Ordinal Priority Approach (OPA) in Multiple Attribute Decision-Making
The current study aims to present a new method called Ordinal Priority Approach (OPA) in Multiple Attribute Decision-Making (MADM). This method can be used in individual or group decision-making (GDM). In the case of GDM, through this method, we first determine the experts and their priorities. The priority of experts may be determined based on their experience and/or knowledge. After prioritization of the experts, the attributes are prioritized by each expert. Meanwhile, each expert ranks the alternatives based on each attribute, and the sub-attributes if any. Ultimately, by solving the presented linear programming model of this method, the weights of the attributes, alternatives, experts, and sub-attributes would be obtained simultaneously. A significant advantage of the proposed method is that it does not make use of pairwise comparison matrix, decision-making matrix (no need for numerical input), normalization methods, averaging methods for aggregating the opinions of experts (in GDM) and linguistic variables. Another advantage of this method is the possibility for experts to only comment on the attributes and alternatives for which they have sufficient knowledge and experience. The validity of the proposed model has been evaluated using several group and individual instances. Finally, the proposed method has been compared with other methods such as AHP, BWM, TOPSIS, VIKOR, PROMETHEE and QUALIFLEX. Based on comparisons among the weights and ranks using Spearman and Pearson correlation coefficients, the proposed method has an applicable performance compared with other methods.
Keywords: Multiple-Criteria Decision-Making, Group Decision-Making, Ordinal Priority ApproachSee the full paper : Ordinal Priority Approach (OPA) in Multiple Attribute Decision-Making
Probabilistic Approach to Multi-Stage Supplier Evaluation: Confidence Level Measurement in Ordinal Priority Approach
A popular framework of the supplier selection process is usually characterized by problem definition, criteria formulation, supplier screening, and supplier selection. The literature review suggested limitations of this framework as it ignores the screening of criteria (beyond criteria weighing) and evaluators (buyers) and its inability to guide the supplier selection problems where a measure of confidence or trust is needed to confirm the reliability of the selected supplier. While extending de Boer’s influential supplier selection framework, the current study argues that the supplier selection problem is not merely about ranking suppliers based on given criteria; instead, it involves evaluating criteria and evaluators (experts) as well. Guided by the theory of statistics and the Ordinal Priority Approach (OPA), the study pioneers a probabilistic approach of supplier evaluation and selection under incomplete information using a novel Confidence Level measure. The study suggests, the probability that a supplier shortlisted for selection is actually the optimum choice or not can be explained through a probability distribution, called W-distribution, therefore, confidently preventing the DMs from selecting the sub-optimum suppliers. The study presents a novel contribution to the theory of multiple-attribute decision-making through the OPA. The proposed approach can help build intelligent decision support systems to aid managers while providing them with early warning tools and suggestions to improve confidence in their selection.
Keywords: Ordinal Priority Approach; confidence level measurement; supplier selection; supply chain management; multi-criteria decision analysis; decision support systemSee the full paper : Probabilistic Approach to Multi-Stage Supplier Evaluation: Confidence Level Measurement in Ordinal Priority Approach
Large-Scale Group Decision-Making (LSGDM) for Performance Measurement of Healthcare Construction Projects: Ordinal Priority Approach
People with various skill sets and backgrounds are usually found working on projects and thus, group decision-making (GDM) is one of the most important functions within any project. However, when projects concern healthcare or other critical services for proletariat or general public (especially during COVID19), the importance of GDM can hardly be overstated. Measuring the performance of healthcare construction projects is a critical activity and should be gauged based on the input from a large number of stakeholders. Such problems are usually recognized as large-scale group decision-making (LSGDM). In the current study, we aim to propose a decision support system for measuring the performance of healthcare construction projects against a large number of experts using ordinal data. The study identifies several key indicators from literature and recorded the observations of a large number of experts about these indicators. After that, the acceptable range of complexity is specified, the Silhouette plot is provided to find the optimal number of clusters, and the ordinal K-means method is employed to cluster the experts’ opinions. Later, the confidence level is measured using a novel Weighted Kendall’s W for the optimal number of the clusters, and the threshold is checked. Finally, the conventional problem is solved using the Group Weighted Ordinal Priority Approach (GWOPA) model in multiple attributes decision making (MADM), and the performance of the projects is determined. The validity of the proposed approach is confirmed through a comparative analysis. Also, a real-world case is solved, and the performance of some healthcare construction projects in China is gauged with a comprehensive sensitivity analysis.
