Mixed-model sequencing with reinsertion of failed vehicles: a case study for automobile industry

References

• Agrawal, S., and M. K. Tiwari. 2008. “A Collaborative Ant Colony Algorithm to Stochastic Mixed-model U-shaped Disassembly Line Balancing and Sequencing Problem.” International Journal of Production Research 46 (6): 1405–1429. https://doi.org/10.1080/00207540600943985.
   Web of Science ®Google Scholar
• Akgündüz, Onur Serkan, and Semra Tunalı. 2010. “An Adaptive Genetic Algorithm Approach for the Mixed-model Assembly Line Sequencing Problem.” International Journal of Production Research48 (17): 5157–5179. https://doi.org/10.1080/00207540903117857.
   Web of Science ®Google Scholar
• Alaghebandha, Mohammad, Vahid Hajipour, and Mojtaba Hemmati. 2017. “Optimizing Multi-objective Sequencing Problem in Mixed-model Assembly Line on Just-in-time: Particle Swarm Optimization Algorithm.” International Journal of Management Science and Engineering Management 12 (4): 288–298. https://doi.org/10.1080/17509653.2016.1258593.
   Web of Science ®Google Scholar
• Bard, Jonathan F., E. Z. E. Y. Dar-Elj, and Avraham Shtub. 1992. “An Analytic Framework for Sequencing Mixed Model Assembly Lines.” The International Journal of Production Research 30 (1): 35–48. https://doi.org/10.1080/00207549208942876.
   Web of Science ®Google Scholar
• Becker, Christian, and Armin Scholl. 2006. “A Survey on Problems and Methods in Generalized Assembly Line Balancing.” European Journal of Operational Research 168 (3): 694–715. https://doi.org/10.1016/j.ejor.2004.07.023.
   Web of Science ®Google Scholar
• Bolat, Ahmet, and Candace A. Yano. 1992. “Scheduling Algorithms to Minimize Utility Work At a Single Station on a Paced Assembly Line.” Production Planning & Control 3 (4): 393–405. https://doi.org/10.1080/09537289208919409.
   Google Scholar
• Boysen, Nils, Malte Fliedner, and Armin Scholl. 2007. “A Classification of Assembly Line Balancing Problems.” European Journal of Operational Research 183 (2): 674–693. https://doi.org/10.1016/j.ejor.2006.10.010.
   Web of Science ®Google Scholar
• Boysen, Nils, Malte Fliedner, and Armin Scholl. 2009. “Sequencing Mixed-model Assembly Lines: Survey, Classification and Model Critique.” European Journal of Operational Research 192 (2): 349–373. https://doi.org/10.1016/j.ejor.2007.09.013.
   Web of Science ®Google Scholar
• Boysen, Nils, Armin Scholl, and Nico Wopperer. 2012. “Resequencing of Mixed-model Assembly Lines: Survey and Research Agenda.” European Journal of Operational Research 216 (3): 594–604. https://doi.org/10.1016/j.ejor.2011.08.009.
   Web of Science ®Google Scholar
• Brammer, Janis, Bernhard Lutz, and Dirk Neumann. 2022. “Stochastic Mixed Model Sequencing with Multiple Stations Using Reinforcement Learning and Probability Quantiles.” OR Spectrum 44 (1): 29–56. https://doi.org/10.1007/s00291-021-00652-x.
   Web of Science ®Google Scholar
• Coello, Carlos A. Coello, Gary B. Lamont, and David A. Van Veldhuizen. 2007. Evolutionary Algorithms for Solving Multi-Objective Problems. Vol. 5. New York, NY: Springer.
   Google Scholar
• Deb, Kalyanmoy, Amrit Pratap, Sameer Agarwal, and T. A. M. T. Meyarivan. 2002. “A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II.” IEEE Transactions on Evolutionary Computation 6 (2): 182–197. https://doi.org/10.1109/4235.996017.
   Web of Science ®Google Scholar
• Dincbas, Mehmet, Helmut Simonis, and Pascal Van Hentenryck. 1988. “Solving the Car-Sequencing Problem in Constraint Logic Programming.” In ECAI, Vol. 88, 290–295.
