Stochastic Optimization Models for Supply Chain Management: Integrating Uncertainty into Decision-Making Processes
DOI:
https://doi.org/10.18034/gdeb.v7i2.725Keywords:
Stochastic Optimization, Supply Chain Management, Uncertainty Integration, Modeling Uncertainty, Decision Processes, Probabilistic Models, Logistics OptimizationAbstract
This study examines how supply chain management can use stochastic optimization models to overcome the problems associated with decision-making uncertainty. The study's primary goals are reviewing the literature on stochastic optimization models in supply chain management, gaining a thorough grasp of their applications, and evaluating how well they integrate uncertainty into decision-making processes. The method includes a comprehensive assessment of the current literature body, including scholarly journals, conference proceedings, and reliable web sources to obtain pertinent data and insights. The significance of incorporating uncertainty into decision-making procedures, the adaptability of stochastic optimization models for diverse supply chain functions, and their function in augmenting supply chain resilience via proactive risk mitigation and sound decision-making are among the principal discoveries. The policy implications indicate that investments in data analytics capabilities, capacity building, training programs, and regulatory frameworks are required to facilitate the implementation of stochastic optimization models in supply chain management. This study advances knowledge in supply chain management and informs future research and practice.
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Ahranjani, A. R., Seifbarghy, M., Bozorgi-Amiri, A., Najafi, E. (2018). Closed-loop Supply Chain Network Design for the Paper Industry: A Multi-objective Stochastic Robust Approach. Scientia Iranica. Transaction E, Industrial Engineering, 25(5), 2881-2903. https://doi.org/10.24200/sci.2017.4464 DOI: https://doi.org/10.24200/sci.2017.4464
Akbari, A. A., Karimi, B. (2015). A New Robust Optimization Approach for Integrated Multi-echelon, Multi-product, Multi-period Supply Chain Network Design Under Process Uncertainty. The International Journal of Advanced Manufacturing Technology, 79(1-4), 229-244. https://doi.org/10.1007/s00170-015-6796-9 DOI: https://doi.org/10.1007/s00170-015-6796-9
Alfieri, A., Tolio, T., Urgo, M. (2012). A Two-Stage Stochastic Programming Project Scheduling Approach to Production Planning. The International Journal of Advanced Manufacturing Technology, 62(1-4), 279-290. https://doi.org/10.1007/s00170-011-3794-4 DOI: https://doi.org/10.1007/s00170-011-3794-4
Ande, J. R. P. K. (2018). Performance-Based Seismic Design of High-Rise Buildings: Incorporating Nonlinear Soil-Structure Interaction Effects. Engineering International, 6(2), 187–200. https://doi.org/10.18034/ei.v6i2.691 DOI: https://doi.org/10.18034/ei.v6i2.691
Ande, J. R. P. K., Varghese, A., Mallipeddi, S. R., Goda, D. R., & Yerram, S. R. (2017). Modeling and Simulation of Electromagnetic Interference in Power Distribution Networks: Implications for Grid Stability. Asia Pacific Journal of Energy and Environment, 4(2), 71-80. https://doi.org/10.18034/apjee.v4i2.720 DOI: https://doi.org/10.18034/apjee.v4i2.720
Baddam, P. R., & Kaluvakuri, S. (2016). The Power and Legacy of C Programming: A Deep Dive into the Language. Technology & Management Review, 1, 1-13. https://upright.pub/index.php/tmr/article/view/107
Campanur, A. G., Olivares-Benitez, E., Miranda, P. A., Perez-Loaiza, R. E., Ablanedo-Rosas, J. H. (2018). Design of a Logistics Nonlinear System for a Complex, Multiechelon, Supply Chain Network with Uncertain Demands. Complexity, 2018. https://doi.org/10.1155/2018/4139601 DOI: https://doi.org/10.1155/2018/4139601
Csaji, B. C., Monostori, L. (2008). Adaptive Stochastic Resource Control: A Machine Learning Approach. The Journal of Artificial Intelligence Research, 32, 453-486. https://doi.org/10.1613/jair.2548 DOI: https://doi.org/10.1613/jair.2548
Franco, C., Alfonso-Lizarazo, E. (2017). A Structured Review of Quantitative Models of the Pharmaceutical Supply Chain. Complexity, 2017. https://doi.org/10.1155/2017/5297406 DOI: https://doi.org/10.1155/2017/5297406