Keywords: Ordinal priority approach, multiple criteria decision analysis, weighted Kendall’s W, healthcare construction projects, large-scale group decision-making, Ordinal K-means.See the full paper : Large-Scale Group Decision-Making (LSGDM) for Performance Measurement of Healthcare Construction Projects: Ordinal Priority Approach
A novel project portfolio selection framework towards organizational resilience: Robust Ordinal Priority Approach
The COVID-19 pandemic has affected the world’s economic condition significantly, and construction projects have faced many challenges and disruptions as well. This should be an alarm bell for project-oriented organizations to be prepared for such events and take necessary actions at the earliest time. In this regard, project-oriented organizations should establish their business based on the resilience concept, making them flexible in dealing with risks and decreasing the recovery time after disruptions. The current study proposes a practical conceptual framework for project-oriented organizations to select the most appropriate portfolio based on organizational resilience strategy. First, portfolios are identified, and the projects are clustered based on organizational resilience strategy using the Elbow and Fuzzy C-Means methods. The projects’ scores are then determined employing the stakeholders’ opinions and the Robust Ordinal Priority Approach (OPA-R), which can handle the uncertainty of the input data. After that, each portfolio’s score is determined using the obtained scores of the projects, and the best portfolio linked to the organizational resilience strategy is selected. The application of the proposed method to a project-oriented organization is examined, and its usage for the managers of project-oriented organizations is discussed in detail.
Keywords: Multiple criteria decision-making, Organizational resilience, Project portfolio selection, Robust ordinal priority approach, Fuzzy C-meansSee the full paper : A novel project portfolio selection framework towards organizational resilience: Robust Ordinal Priority Approach
Sustainable Supplier Selection in Megaprojects: Grey Ordinal Priority Approach
Due to mounting environmental and social challenges, supplier selection has become one of the most critical tasks of project‐oriented organizations. Because supplier selection can affect the long‐term success and profitability of the organizations and their projects, directly, embracing sustainability can add value in the equation. Considering sustainability measures can positively guide project managers in making better decisions for the projects in the long term. Therefore, the current study attempts to provide a conceptual model for selecting the best supplier based on a sustainability framework in megaprojects. Meanwhile, decision‐making methods can be employed as a proper tool to find the best supplier. Ordinal priority approach (OPA) is a recent development in multiple criteria decision making (MCDM), while it has many benefits compared with other methods like analytic hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS). However, this method cannot consider multiple ranks during the decision‐making process, and using an uncertainty approach feels strongly. Grey systems theory (GST) can consider uncertainties with no need for large sample or proposing membership function. Hence, the current study employed the GST to consider multiple ranks for criteria and alternatives in the OPA method. This is the first time that a sustainable supplier selection framework has been presented for megaprojects with the aid of the Grey OPA (OPA‐G) method. Finally, a case study has been examined to evaluate the performance of the proposed approach. The results show that the proposed approach can be used in real‐world situations and it has acceptable performance under uncertainty conditions.
Keywords: decision making under uncertainty, grey systems theory, megaprojects, multiple criteria decision making, ordinal priority approach, sustainable supplier selectionSee the full paper : Sustainable Supplier Selection in Megaprojects: Grey Ordinal Priority Approach
Evaluating the Performance of the Suppliers Using Hybrid DEA-OPA Model: A Sustainable Development Perspective
One of the most important activities in any organization is the identification and selection of the right supplier. The responsible organizations determine the performance of their potential suppliers based on the attributes that are aligned with their sustainable development goals. Data regarding these attributes can be qualitative and/or quantitative, and not every supplier selection methodology can handle them simultaneously. Data envelopment analysis (DEA) is an influential methodology for measuring the suppliers’ performance, especially when there are more than one input and/or output. However, the original DEA model cannot consider human judgment during evaluation. In this regard, the current study proposes a novel methodology with the aid of the Ordinal Priority Approach (OPA) of multiple attributes decision-making and the DEA. The proposed method, the DEA-OPA model, truly enjoys the advantages of both DEA and OPA models, making it more powerful than the original DEA for performance measurement. The proposed model was compared with the original DEA and OPA then retested through incomplete data when the experts lack enough knowledge about inputs and outputs. Finally, a pilot application has been executed in the paper industry, and comprehensive sensitivity analysis has been performed to illustrate the feasibility of the proposed model. The findings are of significant importance to responsible enterprises and organizational decision-makers.
