Z., Rezapour, S., & Kardar, L. (2011). Logistics operations and management: concepts and models. Elsevier.
Felipe, Á., Ortuño, M. T., Righini, G., & Tirado, G. (2014). A heuristic approach for the green vehicle routing problem with multiple technologies and partial recharges. Transportation Research Part E: Logistics and Transportation Review, 71, 111-128.
Franceschetti, A., Honhon, D., Van Woensel, T., Bektaş, T., & Laporte, G. (2013). The time-dependent pollution-routing problem. Transportation Research Part B: Methodological, 56, 265-293.
Garaix, T., Artigues, C., Feillet, D., & Josselin, D. (2010). Vehicle routing problems with alternative paths: An application to on-demand transportation. European Journal of Operational Research, 204(1), 62-75.
Garcia-Najera, A., & Bullinaria, J. A. (2011). An improved multi-objective evolutionary algorithm for the vehicle routing problem with time windows. Computers & Operations Research, 38(1), 287-300.
Gendreau, M., Laporte, G., & Potvin, J. Y. (2002). Metaheuristics for the capacitated VRP. In The vehicle routing problem (pp. 129-154). Society for Industrial and Applied Mathematics.
Glover, F. (1999). Scatter search and path relinking. New ideas in optimization, 297316.
Glover, F., & Laguna, M. (2013). Tabu search: effective strategies for hard problems in analytics and computational science. Handbook of Combinatorial Optimization, 21, 3261-3362.
Goldberg, D. E. (1989), Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, New York.
Gonçalves, F., Cardoso, S. R., Relvas, S., & Barbosa-Póvoa, A. P. F. D. (2011). Optimization of a distribution network using electric vehicles: A VRP problem. In Proceedings of the IO2011-15 Congresso da associação Portuguesa de Investigação Operacional, Coimbra, Portugal (pp. 18-20).
Ha, C., Jun, H. B., & Ok, C. (2018). A mathematical definition and basic structures for supply chain reliability: A procurement capability perspective. Computers & Industrial Engineering, 120, 334-345.
Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor, Michigan, USA: The University of Michigan Press.
Jozefowiez, N., Semet, F., & Talbi, E. G. (2008). Multi-objective vehicle routing problems. European journal of operational research, 189(2), 293-309.
Jozefowiez, N., Semet, F., & Talbi, E. G. (2009). An evolutionary algorithm for the vehicle routing problem with route balancing. European Journal of Operational Research, 195(3), 761-769.
Kara, I., Kara, B. Y., & Yetis, M. K. (2007, August). Energy minimizing vehicle routing problem. In International Conference on Combinatorial Optimization and Applications(pp. 62-71). Springer, Berlin, Heidelberg.
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680.
Klimov, R., & Merkuryev, Y. (2008). Simulation model for supply chain reliability evaluation. Technological and Economic Development of Economy, 14(3), 300-311.
Kuo, Y. (2010). Using simulated annealing to minimize fuel consumption for the time-dependent vehicle routing problem. Computers & Industrial Engineering, 59(1), 157-165.
Kytöjoki, J., Nuortio, T., Bräysy, O., & Gendreau, M. (2007). An efficient variable neighborhood search heuristic for very large scale vehicle routing problems. Computers & Operations Research, 34(9), 2743-2757.
Laporte, G. (2009). Fifty years of vehicle routing. Transportation Science, 43(4), 408-416.
Lin, C., Choy, K. L., Ho, G. T., Chung, S. H., & Lam, H. Y. (2014). Survey of green vehicle routing problem: past and future trends. Expert Systems With Applications, 41(4), 1118-1138.
Lin, S. W., & Vincent, F. Y. (2012). A simulated annealing heuristic for the team orienteering problem with time windows. European Journal of Operational Research, 217(1), 94-107.
Lin, S. W., Lee, Z. J., Ying, K. C., & Lee, C. Y. (2009). Applying hybrid meta-heuristics for capacitated vehicle routing problem. Expert Systems with Applications, 36(2), 1505-1512.
Lin, S. W., Vincent, F. Y., & Lu, C. C. (2011). A simulated annealing heuristic for the truck and trailer routing problem with time windows. Expert Systems with Applications, 38(12), 15244-15252.
Liu, S., So, K. C., & Zhang, F. (2010). Effect of supply reliability in a retail setting with joint marketing and inventory decisions. Manufacturing & Service Operations Management, 12(1), 19-32.
Lourenço, H. R., Martin, O. C., & Stützle, T. (2003). Iterated local search. In Handbook of metaheuristics (pp. 320-353). Springer, Boston, MA.
Mancini, S. (2017). The hybrid vehicle routing problem. Transportation Research Part C: Emerging Technologies, 78, 1-12.
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.
Mitra, D., Romeo, F., & Sangiovanni-Vincentelli, A. (1986). Convergence and finite-time behavior of simulated annealing. Advances in Applied Probability, 18(3), 747-771.
