Multi-objective mathematical model based on fuzzy hybrid multi-criteria decision-making and FMEA approach for the risks of oil and gas projects

2020 ◽  
Vol 18 (6) ◽  
pp. 1997-2016
Author(s):  
Mohammad Khalilzadeh ◽  
Rose Balafshan ◽  
Ashkan Hafezalkotob
Keyword(s):  
Decision Making ◽  
Oil And Gas ◽  
Programming Model ◽  
Multi Criteria Decision Making ◽  
Content Type ◽  
Multi Objective ◽  
Response Strategies ◽  
Fuzzy Environment ◽  
Comprehensive Framework ◽  
Grey Relational

Purpose The purpose of this study is to provide a comprehensive framework for analyzing risk factors in oil and gas projects. Design/methodology/approach This paper consists of several sections. In the first section, 19 common potential risks in the projects of Pars Oil and Gas Company were finalized in six groups using the Lawshe validation method. These factors were identified through previous literature review and interviews with experts. Then, using the “best-worst multi-criteria decision-making” method, the study measured the weights associated with the performance evaluation indicators of each risk. Consequently, failure mode and effects analysis (FMEA) and the grey relational analysis (GRA)-VIKOR mixed method were used to rank and determine the critical risks. Finally, to assign response strategies to each critical risk, a zero-one multi-objective mathematical programming model was proposed and developed Epsilon-constraint method was used to solve it. Findings Given the typical constraints of projects which are time, cost and quality, of the projects that companies are often faced with, this study presents the identified risks of oil and gas projects to the managers of the oil and gas company in accordance with the priority given in the present research and the response to each risk is also suggested to be used by managers based on their organizational circumstances. Originality/value This study aims at qualitative management of cost risks of oil and gas projects (case study of Pars Oil and Gas Company) by combining FMEA, best worst and GRA-VIKOR methods under fuzzy environment and Epsilon constraints. According to studies carried out in previous studies, the simultaneous management of quantitative and qualitative cost of risk of oil and gas projects in Iran has not been carried out and the combination of these methods has also been innovated.

scholarly journals An integrated grey-based multi-criteria decision-making approach for supplier evaluation and selection in the oil and gas industry

Kybernetes ◽  
2019 ◽  
Vol 49 (2) ◽  
pp. 406-441 ◽  
Author(s):  
Mohamad Amin Kaviani ◽  
Amir Karbassi Yazdi ◽  
Lanndon Ocampo ◽  
Simonov Kusi-Sarpong
Keyword(s):  
Decision Making ◽  
Supply Chain ◽  
Supplier Selection ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Supplier Evaluation ◽  
Multi Criteria Decision Making ◽  
Gas Industry ◽  
Content Type ◽  
Delphi Approach

PurposeThe oil and gas industry is a crucial economic sector for both developed and developing economies. Delays in extraction and refining of these resources would adversely affect industrial players, including that of the host countries. Supplier selection is one of the most important decisions taken by managers of this industry that affect their supply chain operations. However, determining suitable suppliers to work with has become a phenomenon faced by these managers and their organizations. Furthermore, identifying relevant, critical and important criteria needed to guide these managers and their organizations for supplier selection decisions has become even more complicated due to various criteria that need to be taken into consideration. With limited works in the current literature of supplier selection in the oil and gas industry having major methodological drawbacks, the purpose of this paper is to develop an integrated approach for supplier selection in the oil and gas industry.Design/methodology/approachTo address this problem, this paper proposes a new uncertain decision framework. A grey-Delphi approach is first applied to aid in the evaluation and refinement of these various available criteria to obtain the most important and relevant criteria for the oil and gas industry. The grey systems theoretic concept is adopted to address the subjectivity and uncertainty in human judgments. The grey-Shannon entropy approach is used to determine the criteria weights, and finally, the grey-EDAS (evaluation based on distance from average solution) method is utilized for determining the ranking of the suppliers.FindingsTo exemplify the applicability and robustness of the proposed approach, this study uses the oil and gas industry of Iran as a case in point. From the literature review, 21 criteria were established and using the grey-Delphi approach, 16 were finally considered. The four top-ranked criteria, using grey-Shannon entropy, include warranty level and experience time, relationship closeness, supplier’s technical level and risks which are considered as the most critical and influential criteria for supplier evaluation in the Iranian oil and gas industry. The ranking of the suppliers is obtained, and the best and worst suppliers are also identified. Sensitivity analysis indicates that the results using the proposed methodology are robust.Research limitations/implicationsThe proposed approach would assist supply chain practicing managers, including purchasing managers, procurement managers and supply chain managers in the oil and gas and other industries, to effectively select suitable suppliers for cooperation. It can also be used for other multi-criteria decision-making (MCDM) applications. Future works on applying other MCDM methods and comparing them with the results of this study can be addressed. Finally, broader and more empirical works are required in the oil and gas industry.Originality/valueThis study is among the first few studies of supplier selection in the oil and gas industry from an emerging economy perspective and sets the stage for future research. The proposed integrated grey-based MCDM approach provides robust results in supplier evaluation and can be used for future domain applications.

