A hybrid mathematical programming model for optimal project portfolio selection using fuzzy inference system and analytic hierarchy process

2019 ◽  
Vol 77 ◽  
pp. 101703 ◽  
Author(s):  
Madjid Tavana ◽  
Ghasem Khosrojerdi ◽  
Hassan Mina ◽  
Amirah Rahman
Keyword(s):  
Mathematical Programming ◽  
Portfolio Selection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Programming Model ◽  
Project Portfolio ◽  
Mathematical Programming Model ◽  
Analytic Hierarchy ◽  
Inference System ◽  
Hierarchy Process

Optimal project portfolio selection with reinvestment strategy considering sustainability in an uncertain environment: a multi-objective optimization approach

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammadali Zarjou ◽  
Mohammad Khalilzadeh
Keyword(s):  
Mathematical Programming ◽  
Portfolio Selection ◽  
Programming Model ◽  
Sustainability Indicators ◽  
Selection Problem ◽  
Project Portfolio ◽  
Mathematical Programming Model ◽  
Content Type ◽  
Multi Objective ◽  
Project Portfolio Selection

PurposeThis study aims to develop a model for project portfolio selection considering organizational goals such as budgets, sustainability cash flow and reinvestment strategy under an uncertain environment.Design/methodology/approachA multi-objective mathematical programming model is proposed for project selection, which takes the social, environmental and financial aspects into account as the objectives of the project portfolio selection problem. The project evaluation and selection process in one of the large capitals in the Middle East with numerous urban construction projects was considered as a real case study, in which the subjects of environmental and social sustainability are of great importance. Then, the most significant criteria for project evaluation and selection based on sustainability were identified and ranked using the fuzzy best-worst method (BWM).FindingsThe criterion of “defining clear and real objectives” was ranked first, “project investment return period” was ranked second, “minimum changes in the predicted range” was ranked third, and the other ten sustainability indicators were ranked as well. Next, the presented mathematical programming model was solved using the augmented e-constraint method. The sensitivity analysis indicated that increasing the amount of investments in projects would increase their net present value. Also, increased investment had no effect on sustainability, while decreased investment caused sustainability to not being optimal.Originality/valueThis study focuses on the impact of the amount of investments on projects, and the associated costs of sustainable projects. Further to the authors' knowledge, there has been no relevant study taking uncertainty into account. Also, very few studies proposed a mathematical programming model for the project portfolio selection problem. Moreover, this research uses the brainstorming and Delphi method to identify the sustainability indicators influencing the organization and screens the evaluation indicators. Furthermore, the weights of the evaluation indicators are determined using the fuzzy BWM based on the consistency of opinions.

scholarly journals Risk Assessment and Mitigation Model for Overseas Steel-Plant Project Investment with Analytic Hierarchy Process—Fuzzy Inference System

2018 ◽  
Vol 10 (12) ◽  
pp. 4780 ◽  
Author(s):  
Min-Sung Kim ◽  
Eul-Bum Lee ◽  
In-Hye Jung ◽  
Douglas Alleman
Keyword(s):  
Risk Assessment ◽  
Analytic Hierarchy Process ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Raw Materials ◽  
Decision Makers ◽  
Analytic Hierarchy ◽  
Project Risk ◽  
Inference System ◽  
Hierarchy Process

This paper presents an analytic hierarchy process (AHP)-fuzzy inference system (FIS) model to aid decision-makers in the risk assessment and mitigation of overseas steel-plant projects. Through a thorough literature review, the authors identified 57 risks associated with international steel construction, operation, and transference of new technologies. Pairwise comparisons of all 57 risks by 14 subject-matter experts resulted in a relative weighting. Furthermore, to mitigate human subjectivity, vagueness, and uncertainty, a fuzzy analysis based on the findings of two case studies was performed. From these combined analyses, weighted individual risk soring resulted in the following top five most impactful international steel project risks: procurement of raw materials; design errors and omissions; conditions of raw materials; technology spill prevention plan; investment cost and poor plant availability and performance. Risk mitigation measures are also presented, and risk scores are re-assessed through the AHP-FIS analysis model depicting an overall project risk score reduction. The model presented is a useful tool for industry performing steel project risk assessments. It also provides decision-makers with a better understanding of the criticality of risks that are likely to occur on international steel projects.

scholarly journals A Model for IT/IS Project Portfolio Selection in the Presence of Uncertainty: Combination of Fuzzy Multi-Criteria Decision Making and Fuzzy Mathematical Programming

2012 ◽  
Vol 488-489 ◽  
pp. 411-416 ◽  
Author(s):  
Reihaneh Amel Sadeghi ◽  
Mehdi Seifbarghy
Keyword(s):  
Decision Making ◽  
Mathematical Programming ◽  
Portfolio Selection ◽  
Programming Model ◽  
Multi Criteria Decision Making ◽  
Project Portfolio ◽  
Fuzzy Mathematical Programming ◽  
Mathematical Programming Model ◽  
Multi Objective ◽  
Second Stage

