Review of Recent Uncertainty Strategies within Optimization Techniques
Keywords:
Robust optimization, Model Predictive Control, Stochastic Programming, Fuzzy programming methods, Rolling-horizon approach
Abstract
Despite the great progress in improvement methodologies, modernity may be a precedent for this progress. Actually, on the supply chain management scenario the decision-making becomes more challenging especially that various sources of model uncertainty are required to ensure the quality of the solution or even practical feasibility. Therefore, one of the most pressing problems today is incorporating variability in process parameters such as manufacturing time and reaction conditions. In this paper, some interactive methods are summarized that modify the actual plan obtained from the authoritative version of the system to correspond to the modifications or updated system data. Finally, the methods of dealing with problems were divided into two main approaches, the reactive approach and the preventive approach.
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References
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41. Safaei, N., Saidi-Mehrabad, M., Tavakkoli-Moghaddam, R., & Sassani, F. (2008). A fuzzy programming approach for a cell formation problem with dynamic and uncertain conditions. Fuzzy Sets and Systems, 159(2), 215-236.
42. Lima, C., Relvas, S., & Barbosa-Póvoa, A. (2021). Designing and planning the downstream oil supply chain under uncertainty using a fuzzy programming approach. Computers & Chemical Engineering, 151, 107373.
43. Sahinidis, N. V. (2004). Optimization under uncertainty: state-of-the-art and opportunities. Computers & Chemical Engineering, 28(6-7), 971-983.
44. Sun, Y., Liang, X., Li, X., & Zhang, C. (2019). A fuzzy programming method for modeling demand uncertainty in the capacitated road–rail multimodal routing problem with time windows. Symmetry, 11(1), 91.
45. Wu, G. H., Chang, C. K., & Hsu, L. M. (2018). Comparisons of interactive fuzzy programming approaches for closed-loop supply chain network design under uncertainty. Computers & Industrial Engineering, 125, 500-513.
46. Pourjavad, E., & Mayorga, R. V. (2019). A comparative study on fuzzy programming approaches to design a sustainable supply chain under uncertainty. Journal of Intelligent & Fuzzy Systems, 36(3), 2947-2961.
47. Wang, S., & Huang, G. H. (2012). Identifying optimal water resources allocation strategies through an interactive multi-stage stochastic fuzzy programming approach. Water resources management, 26(7), 2015-2038.
48. Liu, M. L., & Sahinidis, N. V. (1996). Optimization in process planning under uncertainty. Industrial & Engineering Chemistry Research, 35(11), 4154-4165.
49. Zhang, Z., Zhou, M., Ou, G., Tan, S., Song, Y., Zhang, L., & Nie, X. (2019). Land suitability evaluation and an interval stochastic fuzzy programming-based optimization model for land-use planning and environmental policy analysis. International Journal of Environmental Research and Public Health, 16(21), 4124.
2. Foulds, L. R. (2012). Optimization techniques: an introduction. Springer Science & Business Media.
3. Onwubolu, G. C., & Babu, B. V. (2013). New optimization techniques in engineering (Vol. 141). Springer.
4. Horrocks, I. (2003). Implementation and optimization techniques. In The description logic handbook: theory, implementation, and applications (pp. 306-346).
5. Mukherjee, I., & Ray, P. K. (2006). A review of optimization techniques in metal cutting processes. Computers & Industrial Engineering, 50(1-2), 15-34.
6. Balasubramanian, K., Thanikanti, S. B., Subramaniam, U., Sudhakar, N., & Sichilalu, S. (2020). A novel review on optimization techniques used in wind farm modelling. Renewable Energy Focus, 35, 84-96.
7. Al-Jilawi, A. S., & Abd Alsharify, F. H. (2022). Review of Mathematical Modelling Techniques with Applications in Biosciences. Iraqi Journal For Computer Science and Mathematics, 3(1), 135-144.
8. Kadhim, M. K., Wahbi, F. A., & Hasan Alridha, A. (2022). Mathematical optimization modeling for estimating the incidence of clinical diseases. International Journal of Nonlinear Analysis and Applications, 13(1), 185-195.
