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Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 80 for “"Stochastic Programming"”.
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On Stochastic Programming
Made available in DSpace on 2014-12-10T23:09:00Z (GMT). No. of bitstreams: 1 7412032.pdf: 1769177 bytes, checksum: 26c51c00e3e54d31f5d03a58b6825749 (MD5) Previous issue date: 1973
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Stochastic Programming Mode Models
Made available in DSpace on 2014-12-10T23:09:03Z (GMT). No. of bitstreams: 1 7500268.pdf: 2261339 bytes, checksum: 8e2c08aa2693c7489c7331a3a496ea8f (MD5) Previous issue date: 1974
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Multi-stage Stochastic Programming Models in Production Planning
… we study a series of closely related multi-stage stochastic programming models in production planning, from both a modeling and an algorithmic point of view. We first consider a very simple multi-stage stochastic lot-sizing problem, involving a single item with no fixed charge and capacity …
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Stochastic programming models for interest-rate risk management
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, 1994.
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Sampling based progressive hedging algorithms for stochastic programming problems
… can be estimated to a certain degree, stochastic programming (SP) methodologies are used to identify robust plans. Despite advances in SP, it is still a challenge to solve real world stochastic programming problems, in part due to the exponentially increasing number of scenarios. For …
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Stochastic programming with recourse applied to groundwater quality management
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, 1988.
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Minimizing electricity costs with an auxiliary generator using stochastic programming
… The building load is the random factor of the stochastic problem and is composed of the weather load and the occupancy load. Several optimization techniques such as Dynamic Programming and Linear Programming (deterministic optimization) are looked at. Stochastic Programming using Nested …
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A stochastic programming framework for financial intermediaries liquidity in South Africa
… optimisation. We propose a novel multistage stochastic programming methodology for liquid asset control. Thus we define how to construct and solve stochastic programming models for liquidity needs-driven sub-portfolios. Our approach is based on scenario trees and makes no assumption on the …
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Stochastic Programming Approaches to Multi-product Inventory Management Problems with Substitution
… a binary quadratic program. When the demand is stochastic, we formulate the problem as a two-stage stochastic program with mixed integer recourse, derive several necessary optimality conditions, prove the submodularity of the profit function, develop polynomial-time approximation algorithms, and …
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The Markov chain Monte Carlo approach to importance sampling in stochastic programming
Stochastic programming models are large-scale optimization problems that are used to facilitate decision-making under uncertainty. Optimization algorithms for such problems need to evaluate the expected future costs of current decisions, often referred to as the recourse function. In practice, this …
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A Firm-Level Model for Commercial Banks Servicing Agriculture: A Multi-Stage Stochastic Programming Approach
The model results depend upon the model specification as well as the linkage between the bank and its external environments. In general, the differences between the balance sheet decisions made by the bank under the alternative scenarios are not large. Major findings of this study are (1) …
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Optimal inventory control for assemble-to-order systems: A stochastic programming based asymptotic analysis framework
… the technical challenges, we develop a four-step Stochastic Programming (SP) based asymptotic analysis framework. The SP model, as a surrogate model to ATO systems, is easier to solve and can provide vital guidance in developing novel inventory control policies. And asymptotic analysis can be used …
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A Critical Examination of the Use of Deterministic Models in Budgeting Problems: A Stochastic Programming Approach
Made available in DSpace on 2014-12-11T21:53:30Z (GMT). No. of bitstreams: 1 7511523.pdf: 7754106 bytes, checksum: c72b0e243c2b9d498edc4daa699c8e26 (MD5) Previous issue date: 1974
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A spatial stochastic programming model for timber and core area management under risk of stand-replacing fire
… has been modeled using deterministic and stochastic programming models. Past models seldom address explicit spatial forest management concerns under the influence of natural disturbances. In this research study, we employ multistage full recourse stochastic programming models to explore …
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A reliability-based method for optimization programming problems
… study, a method is developed to solve general stochastic programming problems. The method is applicable to both linear and nonlinear optimization. Based on a proper linearization, a set of probabilistic constraints (performance functions) can be transformed into a corresponding set of …
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Development of GHG-Mitigation Oriented Models for the Planning of Integrated Energy-Environment Systems (IEES) Under Uncertainties
… models include: (a) an interval multi-stage stochastic programming model (IMSP-IEES), (b) an interval fuzzy multi-stage stochastic programming model (IFMP-IEES), (c) a dual-interval mixed-integer linear programming model (DMLP-IEES), (d) a dual-interval multi-stage stochastic programming …
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Stochastic Cellular Manufacturing System Design and Control
… system (CMS) performance are addressed and stochastic optimization approaches are developed and applied to ten case problems from industrial companies and cellular manufacturing literature. This dissertation consists of mainly three phases, namely: stochastic CMS design, stochastic CMS …
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Stochastic Dynamic Programming for Optimal Reservoir Control
… analyses were performed to examine typical stochastic programming (SP) modeling issues for a hypothetical single reservoir system. The elements considered in these analyses include the partitions of inflow and storage states, the hydrologic characteristics of inflows, the types of system …
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Optimal draining of fluid networks with parameter uncertainty
… of the problem. We formulate both schemes using stochastic programming techniques. The first scheme is easier to analyze since the resulting model is convex. Unfortunately, under the second decision scheme, the objective function is non-convex. We develop a branch-and-bound methodology to solve …
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