IT/IS represents a substantial financial investment for many organizations. Making IT project portfolio decision is difficult, because long lead times of IT project and market and technology dynamics lead to unavailable and unreliable collected data for portfolio management. This uncertainty has been modeled using fuzzy concepts. We need a collective model that will help decision-makers evaluate potential new investment projects in an easy, cost-effective, and collective manner. Hence, we propose a new approach based on the fuzzy multi-criteria decision model (FMCDM) and a fuzzy binary multi-objective linear programming model, featuring a 2-stage evaluation and selection process with 19 criteria for IT/IS investment. At the first stage, evaluation, all stakeholders in a corporation can decide the relative weights they give to the criteria when they evaluate a new IT/IS project by using linguistic values. Experts can also use linguistic values to evaluate all candidates easily. Only an Excel worksheet is needed to obtain an evaluation result. The results of FMCDM of the aforementioned are treated as input of a fuzzy binary mathematical programming model as coefficients of objective functions, which is the second stage of the proposed model. In the second stage, selection, we have developed a fuzzy binary mathematical programming model in order to find an optimum combination of investment portfolio considering a multi-objective measurement function in three ways: to maximize the benefit, to maximize the confidence level and to minimize the cost of projects in a complete ambiguous condition, when their initial investment costs, profits, confidence levels, resource requirements and total available budgets are assumed to be uncertain. We solve it in Lingo 10.0 through a Branch and Bound algorithm. In this paper, for the first time we have developed a model for IT/IS project portfolio selection in presence of uncertainty that is combination of fuzzy multi-criteria decision making and fuzzy mathematical programming with 19 criteria that is compatible with the nature of IT projects. We conduct a case study to show how this model can be used and discuss the results.

scholarly journals Estimation of fines amount in syariah criminal offences using adaptive neuro-fuzzy inference system (ANFIS) enhanced with analytic hierarchy process (AHP)

2019 ◽  
Vol 9 (6) ◽  
pp. 5537
Author(s):  
Ahmad Fitri Mazlam ◽  
Wan Nural Jawahir Hj Wan Yussof ◽  
Rabiei Mamat
Keyword(s):  
Analytic Hierarchy Process ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Individual Decision ◽  
Criminal Cases ◽  
Analytic Hierarchy ◽  
Inference System ◽  
Neuro Fuzzy ◽  
The Individual ◽  
Hierarchy Process

<span lang="EN-US">All syariah criminal cases, especially in khalwat offence have their case-fact, and the judges typically look forward to all the facts which were tabulated by the prosecutors. A variety of criteria is considered by the judge to determine the fines amount that should be imposed on an accused who pleads guilty. In Terengganu, there were ten (10) judges, and the judgments were made by the individual decision upon the trial to decide the case. Each judge has a stake, principles and distinctive criteria in determining fines amount on an accused who pleads guilty and convicted. This research paper presents an Adaptive Neuro-fuzzy Inference System (ANFIS) technique combining with Analytic Hierarchy Process (AHP) for estimating fines amount in Syariah (khalwat) criminal. Datasets were collected under the supervision of registrar and syarie judge in the Department of Syariah Judiciary State Of Terengganu, Malaysia. The results showed that ANFIS+AHP could estimate fines efficiently than the traditional method with a very minimal error.</span>

CREDIBILITY-BASED FUZZY MATHEMATICAL PROGRAMMING MODEL FOR PORTFOLIO SELECTION UNDER UNCERTAINTY

2014 ◽  
Vol 13 (01) ◽  
pp. 101-135 ◽  
Author(s):  
MUKESH KUMAR MEHLAWAT ◽  
PANKAJ GUPTA
Keyword(s):  
Mathematical Programming ◽  
Portfolio Selection ◽  
Programming Model ◽  
Fuzzy Optimization ◽  
Chance Constrained Programming ◽  
Fuzzy Mathematical Programming ◽  
Mathematical Programming Model ◽  
Two Phase ◽  
Solution Approach ◽  
Practical Applications

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.

scholarly journals Ranking patients on the kidney transplant waiting list based on fuzzy inference system

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Nasrin Taherkhani ◽  
Mohammad Mehdi Sepehri ◽  
Roghaye Khasha ◽  
Shadi Shafaghi
Keyword(s):  
Kidney Transplantation ◽  
Fuzzy Inference System ◽  
Scoring System ◽  
Fuzzy Inference ◽  
Waiting List ◽  
Analytic Hierarchy ◽  
Inference System ◽  
Kidney Allocation ◽  
Single Output ◽  
Stage Renal Disease

Abstract Background Kidney transplantation is the best treatment for people with End-Stage Renal Disease (ESRD). Kidney allocation is the most important challenge in kidney transplantation process. In this study, a Fuzzy Inference System (FIS) was developed to rank the patients based on kidney allocation factors. The main objective was to develop an expert system, which would mimic the expert intuitive thinking and decision-making process in the face of the complexity of kidney allocation. Methods In the first stage, kidney allocation factors were identified. Next, Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) has been used to weigh them. The purpose of this stage is to develop a point scoring system for kidney allocation. Fuzzy if-then rules were extracted from the United Network for Organ Sharing (UNOS) dataset by constructing the decision tree, in the second stage. Then, a Multi-Input Single-Output (MISO) Mamdani fuzzy inference system was developed for ranking the patients on the waiting list. Results To evaluate the performance of the developed Fuzzy Inference System for Kidney Allocation (FISKA), it was compared with a point scoring system and a filtering system as two common approaches for kidney allocation. The results indicated that FISKA is more acceptable to the experts than the mentioned common methods. Conclusion Given the scarcity of donated kidneys and the importance of optimal use of existing kidneys, FISKA can be very useful for improving kidney allocation systems. Countries that decide to change or improve the kidney allocation system can simply use the proposed model. Furthermore, this model is applicable to other organs, including lung, liver, and heart.

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