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.

HHI-based evaluation of the European banking sector using an integrated fuzzy approach

Kybernetes ◽  
2019 ◽  
Vol 48 (6) ◽  
pp. 1195-1215 ◽  
Author(s):  
Hasan Dincer
Keyword(s):  
Decision Making ◽  
Banking Sector ◽  
Market Competition ◽  
Second Phase ◽  
Multi Criteria Decision Making ◽  
Content Type ◽  
Fuzzy Vikor ◽  
Fuzzy Environment ◽  
European Banking ◽  
The Impact

Purpose This paper aims to evaluate the market concentration and competition in the European Banking Sector using an integrated multi-criteria decision-making approach under the fuzzy environment. Design/methodology/approach The hybrid model combining fuzzy decision-making trial and evaluation laboratory (DEMATEL), fuzzy analytic network process (ANP) and fuzzy VIKOR methods is applied to measure the market competition and concentration in the European Banking Sector. For this purpose, two academicians and one expert from banking sector with at least five-year experiences are selected to evaluate the dimensions, criteria and alternatives. The academicians are also appointed to define the decision-making problem and determine the dimensions and the criteria on the basis of related literature. The implementation of the model has been constructed in three main phases. The first phase consists of the fuzzy DEMATEL technique for understanding the impact-relation map among the dimensions. The second phase includes the fuzzy ANP method for measuring the relative importance of the criteria. The last phase comprises the fuzzy VIKOR approach to rank the alternatives with the values of the Herfindahl–Hirschman Index (HHI). Findings Turkey, France, England and Germany are placed in the competitive market structure of the European Banking Sector respectively. Additionally, the comparative results of the study confirm the market shares and the competitive policies of the European and Turkish Banking Sector. Originality/value The novelty of the paper is to construct a hybrid multi-criteria decision-making model with the proposed HHI scales under the fuzzy environment and defined competition dimensions and criteria based on the literature for the European Banking Industry.

A two-sided matching decision-making approach based on regret theory under intuitionistic fuzzy environment

2021 ◽  
pp. 1-18
Author(s):  
Xiang Jia ◽  
Xinfan Wang ◽  
Yuanfang Zhu ◽  
Lang Zhou ◽  
Huan Zhou
Keyword(s):  
Decision Making ◽  
Hamming Distance ◽  
Optimal Solution ◽  
Intuitionistic Fuzzy Sets ◽  
Regret Theory ◽  
Multi Objective ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Environment ◽  
Objective Model ◽  
Single Objective

This study proposes a two-sided matching decision-making (TSMDM) approach by combining the regret theory under the intuitionistic fuzzy environment. At first, according to the Hamming distance of intuitionistic fuzzy sets and regret theory, superior and inferior flows are defined to describe the comparative preference of subjects. Hereafter, the satisfaction degrees are obtained by integrating the superior and inferior flows of the subjects. The comprehensive satisfaction degrees are calculated by aggregating the satisfaction degrees, based on which, a multi-objective TSMDM model is built. Furthermore, the multi-objective TSMDM model is converted to a single-objective model, the optimal solution of the latter is derived. Finally, an illustrative example and several analyses are provided to verify the feasibility and the effectiveness of the proposed approach.

The framework of data-driven and multi-criteria decision-making for detecting unbalanced bidding

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Huimin Li ◽  
Limin Su ◽  
Jian Zuo ◽  
Xiaowei An ◽  
Guanghua Dong ◽  
...  
Keyword(s):  
Decision Making ◽  
Distance Measure ◽  
Public Procurement ◽  
Unit Price ◽  
Data Driven ◽  
Relative Distance ◽  
Multi Criteria Decision Making ◽  
Ideal Solution ◽  
Content Type ◽  
The Government

PurposeUnbalanced bidding can seriously imposed the government from obtaining the best value for the taxpayers' money in public procurement since it increases the owner's cost and decreases the fairness of the competitive bidding process. How to detect an unbalanced bid is a challenging task faced by theoretical researchers and practical actors. This study aims to develop an identification method of unbalanced bidding in the construction industry.Design/methodology/approachThe identification of unbalanced bidding is considered as a multi-criteria decision-making (MCDM) problem. A data-driven unit price database from the historical bidding document is built to present the reference unit prices as benchmarks. According to the proposed extended TOPSIS method, the data-driven unit price is chosen as the positive ideal solution, and the unit price that has the furthest absolute distance measure as the negative ideal solution. The concept of relative distance is introduced to measure the distances between positive and negative ideal solutions and each bidding unit price. The unbalanced bidding degree is ranked by means of relative distance.FindingsThe proposed model can be used for the quantitative evaluation of unbalanced bidding from a decision-making perspective. The identification process is developed according to the decision-making process. The finding shows that the model will support owners to efficiently and effectively identify unbalanced bidding in the bid evaluation stage.Originality/valueThe data-driven reference unit prices improve the accuracy of the benchmark to evaluate the unbalanced bidding. The extended TOPSIS model is applied to identify unbalanced bidding; the owners can undertake objective decision-making to identify and prevent unbalanced bidding at the stage of procurement.