9. Alridha, A., Wahbi, F. A., & Kadhim, M. K. (2021). Training analysis of optimization models in machine learning. International Journal of Nonlinear Analysis and Applications, 12(2), 1453-1461.
10. Diwekar, U. M. (2020). Optimization under uncertainty. In Introduction to Applied Optimization (pp. 151-215). Springer, Cham.
11. Sahinidis, N. V. (2004). Optimization under uncertainty: state-of-the-art and opportunities. Computers & Chemical Engineering, 28(6-7), 971-983.
12. Jin, R., Du, X., & Chen, W. (2003). The use of metamodeling techniques for optimization under uncertainty. Structural and Multidisciplinary Optimization, 25(2), 99-116.
13. Ning, C., & You, F. (2019). Optimization under uncertainty in the era of big data and deep learning: When machine learning meets mathematical programming. Computers & Chemical Engineering, 125, 434-448.
14. Jaillet, P., Qi, J., & Sim, M. (2016). Routing optimization under uncertainty. Operations research, 64(1), 186-200.
15. Keith, A. J., & Ahner, D. K. (2021). A survey of decision making and optimization under uncertainty. Annals of Operations Research, 300(2), 319-353.
16. Camacho, E. F., & Alba, C. B. (2013). Model predictive control. Springer science & business media.
17. Kouvaritakis, B., & Cannon, M. (2016). Model predictive control. Switzerland: Springer International Publishing, 38.
18. Allgöwer, F., & Zheng, A. (Eds.). (2012). Nonlinear model predictive control (Vol. 26). Birkhäuser.
19. Holkar, K. S., & Waghmare, L. M. (2010). An overview of model predictive control. International Journal of control and automation, 3(4), 47-63.
20. Grüne, L., & Pannek, J. (2017). Nonlinear model predictive control. In Nonlinear model predictive control (pp. 45-69). Springer, Cham.
21. Raković, S. V., & Levine, W. S. (Eds.). (2018). Handbook of model predictive control. Springer.
22. Lu, C. C., Ying, K. C., & Chen, H. J. (2016). Real-time relief distribution in the aftermath of disasters–A rolling horizon approach. Transportation research part E: logistics and transportation review, 93, 1-20.
23. Zaneti, L. A., Arias, N. B., de Almeida, M. C., & Rider, M. J. (2022). Sustainable charging schedule of electric buses in a University Campus: A rolling horizon approach. Renewable and Sustainable Energy Reviews, 161, 112276.
24. Addis, B., Carello, G., Grosso, A., & Tànfani, E. (2016). Operating room scheduling and rescheduling: a rolling horizon approach. Flexible Services and Manufacturing Journal, 28(1), 206-232.
25. Wu, O., Dalle Ave, G., Harjunkoski, I., & Imsland, L. (2021). A rolling horizon approach for scheduling of multiproduct batch production and maintenance using generalized disjunctive programming models. Computers & Chemical Engineering, 148, 107268.
26. Glomb, L., Liers, F., & Rösel, F. (2022). A rolling-horizon approach for multi-period optimization. European Journal of Operational Research, 300(1), 189-206.
27. Silvente, J., Kopanos, G. M., Dua, V., & Papageorgiou, L. G. (2018). A rolling horizon approach for optimal management of microgrids under stochastic uncertainty. Chemical Engineering Research and Design, 131, 293-317.
28. Kamran, M. A., Karimi, B., Dellaert, N., & Demeulemeester, E. (2019). Adaptive operating rooms planning and scheduling: A rolling horizon approach. Operations Research for Health Care, 22, 100200.
29. Shapiro, A. (2008). Stochastic programming approach to optimization under uncertainty. Mathematical Programming, 112(1), 183-220.
30. Shapiro, A., Dentcheva, D., & Ruszczynski, A. (2021). Lectures on stochastic programming: modeling and theory. Society for Industrial and Applied Mathematics.
31. Yoon, S., Albert, L. A., & White, V. M. (2021). A stochastic programming approach for locating and dispatching two types of ambulances. Transportation Science, 55(2), 275-296.
32. Xu, X., & Birge, J. R. (2006). Equity valuation, production, and financial planning: A stochastic programming approach. Naval Research Logistics (NRL), 53(7), 641-655.
