A fuzzy bi-objective mathematical model for optimum portfolio selection by considering inflation rate effects

Ebrahimnejad, Sadoullah; Hosseinpour, Mohammad Hossein; Nasrabadi, Ali Mohammadi

doi:10.1007/s00170-013-5052-4

Cited by 4 publications

(5 citation statements)

References 71 publications

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“…In general, there are three distinct FCCP models (i.e. the expected value, the CCP and the dependent CCP) (Liu, 2009), which were used in parallel in many published works in different contexts (Ebrahimnejad et al , 2013; Ke et al , 2010; Lau et al , 2010). Liu and Iwamura (1998) suggested a spectrum of FCCP models (i.e.…”

Section: Problem Formulationmentioning

confidence: 99%

A model for project team formation in complex engineering projects under uncertainty

Hosseini

Akhavan

2017

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Purpose This paper aims to develop a model for selecting project team members. In this model, while knowledge sharing among individuals is maximized, the project costs and the workload balance among employees are also optimized. Design/methodology/approach The problem of project team formation is formulated as a fuzzy multi-objective 0-1 integer programming model. Afterward, to deal with uncertainty in the decision-making on the candidates’ abilities and the project requirements, the fuzzy multi-objective chance-constrained programming approach is adopted. Finally, by combining the non-dominated sorting genetic algorithm II and the fuzzy simulation algorithms, a method is proposed to solve the problem. Findings The computational results of the proposed model in a case study of project team formation in a large Iranian company from the shipbuilding industry evidently demonstrated its effectiveness in providing Pareto-optimal solutions for the team composition. Originality/value Seemingly for the first time, this paper develops a model to optimize knowledge sharing and improve the project efficiency through the selection of appropriate project team members.

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Section: Problem Formulationmentioning

confidence: 99%

A model for project team formation in complex engineering projects under uncertainty

Hosseini

Akhavan

2017

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“…In Section 2, we employ the fuzzy group MCDM method to propose a systematic approach for the critical path selection under fuzzy environment. To illustrate the working (5,7,9) High (H)/Good (G) (7, 9, 10) Very high (VH)/Very good (VG) (9,9,10) of the methodology, a hypothetical critical path problem under fuzzy environment is solved in this section and the impact of criteria weights on the critical path selection is shown through sensitivity analysis. In Section 3, a case study from manufacturing engineering industry is presented to better justify the applicability and potentials of the proposed methodology.…”

Section: Organization Of the Papermentioning

confidence: 99%

“…In the first six experiments, the weights of two criteria are swapped while the other two kept same, e.g., in experiment 1, the weights of criteria C 1 and C 3 are interchanged. In experiments 7-11, the weight of one criterion is set at higher value and the remaining weights are set at comparatively lower level, e.g., in experiment 7, the weight of criterion C 1 is set a higher value (7,9,10), and the remaining weights are set to the lower value (1,4.333,9). In experiments 12-17, the weights of any two criteria are set a higher value and the remaining two are set comparatively lower value, e.g., in experiment 12, the weights of criteria C 1 , C 2 are set the lower value (1,4.333,9) and the remaining two criteria are set the higher value (7,9,10).…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…This small unit will contain a number of electronic components, assembled on a printed circuit board, which in turn will be supported on an aluminium chassis. This assembly is to be encapsulated in epoxy resin to protect 9) the components from the harsh environmental conditions of the engine bay. A cable connector and a pressure switch will also be mounted on the chassis to protrude outside the encapsulated block.…”

Section: Case Studymentioning

confidence: 99%

“…Therefore, we obtain performance ratings of the activities on the main criteria and the weight of each criterion from the decision makers using fuzzy numbers and/or linguistic variables (mapped to the triangular fuzzy numbers). The triangular fuzzy numbers are used because they offer a good compromise between accuracy and computational time [2,9,13,22] and require elementary fuzzy algebra. Towards the development of the proposed methodology, firstly, the fuzzy information provided by all the decision makers are aggregated using an appropriate aggregation process to determine the aggregated fuzzy evaluation value of each project path on each criterion.…”

Section: Introductionmentioning

confidence: 99%

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A new fuzzy group multi-criteria decision making method with an application to the critical path selection

Mehlawat

Gupta

2015

Int J Adv Manuf Technol

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In this paper, we propose a new fuzzy group multi-criteria decision making method and apply it to determine the critical path in a project network. The criteria used here are time (expected duration), cost, risk, and quality of the project activities that are considered critical in project management. As each criterion has its independent level of importance in the critical path selection, the weights of the project criteria are also considered in the analysis. Considering that the information in terms of various project activities and criteria weights are often incomplete and/or uncertain in real-world situations, the essential information in terms of the criteria and project activities are obtained using triangular fuzzy numbers and/or linguistic variables that are mapped to triangular fuzzy numbers, wherever appropriate. The proposed method involves fuzzy evaluation based on the fuzzy information of the possible project paths on each criterion leading to the strength and weakness index scores of the project paths. We define a measure of criticality termed as the total performance score of each project path obtained using its strength and weakness index scores. The path that has the highest measure of criticality is selected as the critical path. A numerical illustration is provided to demonstrate working of the proposed methodology. Further, a case study from manufacturing engineering industry is also presented to better justify the applicability and potentials of the proposed methodology. A comparison with closely related fuzzy multi-criteria decision methods for the critical path selection is done to analyze the performance of the proposed methodology.

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A New Optimization Model for Project Portfolio Selection Under Interval-Valued Fuzzy Environment

2015

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A fuzzy bi-objective mathematical model for optimum portfolio selection by considering inflation rate effects

Cited by 4 publications

References 71 publications

A model for project team formation in complex engineering projects under uncertainty

A model for project team formation in complex engineering projects under uncertainty

A new fuzzy group multi-criteria decision making method with an application to the critical path selection

A New Optimization Model for Project Portfolio Selection Under Interval-Valued Fuzzy Environment

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