   Google Scholar
• Dong, Jietao, Linxuan Zhang, Tianyuan Xiao, and Huachao Mao. 2014. “Balancing and Sequencing of Stochastic Mixed-model Assembly U-lines to Minimise the Expectation of Work Overload Time.” International Journal of Production Research 52 (24): 7529–7548. https://doi.org/10.1080/00207543.2014.944280.
   Web of Science ®Google Scholar
• Estellon, Bertrand, Frédéric Gardi, and Karim Nouioua. 2006. “Large Neighborhood Improvements for Solving Car Sequencing Problems.” RAIRO-Operations Research 40 (4): 355–379. https://doi.org/10.1051/ro:2007003.
   Web of Science ®Google Scholar
• Fonseca, Carlos M., and Peter J. Fleming. 1996. “On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers.” In International Conference on Parallel Problem Solving from Nature, 584–593. Springer.
   Google Scholar
• Franz, Christian, Eric Caap Hällgren, and Achim Koberstein. 2014. “Resequencing Orders on Mixed-model Assembly Lines: Heuristic Approaches to Minimise the Number of Overload Situations.” International Journal of Production Research 52 (19): 5823–5840. https://doi.org/10.1080/00207543.2014.918293.
   Web of Science ®Google Scholar
• Franz, Christian, Achim Koberstein, and Leena Suhl. 2015. “Dynamic Resequencing At Mixed-model Assembly Lines.” International Journal of Production Research 53 (11): 3433–3447. https://doi.org/10.1080/00207543.2014.993046.
   Web of Science ®Google Scholar
• Gujjula, Rico, and Hans-Otto Gunther. 2009. “Resequencing Mixed-Model Assembly Lines Under Just-in-Sequence Constraints.” In 2009 International Conference on Computers & Industrial Engineering, 668–673. IEEE.
   Google Scholar
• Gunay, Elif Elcin, and Ufuk Kula. 2017. “A Stochastic Programming Model for Resequencing Buffer Content Optimisation in Mixed-model Assembly Lines.” International Journal of Production Research55 (10): 2897–2912. https://doi.org/10.1080/00207543.2016.1227101.
   Web of Science ®Google Scholar
• Hottenrott, Andreas, Leon Waidner, and Martin Grunow. 2021. “Robust Car Sequencing for Automotive Assembly.” European Journal of Operational Research 291 (3): 983–994. https://doi.org/10.1016/j.ejor.2020.10.004.
   Web of Science ®Google Scholar
• Hyun, Chul Ju, Yeongho Kim, and Yeo Keun Kim. 1998. “A Genetic Algorithm for Multiple Objective Sequencing Problems in Mixed Model Assembly Lines.” Computers & Operations Research 25 (7–8): 675–690. https://doi.org/10.1016/S0305-0548(98)00026-4.
   Web of Science ®Google Scholar
• Leng, Jinling, Xingyuan Wang, Shiping Wu, Chun Jin, Meng Tang, Rui Liu, Alexander Vogl, and Huiyu Liu. 2023. “A Multi-objective Reinforcement Learning Approach for Resequencing Scheduling Problems in Automotive Manufacturing Systems.” International Journal of Production Research 61 (15): 5156–5175.
   Web of Science ®Google Scholar
• Liu, Dong, Qiang Huang, Yuanyuan Yang, Dengfeng Liu, and Xiaoting Wei. 2020. “Bi-objective Algorithm Based on NSGA-II Framework to Optimize Reservoirs Operation.” Journal of Hydrology 585: 124830. https://doi.org/10.1016/j.jhydrol.2020.124830.
   Web of Science ®Google Scholar
• Lopes, Thiago Cantos, Adalberto Sato Michels, Nadia Brauner, and Leandro Magatão. 2023. “Balancing-sequencing Paced Assembly Lines: a Multi-objective Mixed-integer Linear Case Study.” International Journal of Production Research 61 (17): 5901–5917. https://doi.org/10.1080/00207543.2022.2118888.
   Web of Science ®Google Scholar
• Lopes, Thiago Cantos, Celso Gustavo Stall Sikora, Adalberto Sato Michels, and Leandro Magatão. 2020. “An Iterative Decomposition for Asynchronous Mixed-model Assembly Lines: Combining Balancing, Sequencing, and Buffer Allocation.” International Journal of Production Research 58 (2): 615–630. https://doi.org/10.1080/00207543.2019.1598597.