Mladenović, N., & Hansen, P. (1997). Variable neighborhood search. Computers & Operations Research, 24(11), 1097-1100.
Moscato, P. (1989). On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms. Caltech Concurrent Computation Program.
Moscato, P., & Cotta, C. (2010). A modern introduction to memetic algorithms. In Handbook of metaheuristics (pp. 141-183). Springer, Boston, MA.
Nam, D., & Park, C. H. (2000). Multiobjective simulated annealing: A comparative study to evolutionary algorithms. International Journal of Fuzzy Systems, 2(2), 87-97.
Nishiguchi, T. & Beaudet, A., (1998). The Toyota Group and the Aisin Fire. Sloan Management Review, 40, 49–59.
Norouzi, N., Tavakkoli-Moghaddam, R., Salamatbakhsh, A., & Alinaghian, M. (2009). Solving a novel bi-objective open vehicle routing problem in a competitive situation by multi objective particle swarm optimization. Journal of Applied Operations Research, 1(1), 15-29.
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.
Osman, I. H. (1993). Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem. Annals of Operations Research, 41(4), 421-451.
Ping, L. (2009). Strategy of green logistics and sustainable development. In 2009 International Conference on Information Management, Innovation Management and Industrial Engineering (Vol. 1, pp. 339-342). IEEE.
Poli, R., Kennedy, J., & Blackwell, T. (2007). Particle swarm optimization. Swarm Intelligence, 1(1), 33-57.
Reed, B. D., Smas, M. J., Rzepka, R. A., & Guiffrida, A. L. (2010). Introducing green transportation costs in supply chain modeling. In Proceedings of the First Annual Kent State International Symposium on Green Supply Chains (pp. 189-197).
Resende, M. G., Ribeiro, C. C., Glover, F., & Martí, R. (2010). Scatter search and path-relinking: Fundamentals, advances, and applications. In Handbook of metaheuristics (pp. 87-107). Springer, Boston, MA.
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.
Rochat, Y., & Taillard, É. D. (1995). Probabilistic diversification and intensification in local search for vehicle routing. Journal of Heuristics, 1(1), 147-167.
Ropke, S., & Pisinger, D. (2006). An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transportation Science, 40(4), 455-472.
Schneider, M., Stenger, A., & Goeke, D. (2014). The electric vehicle-routing problem with time windows and recharging stations. Transportation Science, 48(4), 500-520.
Schott, J. R. (1995). Fault Tolerant Design Using Single and Multi-Criteria Genetic Algorithms. Master's Thesis, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology.
Serafini, P. (1994). Simulated annealing for multi objective optimization problems. In Multiple criteria decision making (pp. 283-292). Springer, New York, NY.
Taillard, É. (1993). Parallel iterative search methods for vehicle routing problems. Networks, 23(8), 661-673.
Tan, K. C., Chew, Y. H., & Lee, L. H. (2006). A hybrid multiobjective evolutionary algorithm for solving vehicle routing problem with time windows. Computational Optimization and Applications, 34(1), 115.
Tiwari, A., & Chang, P. C. (2015). A block recombination approach to solve green vehicle routing problem. International Journal of Production Economics, 164, 379-387.
Tseng, Y. Y., Yue, W. L., & Taylor, M. A. (2005). The role of transportation in logistics chain. Eastern Asia Society for Transportation Studies.
Ulungu, E. L., Teghem, J. F. P. H., Fortemps, P. H., & Tuyttens, D. (1999). MOSA method: a tool for solving multiobjective combinatorial optimization problems. Journal of Multi‐Criteria Decision Analysis, 8(4), 221-236.
Vidal, T., Crainic, T. G., Gendreau, M., & Prins, C. (2013). Heuristics for multi-attribute vehicle routing problems: A survey and synthesis. European Journal of Operational Research, 231(1), 1-21.
Vincent, F. Y., Jewpanya, P., & Redi, A. P. (2016). Open vehicle routing problem with cross-docking. Computers & Industrial Engineering, 94, 6-17.
Vincent, F. Y., Lin, S. W., Lee, W., & Ting, C. J. (2010). A simulated annealing heuristic for the capacitated location routing problem. Computers & Industrial Engineering, 58(2), 288-299.
Vincent, F. Y., Redi, A. P., Hidayat, Y. A., & Wibowo, O. J. (2017). A simulated annealing heuristic for the hybrid vehicle routing problem. Applied Soft Computing, 53, 119-132.
Xiao, Y., Zhao, Q., Kaku, I., & Xu, Y. (2012). Development of a fuel consumption optimization model for the capacitated vehicle routing problem. Computers & Operations Research, 39(7), 1419-1431.
Zaitsev, E. (2012). Supply chain reliability modelling. LogForum, 8(1), 61-69.
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