A linguistic-based portfolio selection model using weighted max–min operator and hybrid genetic algorithm

“…67 These approaches have been widely used to solve fuzzy multi-objective portfolio optimization models. [7][8][9][68][69][70][71] To evaluate performance of the aforementioned fuzzy programming approaches, we solve the bi-objective model (P2) using the input data provided in Table 1 using each fuzzy programming approach mentioned above. We solve many problem instances by varying the importance weights of objective functions ð 1 ; 2 Þ for a given minimum con¯dence level ð ¼ 0:7Þ and the compensation coe±cient ðwÞ is set to 0.2.…”

Section: Performance Analysismentioning

confidence: 99%

Credibility-Based Fuzzy Mathematical Programming Model for Portfolio Selection Under Uncertainty

Mehlawat

Gupta

2014

Int. J. Info. Tech. Dec. Mak.

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In this paper, we develop a hybrid bi-objective credibility-based fuzzy mathematical programming model for portfolio selection under fuzzy environment. To deal with imprecise parameters, we use a hybrid credibility-based approach that combines the expected value and chance constrained programming techniques. The model simultaneously maximizes the portfolio return and minimizes the portfolio risk. We also consider an additional important criterion, namely, portfolio liquidity as a constraint in the model to make it better suited for practical applications. The proposed fuzzy optimization model is solved using a two-phase approach. An empirical study is included to demonstrate applicability of the proposed model and the solution approach in real-world applications of portfolio selection.

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“…If this proportionality is important to the DM, the weighted max-min method can be used. This model is formulated as follows (Amid et al 2011;Dastkhan et al 2011):…”

Section: Constraints and Solution Approachmentioning

confidence: 99%

A Fuzzy Multi-objective Model with MFCA Approach for Selecting Products Variety in a Textile Supply Chain

Amin

Zolfaghari

Siyahpoush

2015

Toward Sustainable Operations of Supply Chain and Logistics Systems

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Offering product variety is one of the strategies for today's competitive advantage. In spite of the fact that greater product variety allows manufacturers to meet a larger share of customer's satisfaction, it increases the complexity of the process and reduces efficiency of the operational performance in a supply chain. Increasing product variety also creates more wastes and more losses, and reduces efficiency of resources. In this chapter, a fuzzy multi-objective model is developed in an imprecise environment by using Material Flow Cost Accounting (MFCA) approach which concentrates on reducing resource consumption by reducing waste and losses as well as active response to environmental requirements. The cost objective function consists of waste management cost, system cost, material cost, and energy cost. The model considers different weights for each of the cost items. Due to uncertain seasonal market demand for textile productions, an imprecise production preparation time and vagueness of input data, a fuzzy weighted maxmin multi-objective model is developed for this problem. In this model, the achieved level of objective functions will match the relative importance of objective functions. The model considers three objective functions: quality, product variety, and cost of negative products which focus on the appropriate variety of the products and help decision makers increase green productivity in a textile supply chain. A numerical example is developed to show the application of the model.

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A linguistic-based portfolio selection model using weighted max–min operator and hybrid genetic algorithm

Cited by 22 publications

References 24 publications

An integrated multi-objective Markowitz–DEA cross-efficiency model with fuzzy returns for portfolio selection problem

An integrated multi-objective Markowitz–DEA cross-efficiency model with fuzzy returns for portfolio selection problem

Credibility-Based Fuzzy Mathematical Programming Model for Portfolio Selection Under Uncertainty

A Fuzzy Multi-objective Model with MFCA Approach for Selecting Products Variety in a Textile Supply Chain

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