An Innovative Genetic Algorithm for a Multi-Objective Optimization of Two-Dimensional Cutting-Stock Problem

References

• Aimin, Z., Y. Bo, L. Hui, Z. Shi-Zheng, N. S. Ponnuthurai, and Z. Qingfu. 2011. Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation 1:32–49. doi:10.1016/j.swevo.2011.03.001.
   Web of Science ®Google Scholar
• Alves, C., and J. M. V. Carvalhoa. 2007. Accelerating column generation for variable sized bin-packing problems. European Journal of Operational Research 183 (3):1333–52. doi:10.1016/j.ejor.2005.07.033.
   Web of Science ®Google Scholar
• Ayadi, O., M. Masmoudi, A. M. Ben, and F. Masmoudi. 2017. A new PSO-based algorithm for two-dimensional non-guillotine non-oriented cutting stock problem. Applied Artificial Intelligence:1–18. doi:10.1080/08839514.2017.1346966).
   Web of Science ®Google Scholar
• Benati, S. 1997. An algorithm for a cutting stock problem on a strip. Journal of the Operational Research Society 48:288–94. doi:10.1057/palgrave.jors.2600351.
   Web of Science ®Google Scholar
• Cui, Y., Y.-P. Cui, and Z. Zhao. 2015. Pattern set generation algorithm for the one-dimensional multiple stock sizes cutting stock problem. Engineering Optimization 47 (9):1289–301. doi:10.1080/0305215X.2014.969726.
   Web of Science ®Google Scholar
• Elaoud, S., T. Loukil, and J. Teghem. 2007. A Pareto fitness genetic algorithm. European Journal of Operational Research 177 (3):1703–19. doi:10.1016/j.ejor.2005.10.018.
   Web of Science ®Google Scholar
• Enrico, M., M. D. Rosa, and T. Paolo. 2014. Approaches to real world two-dimensional cutting problems. Omega 47:99–115. doi:10.1016/j.omega.2013.08.007.
   Web of Science ®Google Scholar
• Farley, A. A., and K. V. Richardson. 1984. Fixed charge problems with identical fixed charges. European Journal of Operational Research 18:245–49. doi:10.1016/0377-2217(84)90190-5.
   Web of Science ®Google Scholar
• Fekete, S. P., J. Schepers, and J. C. V. D. Veen. 2007. An exact algorithm for higher-dimensional orthogonal packing. Operations Research 55:569–87. doi:10.1287/opre.1060.0369.
   Web of Science ®Google Scholar
• Geetha, T., and K. Muthukumaran. 2013. An observational analysis of genetic operators. International Journal of Computer Applications 63 (18):24–34. doi:10.5120/10567-5583.
   Google Scholar
• Godfrey, C. O., and M. Michael. 2003. A genetic algorithm approach for the cutting stock problem. Journal of Intelligent Manufacturing 14 (2):209–18. doi:10.1023/A:1022955531018.
   Web of Science ®Google Scholar
• Haessler, R. W. 1975. Controlling cutting pattern changes in one-dimensional trim problems. Operations Research 23:483–93. doi:10.1287/opre.23.3.483.
   Web of Science ®Google Scholar
• Horacio, H. Y., and S. L. Marcelo. 2006. A hybrid heuristic to reduce the number of different patterns in cutting stock problems. Computers & Operations Research 33 (9):2744–56. doi:10.1016/j.cor.2005.02.026.
   Web of Science ®Google Scholar
• Jose, F. G. 2015. A hybrid biased random key genetic algorithm for a production and cutting problem. IFAC-PapersOnLine 48 (3):496–500. doi:10.1016/j.ifacol.2015.06.130.
   Google Scholar
• Kallrath, J., S. Rebennack, J. Kallrath, and R. Kusche. 2014. Solving real-world cutting stock-problems in the paper industry: Mathematical approaches, experience and challenges. European Journal of Operational Research 239:374–89. doi:10.1016/j.ejor.2014.03.027.