Keywords: Multiple-criteria decision analysis, Sustainable development, Data envelopment analysis, Ordinal priority approach, Supply chain management, Performance measurementSee the full paper : Evaluating the Performance of the Suppliers Using Hybrid DEA-OPA Model: A Sustainable Development Perspective
Gresilient supplier selection through Fuzzy Ordinal Priority Approach: decision‑making in post‑COVID era
Because of the COVID-19 outbreak, the supply of both essential and non-essential goods and services has been unprecedentedly disrupted. In the absence of any playbook, the need for innovative technologies to aid recovery from the Supply Chain (SC) disruptions quickly and effectively becomes persistent. The study aims to develop an innovative decision-making technology to handle the supplier selection problems arising from the frequent impreciseness and incompleteness in the nowadays’ SC reports within the framework of the gresilient supply chain management. Through the integration of green and resilience aspects of the SCs, the supply chain ’gresilience’ has been conceptualized. In light of this construct and based on the data collected from a manufacturing firm, the study deploys a two-fold decomposition of the core algorithm of the Ordinal Priority Approach (OPA), one for attributes and other for alternatives. It extends the OPA to the Fuzzy OPA (OPA-F) for solving the supplier selection problems. The study illustrates how green and resilience aspects of the SC can be integrated to better understand the gresilient suppliers in the wake of the SC disruptions. A novel construct of SC Gresilience is also furnished along with a novel definition of SC Gresilience. It also provides an innovative SC decision-making technology to help procurement managers to evaluate their suppliers. The resultant framework can help them better prepare for the COVID-19 like disruptions in the future.
Keywords: Green supply chain, Resilience, Supplier selection, Fuzzy Ordinal Priority ApproachSee the full paper : Gresilient supplier selection through Fuzzy Ordinal Priority Approach: decision‑making in post‑COVID era
Performance Evaluation of Construction Sub-contractors using Ordinal Priority Approach
The construction industry has been recognized as one of the crucial industries of any country. In large-scale public projects, it is frequently observed that the client, who serves the public, seeks the services of several sub-contractors (project outsourcing firms) to execute projects. It also seeks the consulting firms’ services to monitor the progress and performance of these sub-contractors. However, performance evaluation of a large number of sub-contractors is not only a challenging phase but also a source of conflict and mistrust between the evaluators and evaluatees because of the subjectivity in the evaluation process. The current study classifies perceived organizational performance into two streams, arguing that the one involving the opinions from the independent evaluators is more objective than the self-evaluation one. The study also makes a pioneering attempt in post-qualification performance evaluation of construction project outsourcing firms through the Ordinal Priority Approach (OPA), a promising multiple-attribute decision-making methodology. The deployment of the OPA allows the decision-makers to estimate the weights of the evaluation criteria, the sub-contractors to be evaluated, and the experts who evaluated them simultaneously. Thus, the methodology can minimize the causes of mistrust by uncovering unreliable experts and inappropriate criteria. Also, a novel Relative Performance Index (RPI) has been proposed to standardize the performance evaluation system. The results show that evaluation of the firms does not end at their evaluation as without evaluating the evaluators, such an evaluation is only partially effective.
Keywords: Sub-contractor, Performance evaluation, Ordinal Priority Approach, Construction project execution, Relative Performance Index, Multiple criteria decision-makingSee the full paper : Performance Evaluation of Construction Sub-contractors using Ordinal Priority Approach
Large-scale multiple criteria decision-making with missing values: project selection through TOPSIS-OPA
Nowadays, with the development of information management infrastructures in organizations and the improvement of the data storage process, managers are looking for appropriate decision-making methods based on large volumes of data. Therefore, it is crucial to choose the right approach to make the right decisions based on the volume of available data. The present study seeks to provide a comprehensive framework for the decision-making process using big data, even when it is incomplete. The framework of multiple criteria decision making (MCDM) consists of criteria and alternatives, whereas in real-world cases, decision-makers may face several criteria and alternatives. In this study, the Principal Component Analysis (PCA) approach was selected for the criteria clustering. Later, the K-means algorithm is used to cluster the alternatives, which estimates the optimal number of clusters using the Elbow method. The Fuzzy TOPSIS (TOPSIS-F) and Ordinal Priority Approach (OPA) have been used to rank clusters. Ultimately, the best alternative in the top cluster has been identified with the aid of the OPA, which has a unique function to solve MCDM problems with incomplete data. For evaluating the performance of the proposed approach, first, a pilot testing has been executed on a real-world case, and then a practical study was conducted at a refinery equipment manufacturing company with a project-oriented organizational structure. The approach is flexible, interactive, intelligent, and integrative, and significantly reduces the time and computation costs for the decision-makers. The results confirmed the soundness of the proposed approach, which can be used by managers of different companies with confidence.
