Multi-product continuous plant scheduling: combination of decomposition, genetic algorithm, and constructive heuristic

References

• Berger, K.-E., and F. Galea. 2013. “An Efficient Parallelization Strategy for Dynamic Programming on GPU.” In 2013 IEEE 27th International Symposium on Parallel & Distributed Processing Workshops and PhD Forum, 1797–1806. Boston.
   Google Scholar
• Borisovsky, P. A., A. V. Eremeev, and J. Kallrath. 2017. “On Hybrid Method for Medium-Term Multi-Product Continuous Plant Scheduling.” In 2017 International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON), Novosibirsk, 18–22 Sept. 2017, 42–47. IEEE.
   Google Scholar
• Castro, P. M., I. Harjunkoski, and I. E. Grossmann. 2011. “Greedy Algorithm for Scheduling Batch Plants with Sequence-Dependent Changeovers.” AIChE Journal 57 (2): 373–387. doi: 10.1002/aic.12261
   Web of Science ®Google Scholar
• Cho, D. 2002. “A Solution Approach to the Design of Multi-period, Multipurpose Batch Plants.” Korean Journal of Chemical Engineering 19 (2): 197–205. doi: 10.1007/BF02698402
   Web of Science ®Google Scholar
• Doganis, P., and H. Sarimveis. 2008. “Mixed Integer Linear Programming Scheduling in the Food Industry.” In Optimization in Food Engineering, edited by Ferruh Erdogdu, 305–328. CRC Press.
   Google Scholar
• Dolgui, A., A. V. Eremeev, M. Y. Kovalyov, and P. M. Kuznetsov. 2010. “Multi-Product Lot Sizing and Scheduling on Unrelated Parallel Machines.” IIE Transactions 42 (7): 514–524. doi: 10.1080/07408170903542649
   Web of Science ®Google Scholar
• Dolgui, A., D. Ivanov, S. P. Sethi, and B. Sokolov. 2018. “Scheduling in Production, Supply Chain and Industry 4.0 Systems by Optimal Control: Fundamentals, State-of-the-art and Applications.” International Journal of Production Research. doi:10.1080/00207543.2018.1442948.
   PubMed Web of Science ®Google Scholar
• Duman, E., M. B. Yildirim, and A. F. Alkaya. 2008. “Scheduling Continuous Aluminium Casting Lines.” International Journal of Production Research 46 (20): 5701–5718. doi: 10.1080/00207540701355212
   Web of Science ®Google Scholar
• Eremeev, A. V., and Ju.V. Kovalenko. 2016a. “On Multi-Product Lot-Sizing and Scheduling with Multi-Machine Technologies.” Operations Research Proceedings (OR 2014), Aachen, Germany, Springer, 301–306.
   Google Scholar
• Eremeev, A. V., and Yu.V. Kovalenko. 2016b. “Mixed Integer Programming Approach to Multiprocessor Job Scheduling with Setup Time.” In 9th International Conference on Discrete Optimization and Operations Research (DOOR 2016), Vladivostok, Russia, Lecture Notes in Computer Science, vol. 9869, Springer, 298–308.
   Google Scholar
• Fan, K., Y. Zhai, X. Li, and M. Wang. 2018. “Review and Classification of Hybrid Shop Scheduling.” Production Engineering 12: 597–609. doi: 10.1007/s11740-018-0832-1
   Google Scholar
• Floudas, C. A., and X. Lin. 2004. “Continuous-time Versus Discrete-time Approaches for Scheduling of Chemical Processes: A Review.” Computers and Chemical Engineering 28: 2109–2129. doi: 10.1016/j.compchemeng.2004.05.002
   Web of Science ®Google Scholar
• Goldberg, D. E. 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Reading: Addison Wesley.