   Google Scholar
• Leo, H. W. Y., and K. S. T. Wallace. 2004. Strip packing using hybrid genetic approach. Engineering Applications of Artificial Intelligence 17:169–77. doi:10.1016/j.engappai.2004.02.003.
   Web of Science ®Google Scholar
• Leung, S. Y. S., W. K. Wong, and P. Y. Mok. 2008. Multiple-objective genetic optimization of the spatial design for packing and distribution carton boxes. Computers & Industrial Engineering 54 (4):889–902. doi:10.1016/j.cie.2007.10.018.
   Web of Science ®Google Scholar
• Mahdi, K., and C. Khalil. 2016. A tree search based combination heuristic for the knapsack problem with setup. Computers & Industrial Engineering 99:280–86. doi:10.1016/j.cie.2016.07.021.
   Web of Science ®Google Scholar
• Mellouli, A., and A. Dammak. 2008. An algorithm for the two-dimensional cutting-stock problem based on a pattern generation procedure. International Journal of Information and Management Sciences 19 (2):201–18.
   Google Scholar
• Mellouli, A., F. Masmoudi, I. Kacem, and M. Haddar. 2010. A hybrid genetic algorithm for optimization of two-dimensional cutting-stock problem. International Journal of Applied Metaheuristic Computing 1 (2):34–49. doi:10.4018/IJAMC.
   Google Scholar
• Mobasher, A., and A. Ekici. 2013. Solution approaches for the cutting stock problem with setup cost. Computers & Operations Research 40:225–35. doi:10.1016/j.cor.2012.06.007.
   Web of Science ®Google Scholar
• Radhia, A., B. Slim, and B. S. Lamjed. 2016. Dynamic multi-objective optimization using evolutionary algorithms: A survey. Recent Advances in Evolutionary Multi-Objective Optimization 20:31–70.
   Google Scholar
• Ramiro, V., M. Cesar, S. María, and G. R. Inés. 2007. Improving cutting-stock plans with multi-objective genetic algorithm. Software and Data Technologies 22:332–44.
   Google Scholar
• Rennera, G., and A. Ekart. 2003. Genetic algorithms in computer aided design. Computer-Aided Design 35:709–26. doi:10.1016/S0010-4485(03)00003-4.
   Web of Science ®Google Scholar
• Rinaldi, F., and A. F. Franz. 2007. A two-dimensional strip cutting problem with sequencing constraint. European Journal of Operational Research 183 (3):1371–84. doi:10.1016/j.ejor.2005.12.050.
   Web of Science ®Google Scholar
• Umetami, S., M. Yagiura, and T. Ibaraki. 2003. One-dimensional cutting stock problem to minimize the number of different patterns. European Journal of Operational Research 146:388–402. doi:10.1016/S0377-2217(02)00239-4.
   Web of Science ®Google Scholar
• Vanderbeck, F. 2000. Exact algorithm for minimizing the number of setups in the one-dimensional cutting stock problem. Operations Research 48:915–26. doi:10.1287/opre.48.6.915.12391.
   Web of Science ®Google Scholar
• Wong, W. K., P. Y. Mok, and C. K. Kwong. 2007. Optimization of fault tolerant fabric cutting schedules using genetic algorithms and fuzzy set theory. European Journal of Operational Research 177 (3):1876–93. doi:10.1016/j.ejor.2005.12.021.
   Web of Science ®Google Scholar
• Yanira, G., M. Gara, and L. Coromoto. 2016. Multi-objective multi-level filling evolutionary algorithm for the 3D cutting stock problem. Procedia Computer Science 96:355–64. doi:10.1016/j.procs.2016.08.148.
   Google Scholar
• Yaodong, C., Z. Cheng, and Y. Yi. 2015. Pattern-set generation algorithm for the one-dimensional cutting stock problem with setup cost. European Journal of Operational Research 243 (2):540–46. doi:10.1016/j.ejor.2014.12.015.
   Web of Science ®Google Scholar