   Web of Science ®Google Scholar
• Louis, Adèle, Gülgün Alpan, Bernard Penz, and Alain Benichou. 2023. “Mixed-model Sequencing Versus Car Sequencing: Comparison of Feasible Solution Spaces.” International Journal of Production Research 61 (10): 3415–3434. https://doi.org/10.1080/00207543.2022.2082896.
   Web of Science ®Google Scholar
• Michalak, Krzysztof. 2015. “Improving the NSGA-II Performance with an External Population.” In Intelligent Data Engineering and Automated Learning–IDEAL 2015: 16th International Conference, Wroclaw, Poland, October 14-16, 2015, Proceedings 16, 273–280. Springer.
   Google Scholar
• Mosadegh, Hadi, S. M. T. Fatemi Ghomi, and Gürsel A. Süer. 2017. “Heuristic Approaches for Mixed-model Sequencing Problem with Stochastic Processing Times.” International Journal of Production Research 55 (10): 2857–2880. https://doi.org/10.1080/00207543.2016.1223897.
   Web of Science ®Google Scholar
• Mosadegh, Hadi, S. M. T. Fatemi Ghomi, and Gürsel A. Süer. 2020. “Stochastic Mixed-model Assembly Line Sequencing Problem: Mathematical Modeling and Q-learning Based Simulated Annealing Hyper-heuristics.” European Journal of Operational Research 282 (2): 530–544. https://doi.org/10.1016/j.ejor.2019.09.021.
   Web of Science ®Google Scholar
• Pearce, Bryan W., Kavit Antani, Laine Mears, Kilian Funk, Maria E. Mayorga, and Mary E. Kurz. 2019. “An Effective Integer Program for a General Assembly Line Balancing Problem with Parallel Workers and Additional Assignment Restrictions.” Journal of Manufacturing Systems 50: 180–192. https://doi.org/10.1016/j.jmsy.2018.12.011.
   Web of Science ®Google Scholar
• Peng, Yunfang, Lijun Zhang, Beixin Xia, and Yajuan Han. 2023. “Research on Balancing and Sequencing Problems of Flexible Mixed Model Assembly Lines with Alternative Precedence Relations.” International Journal of Production Research 61 (24): 8451–8467. https://doi.org/10.1080/00207543.2022.2152890.
   Web of Science ®Google Scholar
• Pil, Frits K., and Matthias Holweg. 2004. “Linking Product Variety to Order-fulfillment Strategies.” Interfaces 34 (5): 394–403. https://doi.org/10.1287/inte.1040.0092.
   Web of Science ®Google Scholar
• Sawik, Tadeusz. 2024. “A New MIP Approach for Balancing and Scheduling of Mixed Model Assembly Lines with Alternative Precedence Relations.” International Journal of Production Research 64 (1-2): 110–121.
   Google Scholar
• Schaffer, J. David. 1985. “Multiple Objective Optimization with Vector Evaluated Genetic Algorithms.” In Proceedings of the First International Conference on Genetic Algorithms and Their Applications, 1985, Lawrence Erlbaum Associates. Inc., Publishers.
   Google Scholar
• Sikora, Celso Gustavo Stall. 2021. “Benders' Decomposition for the Balancing of Assembly Lines with Stochastic Demand.” European Journal of Operational Research 292 (1): 108–124. https://doi.org/10.1016/j.ejor.2020.10.019.
   Web of Science ®Google Scholar
• Sivasankaran, Panneerselvam, and P. Shahabudeen. 2014. “Literature Review of Assembly Line Balancing Problems.” The International Journal of Advanced Manufacturing Technology 73 (9–12): 1665–1694. https://doi.org/10.1007/s00170-014-5944-y.
   Web of Science ®Google Scholar
• Solnon, Christine, Alain Nguyen, and Christian Artigues. 2008. “The Car Sequencing Problem: Overview of State-of-the-art Methods and Industrial Case-study of the ROADEF'2005 Challenge Problem.” European Journal of Operational Research 191 (3): 912–927. https://doi.org/10.1016/j.ejor.2007.04.033.
   Web of Science ®Google Scholar
• Srinath, Nitin. 2022. “A Study of Scheduling Problems with Sequence Dependent Restrictions and Preferences”.
   Google Scholar
• Steiner, George, and Scott Yeomans. 1993. “Level Schedules for Mixed-model, Just-in-time Processes.” Management Science 39 (6): 728–735. https://doi.org/10.1287/mnsc.39.6.728.