   Google Scholar
• Graham, R. L., E. L. Lawler, J. K. Lenstra, and A. H. G. Rinnooy Kan. 1979. “Optimization and Approximation in Deterministic Sequencing and Scheduling.” Annals of Discrete Mathematics 5: 287–326. doi: 10.1016/S0167-5060(08)70356-X
   Google Scholar
• Gupta, S., and I. A. Karimi. 2003. “An Improved MILP Formulation for Scheduling Multiproduct, Multistage Batch Plants.” Industrial and Engineering Chemistry Research 42: 2365–2380. doi: 10.1021/ie020180g
   Web of Science ®Google Scholar
• Harjunkoski, I., and I. E. Grossmann. 2001. “A Decomposition Approach for the Scheduling of a Steel Plant Production.” Computers and Chemical Engineering 25: 1647–1660. doi: 10.1016/S0098-1354(01)00729-3
   Web of Science ®Google Scholar
• Held, M., and R. M. Karp. 1962. “A Dynamic Programming Approach to Sequencing Problems.” Journal of the Society for Industrial and Applied Mathematics 10: 196–210. doi: 10.1137/0110015
   Google Scholar
• Holland, J. H. 1975. Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press.
   Google Scholar
• Ierapetritou, M. G., and C. A. Floudas. 1998. “Effective Continuous-Time Formulation for Short-Term Scheduling: I. Multipurpose Batch Processes.” Industrial and Engineering Chemistry Research 37: 4341–4359. doi: 10.1021/ie970927g
   Web of Science ®Google Scholar
• Ivanov, D., A. Dolgui, B. Sokolov, F. Werner, and M. Ivanova. 2016. “A Dynamic Model and An Algorithm for Short-term Supply Chain Scheduling in the Smart Factory Industry 4.0.” International Journal of Production Research 54 (2): 386–402. doi: 10.1080/00207543.2014.999958
   Web of Science ®Google Scholar
• Janak, S. L., C. A. Floudas, J. Kallrath, and N. Vormbrock. 2006. “Production Scheduling of a Large-Scale Industrial Batch Plant: I. Short-Term and Medium-Term Scheduling.” Industrial and Engineering Chemistry Research 45 (25): 8234–8252. doi: 10.1021/ie0600588
   Web of Science ®Google Scholar
• Kallrath, J. 2002. “Planning and Scheduling in the Process Industry.” OR-Spektrum 24 (3): 219–250. doi: 10.1007/s00291-002-0101-7
   Web of Science ®Google Scholar
• Kallrath, J.. 2011. “Polylithic Modeling and Solution Approaches Using Algebraic Modeling Systems.” Optimization Letters 5: 453–466. doi: 10.1007/s11590-011-0320-4
   Web of Science ®Google Scholar
• Kondili, E., C. C. Pantelides, and R. W. H. Sargent. 1993. “A General Algorithm for Short-term Scheduling of Batch Operations – 1. MILP Formulation.” Computers and Chemical Engineering 17: 211–227. doi: 10.1016/0098-1354(93)80015-F
   Web of Science ®Google Scholar
• Lin, X., S. L. Janak, and C. A. Floudas. 2004. “A New Robust Optimization Approach for Scheduling Under Uncertainty: I. Bounded Uncertainty.” Computers and Chemical Engineering 28: 1069–1085. doi: 10.1016/j.compchemeng.2003.09.020
   Web of Science ®Google Scholar
• Maravelias, C. T., and I. E. Grossmann. 2003. “A New General Continuous-Time State Task Network Formulation for Short-Term Scheduling of Multipurpose Batch Plants.” Industrial and Engineering Chemistry Research 42: 3056–3074. doi: 10.1021/ie020923y
   Web of Science ®Google Scholar
• Michalevicz, Z. 1992. Genetic Algorithms + Data Structures = Evolution Programs. Berlin, Heidelberg: Springer.
