Energy-oriented bi-objective optimisation for a multi-module reconfigurable manufacturing system

References

• Afrin, K., A. S. Iquebal, S. K. Kumar, M.K. Tiwari, L. Benyoucef, and A. Dolgui. 2016. “Towards Green Automated Production Line with Rotary Transfer and Turrets: A Multi-objective Approach Using a Binary Scatter Tabu Search Procedure.” International Journal of Computer Integrated Manufacturing 29 (7): 768–785. doi: 10.1080/0951192X.2015.1109143
   Web of Science ®Google Scholar
• Andersen, A. L., T. D. Brunoe, K. Nielsen, and C. Rösiö. 2017. “Towards a Generic Design Method for Reconfigurable Manufacturing Systems: Analysis and Synthesis of Current Design Methods and Evaluation of Supportive Tools.” Journal of Manufacturing Systems 42: 179–195. doi: 10.1016/j.jmsy.2016.11.006
   Web of Science ®Google Scholar
• Bandyopadhyay, S., S. Saha, U. Maulik, and K. Deb. 2008. “A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA.” IEEE Transactions on Evolutionary Computation 12 (3): 269–283. doi: 10.1109/TEVC.2007.900837
   Web of Science ®Google Scholar
• Battaïa, O., A. Dolgui, and N. Guschinsky. 2017. “Decision Support for Design of Reconfigurable Rotary Machining Systems for Family Part Production.” International Journal of Production Research 55 (5): 1368–1385. doi: 10.1080/00207543.2016.1213451
   Web of Science ®Google Scholar
• Battaïa, O., A. Dolgui, N. Guschinsky, and G. Levin. 2012. “Optimal Design of Rotary Transfer Machines with Turrets.” IFAC Proceedings Volumes 45 (6): 407–412. doi: 10.3182/20120523-3-RO-2023.00416
   Google Scholar
• Battaïa, O., A. Dolgui, N. Guschinsky, and G. Levin. 2014. “Combinatorial Techniques to Optimally Customize An Automated Production Line with Rotary Transfer and Turrets.” IIE Transactions 46 (9): 867–879. doi: 10.1080/0740817X.2013.849837
   Web of Science ®Google Scholar
• Benderbal, H. H., M. Dahane, and L. Benyoucef. 2016. “Hybrid Heuristic to Minimize Machines Unavailability Impact on Reconfigurable Manufacturing System Using Reconfigurable Process Plan.” IFAC Papersonline 49 (12): 1626–1631. doi: 10.1016/j.ifacol.2016.07.813
   Google Scholar
• Benderbal, H. H., M. Dahane, and L. Benyoucef. 2018. “Modularity Assessment in Reconfigurable Manufacturing System (RMS) Design: An Archived Multi-objective Simulated Annealing-based Approach.” International Journal of Advanced Manufacturing Technology 94 (1-4): 1–21. doi: 10.1007/s00170-016-9349-y
   Web of Science ®Google Scholar
• Bensmaine, A., M. Dahane, and L. Benyoucef. 2013. “A Non-dominated Sorting Genetic Algorithm Based Approach for Optimal Machines Selection in Reconfigurable Manufacturing Environment.” Computers & Industrial Engineering 66 (3): 519–524. doi: 10.1016/j.cie.2012.09.008
   Web of Science ®Google Scholar
• Coello, C. A. C., and N. C. Cortes. 2005. “Solving Multiobjective Optimization Problems Using An Artificial Immune System.” Genetic Programming & Evolvable Machines 6 (2): 163–190. doi: 10.1007/s10710-005-6164-x
   Google Scholar
• Deb, K., A. Pratap, S. Agarwal, and T. Meyarivan. 2002. “A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II.” IEEE Transactions on Evolutionary Computation 6 (2): 182–197. doi: 10.1109/4235.996017
   Web of Science ®Google Scholar
• Dou, J., X. Dai, and Z. Meng. 2009. “Graph Theory-Based Approach to Optimize Single-Product Flow-Line Configurations of RMS.” International Journal of Advanced Manufacturing Technology 41 (9-10): 916–931. doi: 10.1007/s00170-008-1541-2
   Web of Science ®Google Scholar
• Dou, J., X. Dai, and Z. Meng. 2010. “Optimisation for Multi-Part Flow-Line Configuration of Reconfigurable Manufacturing System Using GA.” International Journal of Production Research 48 (14): 4071–4100. doi: 10.1080/00207540903036305
   Web of Science ®Google Scholar
• Dou, J., X. Dai, and Z. Meng. 2011. “A GA-Based Approach for Optimizing Single-part Flow-Line Configurations of RMS.” Journal of Intelligent Manufacturing 22 (2): 301–317. doi: 10.1007/s10845-009-0305-7
   Web of Science ®Google Scholar
• Eguia, I., J. C. Molina, S. Lozano, and J. Racero. 2017. “Cell Design and Multi-Period Machine Loading in Cellular Reconfigurable Manufacturing Systems with Alternative Routing.” International Journal of Production Research 55 (10): 2775–2790. doi: 10.1080/00207543.2016.1193673
   Web of Science ®Google Scholar
• Goyal, K. K., P. K. Jain, and M. Jain. 2012. “Optimal Configuration Selection for Reconfigurable Manufacturing System Using NSGA-II and TOPSIS.” International Journal of Production Research 50 (15): 4175–4191. doi: 10.1080/00207543.2011.599345
   Web of Science ®Google Scholar
• Haimes, Y., L. Lasdon, and D. Wismer. 1971. “On a Bicriterion Formulation of the Problems of Integrated System Identification and System Optimization.” IEEE Transactions on Systems, Man, and Cybernetics 1 (3): 296–297.
