A visual interactive approach for scenario-based stochastic multi-objective problems and an application

Balibek, Emre; Köksalan, Murat

doi:10.1057/jors.2012.25

Cited by 7 publications

(5 citation statements)

References 8 publications

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“…Interactive multiple objective methods can also reflect the stochastic elements. Recent development is reflected in modern interactive stochastic approaches [20][21][22]. A more complex overview of the interactive stochastic multi-objective programming methods can be seen in [23].…”

Section: Review Of Interactive Multiple Objective Programming Methodsmentioning

confidence: 99%

“…where α is an intersection of all the fuzzy sets in Model (20) representing the (weighted) membership grade of a solution. Therefore, α is maximized.…”

Section: Step 3: Fuzzy Goal Constructionmentioning

confidence: 99%

“…Let us get back to the weights in Model (20). As we can see, the position of the weights is different compared to Model (11) to reflect the importance of the objectives correctly.…”

Section: Step 3: Fuzzy Goal Constructionmentioning

confidence: 99%

“…In the original KSU-STEM algorithm, a unit weight of the unsatisfactory objective works well in Model (11). The problem occurs in the proposed modified Model (20) because zero is on the left side of the condition, and it is related to the fuzzy goal. The fuzzy goal is virtually eliminated, which potentially distort the results.…”

Section: Special Case Of Single Unsatisfactory Objectivementioning

confidence: 99%

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Algorithmic Improvements of the KSU-STEM Method Verified on a Fund Portfolio Selection

Borovička

2020

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The topic of this article is inspired by the problem faced by many people around the world: investment portfolio selection. Apart from the standardly used methods and approaches, non-traditional multiple objective programming methods can also be significant, providing even more efficient support for making a satisfactory investment decision. A more suitable method for this purpose seems to be a concept working with an interactive procedure through the portfolio that may gradually be adapted to the investor’s preferences. Such a method is clearly the Step Method (STEM) or the more suitable improved version KSU-STEM. This method is still burdened by partial algorithmic weaknesses or methodical aspects to think about, but not as much as the other methods. The potentially stronger application power of the KSU-STEM concept motivates its revision. Firstly, an unnecessarily negative principle to determine the basal value of the objectives is revised. Further, the fuzzy goals are specified, which leads to a reformulation of the revealed defuzzified multi-objective model. Finally, the imperfect re-setting of the weights (importance) of unsatisfactory objectives is revealed. Thus, the alternative approaches are proposed. The interventions to the algorithm are empirically verified through a real-life selection of a portfolio of the open unit trusts offered by CONSEQ Investment Management traded on the Czech capital market. This application confirms a significant supporting power of the revised multiple objective programming approach KSU-STEM in a portfolio-making process.

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Section: Review Of Interactive Multiple Objective Programming Methodsmentioning

confidence: 99%

“…where α is an intersection of all the fuzzy sets in Model (20) representing the (weighted) membership grade of a solution. Therefore, α is maximized.…”

Section: Step 3: Fuzzy Goal Constructionmentioning

confidence: 99%

“…Let us get back to the weights in Model (20). As we can see, the position of the weights is different compared to Model (11) to reflect the importance of the objectives correctly.…”

Section: Step 3: Fuzzy Goal Constructionmentioning

confidence: 99%

Section: Special Case Of Single Unsatisfactory Objectivementioning

confidence: 99%

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Algorithmic Improvements of the KSU-STEM Method Verified on a Fund Portfolio Selection

Borovička

2020

Information

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“…A crucial part of any stochastic program study is generating scenarios that fairly represent uncertainties to obtain accurate results. The importance of this subject has been noted by Date et al (2008), Falasca &Zobel (2011), andBalibek &Koksalan (2012). The issue of representing uncertainties through quantitative models is complex and well known (Hoyland & Wallace, 2001).…”

Section: Scenario Generationmentioning

confidence: 99%

A three-stage stochastic optimization model for the Brazilian biodiesel supply chain

Senna

Pinha

Ahluwalia

et al. 2016

Prod.

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The Brazilian program for biodiesel use highlights the production of biodiesel from castor seeds. Biodiesel is a non-polluting energy source that has the potential to promote prosperity by creating jobs in poor regions of Brazil. However, the infrastructure, logistics, and proper facilities are lacking. A variety of approaches to optimizing the biodiesel supply chain have been proposed. The goal is to minimize the grain storage and transportation costs. This paper presents a comparison between a two-stage model and a multistage (three-stage) stochastic model to optimize the biodiesel supply chain. The comparison between these formulations shows that the flexibility gain provided by the multistage model results in a lower total logistic cost. The optimum for the three-stage model was 7,700,019 (BRL), compared to 8,628,002 (BRL) for the two-stage model, representing a savings of 927,983 (BRL). We highlight that this model offers a real solution for castor supply chain design (considering uncertainty) in the Brazilian semiarid region, which is a poorer region of the country, thus making cost reduction mandatory. KeywordsBiodiesel. Mixed Integer Linear programming. Stochastic optimization. Multistage program.A three-stage stochastic optimization … biodiesel supply chain. Production, 26(3), 501-515, jul./set. 2016 502 Senna, P. et al.

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An interactive approach to stochastic programming-based portfolio optimization

Köksalan

Şakar

2014

Ann Oper Res

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A visual interactive approach for scenario-based stochastic multi-objective problems and an application

Cited by 7 publications

References 8 publications

Algorithmic Improvements of the KSU-STEM Method Verified on a Fund Portfolio Selection

Algorithmic Improvements of the KSU-STEM Method Verified on a Fund Portfolio Selection

A three-stage stochastic optimization model for the Brazilian biodiesel supply chain

An interactive approach to stochastic programming-based portfolio optimization

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