   Google Scholar
• Oliveira, F., S. Hamacher, and M. R. Almeida. 2011. “Process Industry Scheduling Optimization Using Genetic Algorithm and Mathematical Programming.” Journal of Intelligent Manufacturing 22: 801–813. doi: 10.1007/s10845-009-0339-x
   Web of Science ®Google Scholar
• Omar, M. K., and S. C. Teo. 2007. “Hierarchical Production Planning and Scheduling in a Multi-product, Batch Process Environment.” International Journal of Production Research 45 (5): 1029–1047. doi: 10.1080/00207540600724989
   Web of Science ®Google Scholar
• Pinto, J. M., and I. E. Grossmann. 1998. “Assignment and Sequencing Models for the Scheduling of Process Systems.” Annals of Operations Research 81: 433–466. doi: 10.1023/A:1018929829086
   Web of Science ®Google Scholar
• Pochet, Y., and L. A. Wolsey. 2006. Production Planning by Mixed Integer Programming. New York: Springer Series in Operations Research and Financial Engineering.
   Google Scholar
• Qiu, M. M., and E. E. Burch. 1997. “Hierarchical Production Planning and Scheduling in a Multi-product, Multi-machine Environment.” International Journal of Production Research 35 (11): 3023–3042. doi: 10.1080/002075497194273
   Web of Science ®Google Scholar
• Reeves, C. 1997. “Genetic Algorithms for the Operations Researcher.” INFORMS Journal on Computing9 (3): 231–250. doi: 10.1287/ijoc.9.3.231
   Google Scholar
• Reynolds, A. P., and G. P. McKeown. 1999. “Scheduling a Manufacturing Plant Using Simulated Annealing and Simulation.” Computers and Industrial Engineering 37 (1–2): 63–67. doi: 10.1016/S0360-8352(99)00024-8
   Web of Science ®Google Scholar
• Salvador, M. S. 1973. “A Solution to a Special Class of Flow Shop Scheduling Problems.” In Symposium on the Theory of Scheduling and Its Applications, 83–91. Berlin: Springer.
   Google Scholar
• Sel, C., B. Bilgen, and J. Bloemhof-Ruwaard. 2017. “Planning and Scheduling of the Make-and-pack Dairy Production Under Lifetime Uncertainty.” Applied Mathematical Modelling 51: 129–144. doi: 10.1016/j.apm.2017.06.002
   Web of Science ®Google Scholar
• Sel, C., B. Bilgen, J. M. Bloemhof-Ruwaard, and J. G. van der Vorst. 2015. “Multi-bucket Optimization for Integrated Planning and Scheduling in the Perishable Dairy Supply Chain.” Computers and Chemical Engineering 77: 59–73. doi: 10.1016/j.compchemeng.2015.03.020
   Web of Science ®Google Scholar
• Shaik, M. A., C. A. Floudas, J. Kallrath, and H.-J. Pitz. 2009. “Production Scheduling of a Large-Scale Industrial Continuous Plant: Short-Term and Medium-Term Scheduling.” Computers and Chemical Engineering 33: 670–686. doi: 10.1016/j.compchemeng.2008.08.013
   Web of Science ®Google Scholar
• Stefansson, H., N. Shah, and P. Jensson. 2006. “Multiscale Planning and Scheduling in the Secondary Pharmaceutical Industry.” AIChE Journal 52 (12): 4133–4149. doi: 10.1002/aic.10989
   Web of Science ®Google Scholar
• Sundaramoorthy, A., and I. A. Karimi. 2005. “A Simpler Better Slot-based Continuous-time Formulation for Short-term Scheduling in Multipurpose Batch Plants.” Chemical Engineering Science 60: 2679–2702. doi: 10.1016/j.ces.2004.12.023
   Web of Science ®Google Scholar
• Velez, S., and C. T. Maravelias. 2014. “Advances in Mixed-Integer Programming Methods for Chemical Production Scheduling.” Annual Review of Chemical and Biomolecular Engineering 5: 97–121. doi: 10.1146/annurev-chembioeng-060713-035859
   PubMed Web of Science ®Google Scholar
• Venditti, L., D. Pacciarelli, and C. Meloni. 2010. “A Tabu Search Algorithm for Scheduling Pharmaceutical Packaging Operations.” European Journal of Operational Research 202: 538–546. doi: 10.1016/j.ejor.2009.05.038
   Web of Science ®Google Scholar