   Web of Science ®Google Scholar
• Tsai, Li-Hui. 1995. “Mixed-model Sequencing to Minimize Utility Work and the Risk of Conveyor Stoppage.” Management Science 41 (3): 485–495. https://doi.org/10.1287/mnsc.41.3.485.
   Web of Science ®Google Scholar
• Uğur, Özcan, Hakan Çerçioğlu, Hadi Gökçen, and Bilal Toklu. 2010. “Balancing and Sequencing of Parallel Mixed-model Assembly Lines.” International Journal of Production Research 48 (17): 5089–5113. https://doi.org/10.1080/00207540903055735.
   Web of Science ®Google Scholar
• Uğur, Özcan, Talip Kellegöz, and Bilal Toklu. 2011. “A Genetic Algorithm for the Stochastic Mixed-model U-line Balancing and Sequencing Problem.” International Journal of Production Research49 (6): 1605–1626. https://doi.org/10.1080/00207541003690090.
   Web of Science ®Google Scholar
• Wang, Penghong, Jianrou Huang, Zhihua Cui, Liping Xie, and Jinjun Chen. 2020. “A Gaussian Error Correction Multi-objective Positioning Model with NSGA-II.” Concurrency and Computation: Practice and Experience 32 (5): e5464. https://doi.org/10.1002/cpe.v32.5.
   Web of Science ®Google Scholar
• Xu, Chuanbo, Yiming Ke, Yanbin Li, Han Chu, and Yunna Wu. 2020. “Data-driven Configuration Optimization of An Off-grid Wind/PV/hydrogen System Based on Modified NSGA-II and CRITIC-TOPSIS.” Energy Conversion and Management 215: 112892. https://doi.org/10.1016/j.enconman.2020.112892.
   Web of Science ®Google Scholar
• Yilmazlar, Ibrahim Ozan. 2023. “Modeling and Solution Methodologies for Mixed-Model Sequencing in Automobile Industry.” PhD diss., Clemson University.
   Google Scholar
• Yilmazlar, I. Ozan, Adarsh Jeyes, Alexis Fiore, Apurva Patel, Chelsea Spence, Chase Wentzky, Nicole Zero, et al. 2020. “A Case Study in Line Balancing and Simulation.” Procedia Manufacturing 48: 71–81. https://doi.org/10.1016/j.promfg.2020.05.076.
   Google Scholar
• Yilmazlar, I. Ozan, and Mary E. Kurz. 2023. “Adaptive Local Search Algorithm for Solving Car Sequencing Problem.” Journal of Manufacturing Systems 68: 635–643. https://doi.org/10.1016/j.jmsy.2023.05.018.
   Web of Science ®Google Scholar
• Yilmazlar, I. Ozan, Mary E. Kurz, and Hamed Rahimian. 2023. “Mixed-Model Sequencing with Stochastic Failures: A Case Study for Automobile Industry.” arXiv preprint arXiv:2306.12618.
   Google Scholar
• Zhao, Xiaobo, Jianyong Liu, Katsuhisa Ohno, and Shigenori Kotani. 2007. “Modeling and Analysis of a Mixed-model Assembly Line with Stochastic Operation Times.” Naval Research Logistics (NRL) 54 (6): 681–691. https://doi.org/10.1002/nav.v54:6.
   Web of Science ®Google Scholar
• Zhou, Bing-hai, and Fen Tan. 2018. “Electric Vehicle Handling Routing and Battery Swap Station Location Optimisation for Automotive Assembly Lines.” International Journal of Computer Integrated Manufacturing 31 (10): 978–991. https://doi.org/10.1080/0951192X.2018.1493229.
   Web of Science ®Google Scholar
• Zitzler, Eckart, Kalyanmoy Deb, and Lothar Thiele. 2000. “Comparison of Multiobjective Evolutionary Algorithms: Empirical Results.” Evolutionary Computation 8 (2): 173–195. https://doi.org/10.1162/106365600568202.
   PubMed Web of Science ®Google Scholar
• Zope, Atul, Raju Kumar Swami, and Atul Patil. 2023. “Lean Six Sigma Barriers with Potential Solutions in Electrical Vehicle Assembly: A Review.” Proceedings on Engineering 5 (3): 375–382.
   Google Scholar