Glazebrook, K. D., Hodge, D. J., Kirkbride, C., Minty, R. J. (2014). Stochastic Scheduling: A Short History of Index Policies and New Approaches to Index Generation for Dynamic Resource Allocation. Journal of Scheduling, 17(5), 407-425. https://doi.org/10.1007/s10951-013-0325-1 DOI: https://doi.org/10.1007/s10951-013-0325-1
Goda, D. R. (2016). A Fully Analytical Back-gate Model for N-channel Gallium Nitrate MESFET's with Back Channel Implant. California State University, Northridge. http://hdl.handle.net/10211.3/176151
Kaluvakuri, S., & Vadiyala, V. R. (2016). Harnessing the Potential of CSS: An Exhaustive Reference for Web Styling. Engineering International, 4(2), 95–110. https://doi.org/10.18034/ei.v4i2.682 DOI: https://doi.org/10.18034/ei.v4i2.682
Mahadasa, R. (2016). Blockchain Integration in Cloud Computing: A Promising Approach for Data Integrity and Trust. Technology & Management Review, 1, 14-20. https://upright.pub/index.php/tmr/article/view/113
Mahadasa, R., & Surarapu, P. (2016). Toward Green Clouds: Sustainable Practices and Energy-Efficient Solutions in Cloud Computing. Asia Pacific Journal of Energy and Environment, 3(2), 83-88. https://doi.org/10.18034/apjee.v3i2.713 DOI: https://doi.org/10.18034/apjee.v3i2.713
Mallipeddi, S. R., Goda, D. R., Yerram, S. R., Varghese, A., & Ande, J. R. P. K. (2017). Telemedicine and Beyond: Navigating the Frontier of Medical Technology. Technology & Management Review, 2, 37-50. https://upright.pub/index.php/tmr/article/view/118
Mallipeddi, S. R., Lushbough, C. M., & Gnimpieba, E. Z. (2014). Reference Integrator: a workflow for similarity driven multi-sources publication merging. The Steering Committee of the World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp). https://www.proquest.com/docview/1648971371
Rafiee, M., Kianfar, F., Farhadkhani, M. (2014). A Multistage Stochastic Programming Approach in Project Selection and Scheduling. The International Journal of Advanced Manufacturing Technology, 70(9-12), 2125-2137. https://doi.org/10.1007/s00170-013-5362-6 DOI: https://doi.org/10.1007/s00170-013-5362-6
Shahrooz, S., Prem, C., Chan, C., Hossein, A. (2018). Modular Recycling Supply Chain Under Uncertainty: A Robust Optimisation Approach. The International Journal of Advanced Manufacturing Technology, 96(1-4), 915-934. https://doi.org/10.1007/s00170-017-1530-4 DOI: https://doi.org/10.1007/s00170-017-1530-4
Surarapu, P. (2016). Emerging Trends in Smart Grid Technologies: An Overview of Future Power Systems. International Journal of Reciprocal Symmetry and Theoretical Physics, 3, 17-24. https://upright.pub/index.php/ijrstp/article/view/114
Surarapu, P., & Mahadasa, R. (2017). Enhancing Web Development through the Utilization of Cutting-Edge HTML5. Technology & Management Review, 2, 25-36. https://upright.pub/index.php/tmr/article/view/115
Vadiyala, V. R., & Baddam, P. R. (2017). Mastering JavaScript’s Full Potential to Become a Web Development Giant. Technology & Management Review, 2, 13-24. https://upright.pub/index.php/tmr/article/view/108
Vadiyala, V. R., Baddam, P. R., & Kaluvakuri, S. (2016). Demystifying Google Cloud: A Comprehensive Review of Cloud Computing Services. Asian Journal of Applied Science and Engineering, 5(1), 207–218. https://doi.org/10.18034/ajase.v5i1.80 DOI: https://doi.org/10.18034/ajase.v5i1.80
Vahdani, B. (2015). An Optimization Model for Multi-objective Closed-loop Supply Chain Network under uncertainty: A Hybrid Fuzzy-stochastic Programming Method. Iranian Journal of Fuzzy Systems, 12(4), 33-57. https://doi.org/10.22111/ijfs.2015.2084
Vahdani, B., Naderi-Beni, M. (2014). A Mathematical Programming Model for Recycling Network Design Under Uncertainty: An Interval-stochastic Robust Optimization Model. The International Journal of Advanced Manufacturing Technology, 73(5-8), 1057-1071. https://doi.org/10.1007/s00170-014-5852-1 DOI: https://doi.org/10.1007/s00170-014-5852-1
Yerram, S. R., & Varghese, A. (2018). Entrepreneurial Innovation and Export Diversification: Strategies for India’s Global Trade Expansion. American Journal of Trade and Policy, 5(3), 151–160. https://doi.org/10.18034/ajtp.v5i3.692 DOI: https://doi.org/10.18034/ajtp.v5i3.692
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Copyright (c) 2018 Dileep Reddy Goda, Sridhar Reddy Yerram, Suman Reddy Mallipeddi
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