Enhancing intelligence source performance management through two-stage stochastic programming and machine learning techniques [PDF]
Frontiers in Big DataIntroductionThe effectiveness of intelligence operations depends heavily on the reliability and performance of human intelligence (HUMINT) sources. Yet, source behavior is often unpredictable, deceptive or shaped by operational context, complicating ...
Lucas Wafula Wekesa, Stephen Korir
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A Monte Carlo-Based Framework for Two-Stage Stochastic Programming: Application to Bond Portfolio Optimization [PDF]
EntropyThis paper presents a Monte Carlo simulation-based approach for solving stochastic two-stage bond portfolio optimization problems. The main objective is to optimize the cost of the bond portfolio while making decisions on bond purchases, holdings, and ...
Hissah Albaqami +3 more
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A two-stage stochastic programming approach for care providers' shift scheduling problems [PDF]
Journal of Applied Research on Industrial Engineering, 2023 Due to the importance of the health field, the problem of determining the shift scheduling of care providers has been addressed in many studies, and various methods have been proposed to solve it.
Hajar Shirneshan +3 more
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A Weighted Robust Two-Stage Stochastic Optimization Model for Supplier Selection and Order Allocation under Uncertainty [PDF]
چشمانداز مدیریت صنعتی, 2020 This paper presents an integrated model as a combination of fuzzy analytical hierarchy process (FAHP), scenario-based two-stage stochastic programming and robust optimization approaches for the problem of supplier selection and order allocation under ...
Mostafa Jokar +2 more
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Natural gas supply chain under uncertainty condition [PDF]
New Applied Studies in Management, Economics & Accounting, 2023 In today’s competitive world, uncertainty is an integral part of all optimization problems. One of the cases where uncertainty has the greatest impact on optimization issues is SCN design.
Reza Mohammadi
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Differential Stability of Two-Stage Stochastic Programs [PDF]
SIAM Journal on Optimization, 2000 Summary: Two-stage stochastic programs with random right-hand side are considered. Optimal values and solution sets are regarded as mappings of the expected recourse functions and their perturbations, respectively. Conditions are identified implying that these mappings are directionally differentiable and semidifferentiable on appropriate functional ...
Darinka Dentcheva, Werner Römisch
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A Review of Stochastic Programming Methods for Optimization of Process Systems Under Uncertainty
Frontiers in Chemical Engineering, 2021 Uncertainties are widespread in the optimization of process systems, such as uncertainties in process technologies, prices, and customer demands. In this paper, we review the basic concepts and recent advances of a risk-neutral mathematical framework ...
Can Li, Ignacio E. Grossmann
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Neur2SP: Neural Two-Stage Stochastic Programming
Advances in Neural Information Processing Systems 35, 2022 Stochastic Programming is a powerful modeling framework for decision-making under uncertainty. In this work, we tackle two-stage stochastic programs (2SPs), the most widely used class of stochastic programming models. Solving 2SPs exactly requires optimizing over an expected value function that is computationally intractable.
Justin Dumouchelle +3 more
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A Stochastic Programming Approach for a Multi-Site Supply Chain Planning in Textile and Apparel Industry under Demand Uncertainty [PDF]
International Journal of Supply and Operations Management, 2015 In this study, a new stochastic model is proposed to deal with a multi-product, multi-period, multi-stage, multi-site production and transportation supply chain planning problem under demand uncertainty. A two-stage stochastic linear programming approach
Houssem Felfel +2 more
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Multistage stochastic programming modeling for farmland irrigation management under uncertainty.
PLoS ONE, 2020 Farmland management and irrigation scheduling are vital to a productive agricultural economy. A multistage stochastic programming model is proposed to maximize farmers' annual profit under uncertainty.
Qi Li, Guiping Hu
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