33. Mavromatidis, G., Orehounig, K., & Carmeliet, J. (2018). Design of distributed energy systems under uncertainty: A two-stage stochastic programming approach. Applied energy, 222, 932-950.
34. Birge, J. R., & Louveaux, F. (2011). Introduction to stochastic programming. Springer Science & Business Media.
35. Bruni, M. E., Beraldi, P., & Conforti, D. (2015). A stochastic programming approach for operating theatre scheduling under uncertainty. IMA Journal of Management Mathematics, 26(1), 99-119.
36. Beraldi, P., Violi, A., Carrozzino, G., & Bruni, M. E. (2018). A stochastic programming approach for the optimal management of aggregated distributed energy resources. Computers & Operations Research, 96, 200-212.
37. King, A. J., & Wallace, S. W. (2012). Modeling with stochastic programming. Springer Science & Business Media.
38. Lin, X., Janak, S. L., & Floudas, C. A. (2004). A new robust optimization approach for scheduling under uncertainty:: I. Bounded uncertainty. Computers & chemical engineering, 28(6-7), 1069-1085.
39. Bertsimas, D., & Thiele, A. (2006). A robust optimization approach to inventory theory. Operations research, 54(1), 150-168.
40. Tsao, Y. C., & Thanh, V. V. (2020). A multi-objective fuzzy robust optimization approach for designing sustainable and reliable power systems under uncertainty. Applied Soft Computing, 92, 106317.
41. Safaei, N., Saidi-Mehrabad, M., Tavakkoli-Moghaddam, R., & Sassani, F. (2008). A fuzzy programming approach for a cell formation problem with dynamic and uncertain conditions. Fuzzy Sets and Systems, 159(2), 215-236.
42. Lima, C., Relvas, S., & Barbosa-Póvoa, A. (2021). Designing and planning the downstream oil supply chain under uncertainty using a fuzzy programming approach. Computers & Chemical Engineering, 151, 107373.
43. Sahinidis, N. V. (2004). Optimization under uncertainty: state-of-the-art and opportunities. Computers & Chemical Engineering, 28(6-7), 971-983.
44. Sun, Y., Liang, X., Li, X., & Zhang, C. (2019). A fuzzy programming method for modeling demand uncertainty in the capacitated road–rail multimodal routing problem with time windows. Symmetry, 11(1), 91.
45. Wu, G. H., Chang, C. K., & Hsu, L. M. (2018). Comparisons of interactive fuzzy programming approaches for closed-loop supply chain network design under uncertainty. Computers & Industrial Engineering, 125, 500-513.
46. Pourjavad, E., & Mayorga, R. V. (2019). A comparative study on fuzzy programming approaches to design a sustainable supply chain under uncertainty. Journal of Intelligent & Fuzzy Systems, 36(3), 2947-2961.
47. Wang, S., & Huang, G. H. (2012). Identifying optimal water resources allocation strategies through an interactive multi-stage stochastic fuzzy programming approach. Water resources management, 26(7), 2015-2038.
48. Liu, M. L., & Sahinidis, N. V. (1996). Optimization in process planning under uncertainty. Industrial & Engineering Chemistry Research, 35(11), 4154-4165.
49. Zhang, Z., Zhou, M., Ou, G., Tan, S., Song, Y., Zhang, L., & Nie, X. (2019). Land suitability evaluation and an interval stochastic fuzzy programming-based optimization model for land-use planning and environmental policy analysis. International Journal of Environmental Research and Public Health, 16(21), 4124.
Published
2022-06-12
How to Cite
Ahmed Hasan ALRIDHA, Ekhlas Annon Mousa, & Ahmed Sabah Al-Jilawi. (2022). Review of Recent Uncertainty Strategies within Optimization Techniques. Central Asian Journal of Theoretical and Applied Science, 3(6), 160-170. https://doi.org/10.17605/OSF.IO/WHTR4
Section
Articles
Copyright (c) 2022 Ahmed Hasan ALRIDHA, Ekhlas Annon Mousa, Ahmed Sabah Al-Jilawi
This work is licensed under a Creative Commons Attribution 4.0 International License.
