A polar-based guided multi-objective evolutionary algorithm to search for optimal solutions interested by decision-makers in a logistics network design problem

Branke, J., Kaubler, T., & Schmeck, H. (2000). Guiding multi-objective evolutionary algorithms toward interesting regions. Germany: Institute AIFB, University of Karlsruhe. Branke, J., Kaussler, T., & Schmeck, H. (2001). Guidance in evolutionary multi-objective optimization. Advances in Engineering Software, 32(6), 499–507. Cagnina, L. C., Esquivel, S. C., & Coello, C. A. C. (2011). Solving constrained optimization problems with a hybrid particle swarm optimization algorithm. Engineering Optimization, 43(8), 843–866. Cooper, L. (1963). Location-allocation Problems. Operations Research, 11, 331–343. Cvetkovic, D., & Parmee, I. (2002). Agent-based support within an interactive evolutionary design system. Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing, 16(5), 331–342. Cvetkovic, D., & Parmee, I. C. (2002). Preferences and their application in evolutionary multiobjective optimization. Ieee Transactions on Evolutionary Computation, 6(1), 42–57. doi:10.1109/4235.985691. da Silva, E. K., Barbosa, H. J. C., & Lemonge, A. C. C. (2011). An adaptive constraint handling technique for differential evolution with dynamic use of variants in engineering optimization. Optimization and Engineering, 12(1–2), 31–54. Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. West Sussex: Wiley. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. Ieee Transactions on Evolutionary Computation, 6(2), 182–197. doi:10.1109/4235.996017. Deb, K., Sundar, J., Udaya Bhaskara Rao, N., & Chaudhuri, S. (2006). Reference Point Based Multi-Objective Optimization Using Evolutionary Algorithms. International Journal of Computational Intelligence Research, 2(3), 273–286. http://repository.ias.ac.in/81053/. Engelbrecht, A. P. (2007). Computational intelligence. An introduction (2nd ed.). London: Wiley. Fonseca, C., & Fleming, P. (1993). Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization. In S. Forrest (Ed.), the fifth international conference on genetic algorithms, San Mateo, California, (pp. 416–423). University of Illinois at Urbana Champaign, Morgan Kaufmann Publishers. Fonseca, C. M., & Fleming, P. J. (1998). Multiobjective optimization and multiple constraint handling with evolutionary algorithms—Part I: A unified formulation. Ieee Transactions on Systems Man and Cybernetics Part a-Systems and Humans, 28(1), 26–37. Gen, M., Altiparmak, F., & Lin, L. (2006). A genetic algorithm for two-stage transportation problem using priority-based encoding. Or Spectrum, 28(3), 337–354. Gen, M., Cheng, R., & Lin, L. (2008). Network models and optimization, multiobjective genetic algorithm approach. London: Springer. Gong, M., Liu, F., Zhang, W., Jiao, L., & Zhang, Q. Interactive MOEA/D for multi-objective decision making. In GECCO ’11 Proceedings of the 13th annual conference on Genetic and evolutionary computation, (pp. 721–728). New York, NY, USA: ACM. doi:10.1145/2001576.2001675. Hitchcock, F. L. (1941). The distribution of a product from several sources to numerous localities. Journal of Mathematical Physics, 20, 220–230. Jin, Y. C., & Sendhoff, B. (2002). Incorporation of fuzzy preferences into evolutionay multiobjective optimization. In The 4th Asia-Pacific conference on simulated evolution and learning, Orchid Country Club, Singapore (vol. 1, pp. 26–30). Kim, J. H., Han, J. H., Kim, Y. H., Choi, S. H., & Kim, E. S. (2012). Preference-based solution selection algorithm for evolutionary multiobjective optimization. Ieee Transactions on Evolutionary Computation, 16(1), 20–34. Ozcan, U., & Toklu, B. (2009). Multiple-criteria decision-making in two-sided assembly line balancing: A goal programming and a fuzzy goal programming models. Computers & Operations Research, 36(6), 1955–1965. doi:10.1016/j.cor.2008.06.009. Park, S., Lee, T. E., & Sung, C. S. (2010). A three-level supply chain network design model