Solution for the Investment Decision Making Problem through Interval Probabilities

Energy-Investment Decision-Making for Industry: Quantitative and Qualitative Risks Integrated Analysis

Sustainability ◽

10.3390/su13126977 ◽

2021 ◽

Vol 13 (12) ◽

pp. 6977

Author(s):

Eva M. Urbano ◽

Victor Martinez-Viol ◽

Konstantinos Kampouropoulos ◽

Luis Romeral

Keyword(s):

Decision Making ◽

Cost Benefit Analysis ◽

Investment Decision ◽

Energy Transition ◽

Integrated Analysis ◽

Energy Investment ◽

Inference System ◽

Investment Decision Making ◽

Decision Making Problem ◽

Energy Equipment

Industrial SMEs may take the decision to invest in energy efficient equipment to reduce energy costs by replacing or upgrading their obsolete equipment or due to external socio-political and legislative pressures. When upgrading their energy equipment, it may be beneficial to consider the adoption of new energy strategies rising from the ongoing energy transition to support green transformation and decarbonisation. To face this energy-investment decision-making problem, a set of different economic and environmental criteria have to be evaluated together with their associated risks. Although energy-investment problems have been treated in the literature, the incorporation of both quantitative and qualitative risks for decision-making in SMEs has not been studied yet. In this paper, this research gap is addressed, creating a framework that considers non-risk criteria and quantitative and qualitative risks into energy-investment decision-making problems. Both types of risks are evaluated according to their probability and impact on the company’s objectives and, additionally for qualitative risks, a fuzzy inference system is employed to account for judgmental subjectivity. All the criteria are incorporated into a single cost–benefit analysis function, which is optimised along the energy assets’ lifetime to reach the best long-term energy investment decisions. The proposed methodology is applied to a specific industrial SME as a case study, showing the benefits of considering these risks in the decision-making problem. Nonetheless, the methodology is expandable with minor changes to other entities facing the challenge to invest in energy equipment or, as well, other tangible assets.

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Multi-criteria decision making based on induced generalized interval neutrosophic Choquet integral

PLoS ONE ◽

10.1371/journal.pone.0242449 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0242449

Author(s):

Yangyang Jiao ◽

Lu Wang ◽

Jianxia Liu ◽

Gang Ma

Keyword(s):

Decision Making ◽

Choquet Integral ◽

Investment Decision ◽

Aggregation Operators ◽

Optimal Decision ◽

Multi Criteria Decision Making ◽

Investment Decision Making ◽

Decision Making Problem ◽

Geometric Operator ◽

Integral Average

In this paper, two new aggregation operators based on Choquet integral, namely the induced generalized interval neutrosophic Choquet integral average operator(IGINCIA) and the induced generalized interval neutrosophic Choquet integral geometric operator(IG-INCIG), are proposed for multi-criteria decision making problems (MCDM). Firstly, the criteria are dependent to each other and the evaluation information of the criteria are expressed by interval neutrosophic numbers. Moreover, two indices which are inspired by the geometrical structure are established to compare the interval neutrosophic numbers. Then, a MCDM method is proposed based on the proposed aggregation operators and ranking indices to cope with MCDM with interactive criteria. Lastly, an investment decision making problem is provided to illustrate the practicality and effectiveness of the proposed approach. The validity and advantages of the proposed method are analyzed by comparing with some existing approaches. By a numerical example in company investment to expand business though five alternatives with considering four criteria, the optimal decision is made.

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Composite Fuzzy Measure and its Application to Decision-Making

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2004.p0252 ◽

2004 ◽

Vol 8 (3) ◽

pp. 252-259

Author(s):

Toshihiro Kaino ◽

◽

Kaoru Hirota ◽

Keyword(s):

Decision Making ◽

Capital Investment ◽

Choquet Integral ◽

Investment Decision ◽

Measurable Space ◽

Fuzzy Measure ◽

Fuzzy Measures ◽

Investment Decision Making ◽

Decision Making Problem ◽

Fuzzy Weights

In applications using fuzzy measures (on real numbers), it becomes a problem how to evaluate in-between intervals each characterized by a fuzzy measure, especially when the Choquet integral is differentiated in real world problems. A composite fuzzy measure built from fuzzy measures defined on fuzzy measurable spaces has been proposed by Kaino and Hirota using composite fuzzy weights, where the measurable space of this composite fuzzy measure is the direct sum of measurable spaces. An associative, composite fuzzy measure built from a finite number of fuzzy measures is proposed and, in a constructive application, it is applied to the automobile plant capital investment decision-making problem. It is assumed that an automobile company plans to sell a new car. The current plant line has a capacity of 3,200 new cars in addition to current car lines. Using this composite fuzzy measure, differentiation of the Choquet integral becomes a quantitative index for decision-making, which is confirmed by this decision-making experiment.

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A Grey Interval Relational Degree-Based Dynamic Multiattribute Decision Making Method and Its Application in Investment Decision Making

Mathematical Problems in Engineering ◽

10.1155/2014/607016 ◽

2014 ◽

Vol 2014 ◽

pp. 1-6 ◽

Cited By ~ 6

Author(s):

Yuhong Wang ◽

Xiaojuan Shi ◽

Jihong Sun ◽

Wuyong Qian

Keyword(s):

Decision Making ◽

Investment Decision ◽

Three Dimensional ◽

Observation Data ◽

Interval Numbers ◽

Investment Decision Making ◽

Decision Making Problem ◽

Grey Relational Degree ◽

Relational Degree ◽

Grey Relational

The purpose of this paper is to propose a three-dimensional grey interval relational degree model for dynamic Multiattribute decision making. In the model, the observed values are interval grey numbers. Elements are selected in the system as the points in anm-dimensional linear space. Then observation data of each element to different time and objects are as the coordinates of point. An optimization model is employed to obtain each scheme’s affiliate degree for the positive and negative ideal schemes. And a three-dimensional grey interval relational degree model based on time, index, and scheme is constructed in the paper. The result shows that the three-dimensional grey relational degree simplifies the traditional dynamic multiattribute decision making method and can better resolve the dynamic multiattribute decision making problem of interval numbers. The example illustrates that the method presented in the paper can be used to deal with problems of uncertainty such as dynamic multiattribute decision making.

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