A STOCHASTIC PROGRAMMING MODEL FOR AGRICULTURAL PLANNING UNDER UNCERTAIN SUPPLY -DEMAND RELATIONS Teruaki Nanseki NatiolUlI Agriculture Research Center (Received April 15,1988; Revised October 28, 1988) The main purpose of this paper is to present a stochastic programming model for agricultural planning under uncertain supply-demand relations and to propose an algorithm for the model. We first ...

Production planning is a necessary process that directly affects the efficiency of production systems in most industries. The complexity current problem depends on increased options production, uncertainties demand and resources. In this study, stochastic multi-objective mixed-integer optimization model developed to ensure uncertainty conditions satisfy requirements sustainable development. sys...

In this paper, we study a stochastic version of the fault-tolerant facility location problem. By exploiting the stochastic structure, we propose a 5-approximation algorithm which uses the LProunding technique based on the revised optimal solution to the linear programming relaxation of the stochastic fault-tolerant facility location problem.

Geometric programming is efficient tool for solving a variety of nonlinear optimizationproblems. Geometric programming is generalized for solving engineering design. However,Now Geometric programming is powerful tool for optimization problems where decisionvariables have exponential form.The geometric programming method has been applied with known parameters. However,the observed values of the ...

We consider two-stage stochastic programming problems with integer recourse. The L-shaped method of stochastic linear programming is generalized to these problems by using generalized Benders decomposition. Nonlinear feasibility and optimality cuts are determined via general duality theory and can be generated when the second stage problem is solved by standard techniques. Finite convergence of...

We define a regularized variant of the Dual Dynamic Programming algorithm called REDDP (REgularized Dual Dynamic Programming) to solve nonlinear dynamic programming equations. We extend the algorithm to solve nonlinear stochastic dynamic programming equations. The corresponding algorithm, called SDDP-REG, can be seen as an extension of a regularization of the Stochastic Dual Dynamic Programming...

In this paper, the author presents a model to measure the superiority and inferiority of fuzzy numbers/fuzzy stochastic variables. Then, the new measures are used to convert the fuzzy (stochastic) linear program into the corresponding deterministic linear program. Numerical examples are provided to illustrate the effectiveness of the proposed method. 2006 Elsevier Inc. All rights reserved.

We analyze the problem of pricing and hedging contingent claims in the multi-period, discrete time, discrete state case using the concept of a sufficiently attractive expected gain opportunity to a claim’s writer and buyer. Pricing results somewhat different from, but reminiscent of, the arbitrage pricing theorems of mathematical finance are obtained. We show that our analysis provides tighter ...

DEA (Data Envelopment Analysis) is a technique for evaluating the relative effectiveness of decision-making units (DMU) with multiple inputs and outputs data based on non-parametric modeling using mathematical programming (including linear programming, multi-parameter programming, stochastic programming, etc.). The classical DEA methods are developed to handle the information in the form of cri...

In this paper we discuss statistical properties and rates of convergence of the Stochastic Dual Dynamic Programming (SDDP) method applied to multistage linear stochastic programming problems. We assume that the underline data process is stagewise independent and consider the framework where at first a random sample from the original (true) distribution is generated and consequently the SDDP alg...