Stochastic Programming: Optimization When Uncertainty Matters
نویسنده
چکیده
Stochastic programming (SP) was first introduced by George Dantzig in the 1950s. Since that time, tremendous progress has been made toward an understanding of properties of SP models and the design of algorithmic approaches for solving them. As a result, SP is gaining recognition as a viable approach for large-scale models of decisions under uncertainty. In this paper, we present an introduction to stochastic programming models and methodology at a level that is intended to be accessible to the breadth of members within the INFORMS community.
منابع مشابه
A Combined Stochastic Programming and Robust Optimization Approach for Location-Routing Problem and Solving it via Variable Neighborhood Search algorithm
The location-routing problem is one of the combined problems in the area of supply chain management that simultaneously make decisions related to location of depots and routing of the vehicles. In this paper, the single-depot capacitated location-routing problem under uncertainty is presented. The problem aims to find the optimal location of a single depot and the routing of vehicles to serve th...
متن کاملOptimization Model of Hirmand River Basin Water Resources in the Agricultural Sector Using Stochastic Dynamic Programming under Uncertainty Conditions
In this study, water management allocated to the agricultural sector’ was analyzed using stochastic dynamic programming under uncertainty conditions. The technical coefficients used in the study referred to the agricultural years, 2013-2014. They were obtained through the use of simple random sampling of 250 farmers in the region for crops wheat, barley, melon, watermelon and ruby grapes under ...
متن کاملAn Optimization Model for Multi-objective Closed-loop Supply Chain Network under uncertainty: A Hybrid Fuzzy-stochastic Programming Method
In this research, we address the application of uncertaintyprogramming to design a multi-site, multi-product, multi-period,closed-loop supply chain (CLSC) network. In order to make theresults of this article more realistic, a CLSC for a case study inthe iron and steel industry has been explored. The presentedsupply chain covers three objective functions: maximization ofprofit, minimization of n...
متن کاملEffects of Probability Function on the Performance of Stochastic Programming
Stochastic programming is a valuable optimization tool where used when some or all of the design parameters of an optimization problem are defined by stochastic variables rather than by deterministic quantities. Depending on the nature of equations involved in the problem, a stochastic optimization problem is called a stochastic linear or nonlinear programming problem. In this paper,a stochasti...
متن کاملA novel bi-level stochastic programming model for supply chain network design with assembly line balancing under demand uncertainty
This paper investigates the integration of strategic and tactical decisions in the supply chain network design (SCND) considering assembly line balancing (ALB) under demand uncertainty. Due to the decentralized decisions, a novel bi-level stochastic programming (BLSP) model has been developed in which SCND problem has been considered in the upper-level model, while the lower-level model contain...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2005