   Google Scholar
• Hasan, F., P. K. Jain, and D. Kumar. 2014. “Optimum Configuration Selection in Reconfigurable Manufacturing System Involving Multiple Part Families.” Opsearch 51 (2): 297–311. doi: 10.1007/s12597-013-0146-1
   Google Scholar
• Jaszkiewicz, A., and T. Lust. 2017. “Proper Balance Between Search Towards and Along Pareto Front: Biobjective TSP Case Study.” Annals of Operations Research 254 (1-2): 111–130. doi: 10.1007/s10479-017-2415-5
   Web of Science ®Google Scholar
• Kirkpatrick, S., and M. P. Vecchi. 1987. “Optimization by Simulated Annealing.” Readings in Computer Vision: Issues, Problems, Principles, and Paradigms, 339–348. San Francisco: Morgan Kaufmann Publishers Inc.
   Google Scholar
• Koren, S. Y., U. Heisel, and F. Jovane. 1999. “Reconfigurable Manufacturing Systems.” Journal of Manufacturing Systems 29 (4): 130–141. doi: 10.1016/j.jmsy.2011.01.001
   Web of Science ®Google Scholar
• Laumanns, M., L. Thiele, and E. Zitzler. 2006. “An Efficient, Adaptive Parameter Variation Scheme for Metaheuristics Based on the Epsilon-Constraint Method.” European Journal of Operational Research 169 (3): 932–942. doi: 10.1016/j.ejor.2004.08.029
   Web of Science ®Google Scholar
• Liles, D. H., and B. L. Huff. 1990. “A Computer Based Production Scheduling Architecture Suitable for Driving a Reconfigurable Manufacturing System.” Computers & Industrial Engineering 19 (1-4): 1–5. doi: 10.1016/0360-8352(90)90066-U
   Web of Science ®Google Scholar
• Liu, M., C.-Y. Lee, Z. Zhang, and C. Chu. 2016. “Bi-Objective Optimization for the Container Terminal Integrated Planning.” Transportation Research Part B: Methodological 93: 720–749. doi: 10.1016/j.trb.2016.05.012
   Web of Science ®Google Scholar
• Qin, J., Y. Liu, and R. Grosvenor. 2016. “A Categorical Framework of Manufacturing for Industry 4.0 and Beyond.” Procedia Cirp 52: 173–178. doi: 10.1016/j.procir.2016.08.005
   Google Scholar
• Renzi, C., F. Leali, M. Cavazzuti, and A. O. Andrisano. 2014. “A Review on Artificial Intelligence Applications to the Optimal Design of Dedicated and Reconfigurable Manufacturing Systems.” International Journal of Advanced Manufacturing Technology 72 (1-4): 403–418. doi: 10.1007/s00170-014-5674-1
   Web of Science ®Google Scholar
• Schlechtendahl, J., M. Keinert, F. Kretschmer, A. Lechler, and A. Verl. 2015. “Making Existing Production Systems Industry 4.0-ready.” Production Engineering 9 (1): 143–148. doi: 10.1007/s11740-014-0586-3
   Google Scholar
• Stidsen, T. R., K. A. Andersen, and B. Dammann. 2014. “A Branch and Bound Algorithm for a Class of Biobjective Mixed Integer Programs.” Management Science 60 (4): 1009–1032. doi: 10.1287/mnsc.2013.1802
   Web of Science ®Google Scholar
• Yingjie, Z. 2014. “Energy Efficiency Techniques in Machining Process: A Review.” International Journal of Advanced Manufacturing Technology 71 (5-8): 1123–1132. doi: 10.1007/s00170-013-5551-3
   Web of Science ®Google Scholar
• Yu, J.-M., H.-H. Doh, H.-W. Kim, J.-S. Kim, D.-H. Lee, and S.-H. Nam. 2012. “Iterative Algorithms for Part Grouping and Loading in Cellular Reconfigurable Manufacturing Systems.” Journal of the Operational Research Society 63 (12): 1635–1644. doi: 10.1057/jors.2012.9
   Web of Science ®Google Scholar
• Zitzler, E., K. Deb, and L. Thiele. 2000. “Comparison of Multiobjective Evolutionary Algorithms: Empirical Results.” Evolutionary Computation 8 (2): 173–195. doi: 10.1162/106365600568202
   PubMed Web of Science ®Google Scholar