Happiness at work: a multi-criteria decision-making approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rinki Dahiya ◽  
Juhi Raghuvanshi
Keyword(s):  
Decision Making ◽  
Information Technology ◽  
Transformational Leadership ◽  
Design Methodology ◽  
Causal Relation ◽  
Multi Criteria Decision Making ◽  
Human Resource Managers ◽  
Content Type ◽  
Resource Managers ◽  
Causal Framework

Purpose Researchers have strived to identify the factors enhancing happiness at work (HAW), and the causal relations among the enablers of happiness remained underexplored. Therefore, this study aims to map and prioritize the causal relation structures of enablers of HAW. Design/methodology/approach Data were collected from key representatives of information technology (IT) firms located in India. A framework based on the cause and effect relationship among enablers of HAW is proposed, and to establish this causality, the decision-making trial and evaluation laboratory (DEMATEL) technique was applied. Findings The findings indicate five out of 12 enablers as causal, namely, transformational leadership, authentizotic work climate, person–organization work fit, organizational virtuousness and meaningfulness in work. Originality/value Human resource managers, organizational policymakers and scholars will gain greater understanding through this causal framework of enablers of HAW. Knowledge and facilitation of these enablers will aid in nurturing a happy workplace.

Risk quantification in cold chain management: a federated learning-enabled multi-criteria decision-making methodology

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Henry Lau ◽  
Yung Po Tsang ◽  
Dilupa Nakandala ◽  
Carman K.M. Lee
Keyword(s):  
Decision Making ◽  
Evaluation System ◽  
Small And Medium Enterprises ◽  
Handling Time ◽  
Cold Chain ◽  
Pairwise Comparisons ◽  
Temperature Sensitive ◽  
Multi Criteria Decision Making ◽  
Firm Level ◽  
Content Type

PurposeIn the cold supply chain (SC), effective risk management is regarded as an essential component to address the risky and uncertain SC environment in handling time- and temperature-sensitive products. However, existing multi-criteria decision-making (MCDM) approaches greatly rely on expert opinions for pairwise comparisons. Despite the fact that machine learning models can be customised to conduct pairwise comparisons, it is difficult for small and medium enterprises (SMEs) to intelligently measure the ratings between risk criteria without sufficiently large datasets. Therefore, this paper aims at developing an enterprise-wide solution to identify and assess cold chain risks.Design/methodology/approachA novel federated learning (FL)-enabled multi-criteria risk evaluation system (FMRES) is proposed, which integrates FL and the best–worst method (BWM) to measure firm-level cold chain risks under the suggested risk hierarchical structure. The factors of technologies and equipment, operations, external environment, and personnel and organisation are considered. Furthermore, a case analysis of an e-grocery SC in Australia is conducted to examine the feasibility of the proposed approach.FindingsThroughout this study, it is found that embedding the FL mechanism into the MCDM process is effective in acquiring knowledge of pairwise comparisons from experts. A trusted federation in a cold chain network is therefore formulated to identify and assess cold SC risks in a systematic manner.Originality/valueA novel hybridisation between horizontal FL and MCDM process is explored, which enhances the autonomy of the MCDM approaches to evaluate cold chain risks under the structured hierarchy.

New Einstein Hybrid Aggregation Operators for Intuitionistic Fuzzy Sets and Applications in Multi-Criteria Decision-Making

2019 ◽  
pp. 252-283
Author(s):  
Bhagawati Prasad Joshi ◽  
Abhay Kumar
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Decision Maker ◽  
Intuitionistic Fuzzy Sets ◽  
Aggregation Operators ◽  
Weighted Averaging ◽  
Multi Criteria Decision Making ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Environment ◽  
Decision Making Processes

The fusion of multidimensional intuitionistic fuzzy information plays an important part in decision making processes under an intuitionistic fuzzy environment. In this chapter, it is observed that existing intuitionistic fuzzy Einstein hybrid aggregation operators do not follow the idempotency and boundedness. This leads to sometimes illogical and even absurd results to the decision maker. Hence, some new intuitionistic fuzzy Einstein hybrid aggregation operators such as the new intuitionistic fuzzy Einstein hybrid weighted averaging (IFEHWA) and the new intuitionistic fuzzy Einstein hybrid weighted geometric (IFEHWG) were developed. The new IFEHWA and IFEHWG operators can weigh the arguments as well as their ordered positions the same as the intuitionistic fuzzy Einstein hybrid aggregation operators do. Further, it is validated that the defined operators are idempotent, bounded, monotonic and commutative. Then, based on the developed approach, a multi-criteria decision-making (MCDM) procedure is given. Finally, a numerical example is conducted to demonstrate the proposed method effectively.

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