Strange: An interactive method for multi-objective linear programming under uncertainty

Teghem, Jacques; Dufrane, D.; Thauvoye, M.; Kunsch, Pierre Louis

doi:10.1016/0377-2217(86)90160-8

Cited by 128 publications

(36 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…( Kunsch and Teghem, 1987) take a MUA approach to the decision to reprocess SNF utilizing STRAtegy for the Nuclear Generation of Electricity (STRANGE), a stochastic LP (Teghem et al, 1986). The examined criteria include production costs, energy independence, commercial balance, and national technological level.…”

Section: Stochastic Optimizationmentioning

confidence: 99%

Nuclear Fuel Cycle Transition Analysis under Uncertainty

Phathanapirom

Schneider

2016

Nuclear Science and Engineering

View full text Add to dashboard Cite

Section: Stochastic Optimizationmentioning

confidence: 99%

Nuclear Fuel Cycle Transition Analysis under Uncertainty

Phathanapirom

Schneider

2016

Nuclear Science and Engineering

View full text Add to dashboard Cite

“…For other problems that may be modelled in the same way as (1), we may mention reconfigurable manufacturing systems [40], distributed energy resources planning [54], water use planning [55], manufacturing planning [56], power systems planning [57][58][59] energy and reserves markets [60] and multi-product batch plant design [61].…”

Section:  mentioning

confidence: 99%

“…These computations are easier to handle than those required by the stochastic approach. (see e.g., [49,58,95]). …”

Section: Comparison Of Different Approachmentioning

confidence: 99%

Multiobjective Stochastic Linear Programming: An Overview

Adeyefa¹,

Luhandjula²

2011

AJOR

View full text Add to dashboard Cite

Many Optimization problems in engineering and economics involve the challenging task of pondering both conflicting goals and random data. In this paper, we give an up-to-date overview of how important ideas from optimization, probability theory and multicriteria decision analysis are interwoven to address situations where the presence of several objective functions and the stochastic nature of data are under one roof in a linear optimization context. In this way users of these models are not bound to caricature their problems by arbitrarily squeezing different objective functions into one and by blindly accepting fixed values in lieu of imprecise ones.

show abstract

“…He transformed the stochastic problem into an equivalent deterministic quadratic programming problem, where the objective functions consisted of maximizing the probability of a vector of goals lying in the confidence region of a predefined size. Teghem et al (1986) and Leclercq (1982) have presented interactive methods in stochastic programming. Two major approach to stochastic programming as recognized by Goicoechea et al (1982) and Kambo (1984) are:…”

Section: Introductionmentioning

confidence: 99%

On two-stage fuzzy random programming for water resources management

Khalifa¹

2015

Afr. J. Math. Comput. Sci. Res.

View full text Add to dashboard Cite

In this paper, a two-stage fuzzy random programming for a management problem in terms of water resources allocation having fuzzy random variable coefficients and decision vector of random variables is studied. The first results show the fact that a fuzzy pseudorandom optimal solution of a two-stage fuzzy random programming may be resolved into a two of pseudorandom optimal solutions of relative two-stage random programmings. The subsequent results present a method that a fuzzy pseudorandom optimal solution of two-stage fuzzy random programming is structured by a two of pseudorandom optimal solutions of a relative two two-stage random programmings. A numerical example was given to clarify the obtained results.

show abstract

Strange: An interactive method for multi-objective linear programming under uncertainty

Cited by 128 publications

References 4 publications

Nuclear Fuel Cycle Transition Analysis under Uncertainty

Nuclear Fuel Cycle Transition Analysis under Uncertainty

Multiobjective Stochastic Linear Programming: An Overview

On two-stage fuzzy random programming for water resources management

Contact Info

Product

Resources

About