The Sort-Assemble-Blend Routing Problem and Its Application to Semiconductors

References

• Afsar HM, Hnaien F (2019) Formulations and solution algorithms for dynamic assembly routing problem. Internat. J. Production Res. 58(3):671–688.Google Scholar
• Alhajri I, Elkamel A, Albahri T, Douglas PL (2008) A nonlinear programming model for refinery planning and optimisation with rigorous process models and product quality specifications. Internat. J. Oil Gas Coal Tech. 1(3):283–307.Google Scholar
• Arnold RJ, Ochoa A, Kerth CR, Miller RK, Murray SC (2019) Assessing the impact of corn variety and Texas terroir on flavor and alcohol yield in new-make bourbon whiskey. PLoS One 14(8):e0220787.Google Scholar
• Bowman KA, Duvall SG, Meindl JD (2002) Impact of die-to-die and within-die parameter fluctuations on the maximum clock frequency distribution for gigascale integration. IEEE J. Solid-State Circuits 37(2):183–190.Google Scholar
• Chaerle B, Bauwens D, Claeys D, Gautama S, Aghezzaf E-H (2021) Simulated-annealing algorithm to solve multi component matching problems in generalised selective assembly. Armenia S, Geril P, eds. Proc. 2021 Eur. Simulation Modeling Conf., 111–116.Google Scholar
• Chitsaz M, Cordeau J-F, Jans R (2019) A unified decomposition matheuristic for assembly, production, and inventory routing. INFORMS J. Comput. 31(1):134–15.Link, Google Scholar
• Fabozzi FJ (2016) The Handbook of Mortgage-Backed Securities, 7th ed. (Oxford University Press, Oxford, UK).Google Scholar
• Goodwin R, Miller R, Tuv E, Borisov A (2006) Semiconductor yield analysis and multi-chip package (MCP) die pairing optimization using statistical-learning. Proc. 7th Internat. Conf. Electronic Packaging Tech. (Institute of Electrical and Electronics Engineers, Washington, DC), 1–10.Google Scholar
• Iwata S, Matsui T, McCormick ST (1998) A fast bipartite network flow algorithm for selective assembly. Oper. Res. Lett. 22(4):137–143.Google Scholar
• Kim S-J, Jeong J-W, Jang J-S, Lee M-G (2005) Selective assembly for products with two performance characteristics. Proc. Korean Oper. Management Sci. Soc. Conf. (Chungbuk National University, Cheongju, South Korea), 740–745.Google Scholar
• Kumar N, Kennedy K, Giddersleve K, Abelson R, Mastrangelo CM, Montgommery DC (2006) A review of yield modelling techniques for semiconductor manufacturing. Internat. J. Production Res. 44(23):5019–5036.Google Scholar
• Munoz R (2022) Managing trade-offs in the chiplet era. Chip Scale Rev. 26(1):30–34.Google Scholar
• Pan R, Yu J, Zhao Y (2022) Many-objective optimization and decision-making method for selective assembly of complex mechanical products based on improved NSGA-III and VIKOR. Processes (Basel) 10(1):34.Google Scholar
• Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, et al. (2011) Scikit-learn: Machine learning in Python. J. Machine Learn. Res. 12(85):2825–2830.Google Scholar
• Prifita S, Afsar HM, Hnaien F (2021) Robust disassembly assembly routing problem with returns under uncertain yields. IFAC-PapersOnLine 54(2):354–359.Google Scholar
• Tadayon P, Iovino G, Ruiwale S (2021) Moore’s law and the future of test. Chip Scale Rev. 25(3):16–31.Google Scholar
• Tan MHY, Wu CFJ (2012) Generalized selective assembly. IIE Trans. 44(1):27–42.Google Scholar