with risk-pooling and lead times. Transportation Research Part E-Logistics and Transportation Review, 46(5), 563–581. doi:10.1016/j.tre.2009.12.004. Parmee, I., Cvetkovic, D., Bonham, C., & Packham, I. (2001). Introducing prototype interactive evolutionary systems for ill-defined, multi-objective design environments. Advances in Engineering Software, 32(6), 429–441. Parmee, I. C., Cvetković, D. C., Watson, A. H., & Bonham, C. R. (2000). Multiobjective satisfaction within an interactive evolutionary design environment. Evolutionary Computatio, 8(2), doi:10.1162/106365600568176. Rachmawati, L., & Srinivasan, D. (2010). Incorporation of Imprecise Goal Vectors into Evolutionary Multi-Objective Optimization. In Paper presented at the 2010 IEEE World Congress on Computational Intelligence. Spain: Barcelona. Rajabalipour Cheshmehgaz, H., Desa, M., & Wibowo, A. (2011). A flexible three-level logistic network design considering cost and time criteria with a multi-objective evolutionary algorithm. Journal of Intelligent Manufacturing (first online published), 1–17, doi:10.1007/s10845-011-0584-7. Rajabalipour Cheshmehgaz, H., Desa, M. I., & Wibowo, A. (2012a). Effective local evolutionary searches distributed on an island model solving bi-objective optimization problems. Applied Intelligence (first online published). doi:10.1007/s10489-012-0375-7. Rajabalipour Cheshmehgaz, H., Desa, M. I., & Wibowo, A. (2012b). An effective model of multiple multi-objective evolutionary algorithms with the assistance of regional multi-objective evolutionary algorithms: VIPMOEAs. Applied Soft Computing (first online published). doi:10.1016/j.asoc.2012.04.027. Sato, H., Aguirre, H. E., & Tanaka, K. (2007). Local dominance including control of dominance area of solutions in MOEAs. In 2007 Ieee symposium on computational intelligence in multi-criteria decision making, 310–317, 402. Shen, X. N., Guo, Y., Chen, Q. W., & Hu, W. L. (2010). A multi-objective optimization evolutionary algorithm incorporating preference information based on fuzzy logic. Computational Optimization and Applications, 46(1), 159–188. Stalk, G., & Hout, T. M. (1990). Competing against Time. Research-Technology Management, 33(2), 19–24. Suo, M. Q., Li, Y. P., & Huang, G. H. (2011). An inventory-theory-based interval-parameter two-stage stochastic programming model for water resources management. Engineering Optimization, 43(9), 999–1018. Tan, K. C., Khor, E. F., Lee, T. H., & Sathikannan, R. (2003). An evolutionary algorithm with advanced goal and priority specification for multi-objective optimization. Journal of Artificial Intelligence Research, 18, 183–215. Thiele, L., Miettinen, K., Korhonen, P. J., & Molina, J. (2009). A preference-based evolutionary algorithm for multi-objective optimization. Evolutionary Computation, 17(3), 411–436. Timm, G., & Herbert, K. (2009). Comprehensive logistics. London: Springer. Vidal, C. J., & Goetschalckx, M. (1997). Strategic production-distribution models: A critical review with emphasis on global supply chain models. European Journal of Operational Research, 98(1), 1–18. Yang, W., Li, L., & Ma, S. (2007). Coordinating supply chain response-time: A bi-level programming approach. International Journal of Advanced Manufacturing Technology, 31(9–10), 1034–1043. doi:10.1007/s00170-005-0260-1. Zhang, Q., & Li, H. (2007). MOEA/D: A multiobjective evolutionary algorithm based on decomposition. Ieee Transactions on Evolutionary Computation, 11(6), 712–731. Zitzler, E., & Kunzli, S. (2004). Indicator-based selection in multiobjective search. Parallel Problem Solving from Nature—Ppsn Viii, 3242, 832–842. Zitzler, E., Laumanns, M., & Thiele, L. (2001). SPEA2: Improving the strength pareto evolutionary algorithm. Zurich, Switzerland: Swiss Federal Institute of Technology (ETH) Zurich. Zitzler, E., Thiele, L., & Bader, J. (2008). SPAM: Set preference algorithm for multiobjective optimization. Parallel Problem Solving from Nature—Ppsn X, Proceedings, 5199(847–858), 1164.