stochastic programming
Stochastic Programming Model for Discrete Lotsizing and Scheduling Problem on Parallel Machines
... without stochastic factors, treating sto- chastic elements as deterministic variables or ...parameters. Stochastic programming incorporates such factors into the mathematical ...incorporating ...
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Software tools for Stochastic Programming: A Stochastic Programming Integrated Environment. (SPInE)
... OSL Stochastic Extension (King 1994) is an optimisation library which provides a set of routines for the manipulation and solution of multistage Stochastic Programming problems presented in SMPS ...
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Waste processing facility location problem by stochastic programming: Models and solutions
... Abstract. The paper deals with the so-called waste processing facil- ity location problem (FLP), which asks for establishing a set of oper- ational waste processing units, optimal against the total expected cost. We ...
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Combining stochastic programming and optimal control to solve multistage stochastic optimization problems
... multistage stochastic programming problem which allows us to obtain a time and nodal decomposition of the original ...original stochastic programming problem in arborescent ...the ...
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A simple randomised algorithm for convex optimisation: Application to two-stage stochastic programming
... two-stage stochastic programming ...two-stage stochastic programming problem and under a given uniform bound on the directional derivatives of the second stage random value function, the ...
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A Defined Benefit Pension Fund ALM Model through Multistage Stochastic Programming
... a stochastic programming problem in discrete time and with a discrete set of relevant future economic and demographic ...Dynamic stochastic programming is shown under such conditions to ...
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Gamma distribution approach in chance constrained stochastic programming model
... chance-constrained stochastic programming (CCSP) models is one of the major approaches for dealing with random parameters in the optimization ...optimal stochastic decision rules under ...
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Risk analysis of sourcing problem using stochastic programming
... Demand uncertainty is one of the main aspects of the present paper. Some papers consider only this parameter to be uncertain. In line with this assumption, and in a multi-period sourcing problem, Serel et al. [9] ...
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Sequential Bounding Methods for Stochastic Programming Models of Production Planning.
... estimate of the true optimal objective function value, since E[¯ z] ˆ ≤ z ∗ (Shapiro et al., 2009). Chiralaksanakul and Morton (2004) use the ˆ z i ∗ values to obtain a lower confidence bound on z ∗ . With finite second ...
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Stochastic Programming Models in Financial Optimization: A Survey 1
... for stochastic programming models in financial optimization are arbi- trage opportunities in the event tree that are due to approximation ...the stochastic programming ...
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A Literature Review of Stochastic Programming and Unit Commitment
... of stochastic programming’s applications in ...by stochastic optimization methods. Stochastic programming, robust optimization and sto- chastic dynamic programming are all outlined in ...
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Stochastic Programming with Cauchy Distribution
... Charnes and Cooper [6] first introduced the stochastic programming by taking different objective functions and constraints. Various models have been suggested by several researchers and most of the ...
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Multi time period stochastic programming for medium term production planning
... through the methods o f chance co nstraints or dynamic programming. See, fo r example Charnes, Cooper and Symonds [ J O ] , who aggregate to r e s t r ic th e ir a tte n tio n to a sin g le product and use chance ...
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Stochastic programming for City Logistics: new models and methods
... and Muller, 2008), which consider economic aspects (e.g. cost, performance and customer satisfaction) related to carriers and shippers as well as the environmental sustainability and the efficiency of transport that ...
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Stochastic Programming Models for Appointment Scheduling that Compensate for Variation in Server Behavior.
... Table 4.13 shows the mean optimal objective value when a learning effect is present using the 2-SLP learning model, learning heuristic, and ASP model for n = 10 patients and our previous[r] ...
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Determining the Direct Mailing Frequency with Dynamic Stochastic Programming
... We will model the mailing frequency problem for each individual as a Markov decision chain with the objective to maximize customer profitability over an infinite time horizon. We define finite planning periods within the ...
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An Interactive Fuzzy Satisficing Method for Multiobjective Stochastic Integer Programming with Simple Recourse
... chastic programming and fuzzy programming. Stochastic programming, as an optimization method on the basis of the probability theory, has been developing in various ways [1-5], including two ...
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Fuzzy Stochastic Linear Programming Problems With Uncertainty Probability Distribution
... fuzzy programming (Zimmermann, 1983) and Zhong et ...linear programming problems, when coefficients of the objective functions are given as a fuzzy numbers, and used ranking functions in ...fuzzy ...
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Monte Carlo sampling approach to stochastic programming
... Various stochastic programming problems can be formulated as problems of optimization of an expected value ...of stochastic programming ...
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A Stochastic Dynamic Programming Approach To Balancing Wind Intermittency With Hydropower
... nonlinear programming method (NLP) is used to solve for within-day hourly ...traditional stochastic dynamic programming, which would treat each hour as a stage and thus require many stages to go out ...
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