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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

Mixed integer programming approaches for nonlinear and stochastic programming

Vielma Centeno, Juan Pablo. January 2009 (has links)
Thesis (Ph.D)--Industrial and Systems Engineering, Georgia Institute of Technology, 2010. / Committee Chair: Nemhauser, George; Committee Co-Chair: Ahmed, Shabbir; Committee Member: Bill Cook; Committee Member: Gu, Zonghao; Committee Member: Johnson, Ellis. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Stochastic network interdiction: models and methods

Pan, Feng 28 August 2008 (has links)
Not available / text

Approximate method for solving two-stage stochastic programming and its application to the groundwater management

Wang, Maili. January 1999 (has links)
Stochastic two-stage programming, a main branch of stochastic programming, offers models and methods to find the optimal objective function and decision variables under uncertainty. This dissertation is concerned with developing an approximate procedure to solve the stochastic two-stage programming problem and applying it in relative field. Five methods used in evaluating the expected value of function for distribution problem are discussed and their basic characteristics and performances are compared to choose the most effective approach for use in a two-stage program. Then the stochastic two-stage programming solving method has been established with the combination of a genetic algorithm (GA) and point estimation (PE) procedure. This approach avoids the inherent limitations of other methods by using PE to estimate the expected value of recourse function and the GA to search optimal solution of the problem. To extend the advantage of GA the modified genetic algorithm (MGA) is built to improve the performance of GA. Finally, the whole procedure is used in several examples with different kinds of variable and linear or nonlinear style objective functions. A stochastic two-stage programming model for an aquifer management problem is set up with considering conductivity and local random recharge as the source of uncertainty in the system. The designed procedure includes the response matrix process that replaces the partial differential flow equation, Girinski potential process and a pre-setup process that makes the response matrix process application in general aquifer random field possible. Other chosen problems are solved with designed approach to illustrate the effects of uncertainty source in the stochastic programming model and compared with results with ones given in literatures.

Two-stage stochastic linear programming: Stochastic decomposition approaches.

Yakowitz, Diana Schadl. January 1991 (has links)
Stochastic linear programming problems are linear programming problems for which one or more data elements are described by random variables. Two-stage stochastic linear programming problems are problems in which a first stage decision is made before the random variables are observed. A second stage, or recourse decision, which varies with these observations compensates for any deficiencies which result from the earlier decision. Many applications areas including water resources, industrial management, economics and finance lead to two-stage stochastic linear programs with recourse. In this dissertation, two algorithms for solving stochastic linear programming problems with recourse are developed and tested. The first is referred to as Quadratic Stochastic Decomposition (QSD). This algorithm is an enhanced version of the Stochastic Decomposition (SD) algorithm of Higle and Sen (1988). The enhancements were designed to increase the computational efficiency of the SD algorithm by introducing a quadratic proximal term in the master program objective function and altering the manner in which the recourse function approximations are updated. We show that every accumulation point of an easily identifiable subsequence of points generated by the algorithm are optimal solutions to the stochastic program with probability 1. The various combinations of the enhancements are empirically investigated in a computational experiment using operations research problems from the literature. The second algorithm is an SD based algorithm for solving a stochastic linear program in which the recourse problem appears in the constraint set. This algorithm involves the use of an exact penalty function in the master program. We find that under certain conditions every accumulation point of a sequence of points generated by the algorithm is an optimal solution to the recourse constrained stochastic program, with probability 1. This algorithm is tested on several operations research problems.

Deterministic approximations in stochastic programming with applications to a class of portfolio allocation problems

Dokov, Steftcho Pentchev 09 March 2011 (has links)
Not available / text

Input specifications to stochastic decision models

Clainos, Deme Michael, 1943- January 1972 (has links)
No description available.

Statistical modeling of the value function in high-dimensional, continuous-state SDP

Tsai, Julia Chia-Chieh 08 1900 (has links)
No description available.

Behavior of fork-join networks, and effect of variability in service systems

Ko, Sung-Seok 08 1900 (has links)
No description available.

Simulation-based methods for stochastic optimization

Homem de Mello, Tito 08 1900 (has links)
No description available.

Task Optimization and Workforce Scheduling

Shateri, Mahsa 31 August 2011 (has links)
This thesis focuses on task sequencing and manpower scheduling to develop robust schedules for an aircraft manufacturer. The production of an aircraft goes through a series of multiple workstations, each consisting of a large number of interactive tasks and a limited number of working zones. The duration of each task varies from operator to operator, because most operations are performed manually. These factors limit the ability of managers to balance, optimize, and change the statement of work in each workstation. In addition, engineers spend considerable amount of time to manually develop schedules that may be incompatible with the changes in the production rate. To address the above problems, the current state of work centers are first analyzed. Then, several deterministic mathematical programming models are developed to minimize the total production labour cost for a target cycle time. The mathematical models seek to find optimal schedules by eliminating and/or considering the effect of overtime on the production cost. The resulting schedules decrease the required number of operators by 16% and reduce production cycle time of work centers by 53% to 67%. Using these models, the time needed to develop a schedule is reduced from 36 days to less than a day. To handle the stochasticity of the task durations, a two-stage stochastic programming model is developed to minimize the total production labour cost and to find the number of operators that are able to work under every scenario. The solution of the two-stage stochastic programming model finds the same number of operators as that of the deterministic models, but reduces the time to adjust production schedules by 88%.

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