of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy DECOMPOSITION ALGORITHMS FOR TWO-STAGE STOCHASTIC INTEGER PROGRAMMING By John H. Penuel, Jr. August 2009 Chair: J. Cole Smith Major: Industrial and Systems Engineering Stochastic programming seeks to optimize decision making in uncertain...

This paper applies two-stage stochastic quadratic programming to optimize conjunctive use operations of groundwater pumping and artificial recharge with farmer’s expected revenue and cropping decisions. The two-stage programming approach allows modeling of water and permanent crop production decisions, with recourse for uncertain conditions of hydrology, annual crops, and irrigation technology ...

This paper presents an application to the multi-objective stochastic transportation problem in fuzzy environment. In this paper, we focus on our attention to multiobjective stochastic transportation problem involving an inequality type of constraints in which all parameters ( supply and demand ) are log-normal random variable and the objectives are non-commensurable and conflicting in nature. A...

We seek to optimize the production planning of a three-echelon remanufacturing system under uncertain input data. consider multi-stage stochastic integer programming approach and use scenario trees represent information structure. introduce new dynamic formulation that relies on partial nested decomposition tree. then propose approximate dual algorithm based this decomposition. Our numerical re...

We consider a model of medium-term commodity contracts management. Randomness takes place only in the prices on which the commodities are exchanged, whilst state variable is multi-dimensional, and decision variable is integer. In [7], we proposed an algorithm based on the quantization of random process and a dual dynamic programming type approach to solve the continuous relaxation problem. In t...

multistage stochastic programming is a key technology for making decisions over time in an uncertain environment. one of the promising areas in which this technology is implementable, is medium term planning of electricity production and trading where decision makers are typically faced with uncertain parameters (such as future demands and market prices) that can be described by stochastic proc...

We study logistical investment flexibility provided by modular processing technologies for mitigating risk. Specifically, we propose a multi-stage stochastic programming formulation that determines optimal capacity expansion plans mitigate demand uncertainty. The accounts multi-product dependencies between small/large units and trade-offs expected profit uses cumulative risk measure to avoid ti...

This thesis develops integer linear programming models for and studies the complexity of problems in the areas of maintenance optimization and location–routing. We study how well the polyhedra defined by the linear programming relaxation of the models approximate the convex hull of the integer feasible solutions. Four of the papers consider a series of maintenance decision problems whereas the ...