Keywords: Big data, Intelligent multiple criteria decision making, Ordinal priority approach, Project selection problem, K-means, Fuzzy TOPSISSee the full paper : Large-scale multiple criteria decision-making with missing values
Adopting distributed ledger technology for the sustainable construction industry: evaluating the barriers using Ordinal Priority Approach
Construction 4.0 has become a buzzword since the penetration of building information modeling (BIM), cyber-physical systems, and digital and computing technologies into the construction industry. Among emerging technologies, distributed ledger technology (DLT), or blockchain, is a powerful business enhancer whose potential can disrupt projects, AEC (architecture, engineering, and construction) firms, and construction supply chain, and in a broader sense, the whole construction industry. This technology has not reached the plateau of productivity due to several barriers and challenges. Previous studies have started to investigate the barriers to implementing DLT in various sectors and segmentations. However, we still need further surveys in the construction industry. This study evaluates the applicability of identified challenges and barriers based on a sustainability perspective. Precisely, we will answer which challenges need to be addressed for the sustainability of the construction industry. To meet the research objective, the ordinal priority approach (OPA) in multiple attributes decision-making (MADM) was utilized. This novel method determines the weight of sustainability attributes and barriers simultaneously. The results show that DLT implementation needs (i) infrastructure for data management, (ii) advanced applications and archetypes, and (iii) customers’ demand, interest, and tendency, and (iv) taxation and reporting. Solving high-ranked challenges is the key to social sustainability from the aspects of “supply chain management and procurement”; “transparency, anti-corruption, and anti-counterfeiting”; and “fair operation and honest competition.”
Keywords: Construction 4.0, Building information modeling (BIM), Distributed ledger technology (DLT), Blockchain, Sustainability, Ordinal Priority Approach (OPA), Multiple attributes decision-making (MADM)See the full paper : Adopting distributed ledger technology for the sustainable construction industry: evaluating the barriers using Ordinal Priority Approach
Performance measurement of construction suppliers under localization, agility, and digitalization criteria: Fuzzy Ordinal Priority Approach
The suppliers’ performance plays a vital role, with a domino effect, in project success, organizational competitiveness, protecting supply chain and construction industry from disruptions and PESTEL risks (political, economic, social, technological, environmental, and legal). Therefore, measuring the performance of the construction suppliers has become the primary focus of project-oriented organizations and the core of business decision-making, especially during global megatrends. The question that may arise here is, “How can the performance of the construction suppliers be determined under uncertainties considering the post-COVID-19 era?” Organizations need eligible suppliers for the rapid recovery of the supply chain and construction sector at this critical stage. Given the importance of the issue, this study aims to propose a novel approach for measuring the performance of construction suppliers using the fuzzy ordinal priority approach (OPA-F). OPA-F is a recent development in multiple criteria decision-making (MCDM) that can determine the criteria weights for performance measurement using fuzzy linguistic variables. We do not always have access to a complete data set in real-world situations and business environments. Nevertheless, OPA-F can handle this dilemma, even with incomplete input data. This research intends to consider three main aspects of the construction suppliers, known as (L-A-D) capabilities, including localization, agility, and digitalization. In this regard, we bring up a case study from the construction industry to demonstrate the application of the proposed framework. The findings show that the most critical criterion is “digitalization” for the case study. This criterion covers “supply chain automation” and “virtualization and dematerialization” of services/products. The proposed approach is practical and straightforward, particularly for academicians and decision-makers; it can also incorporate uncertainties.
Keywords: Performance measurement, Supply chain disruptions, Localization, Agility, Digitalization, Fuzzy ordinal priority approachSee the full paper : Performance measurement of construction suppliers under localization, agility, and digitalization criteria: Fuzzy Ordinal Priority Approach
Blockchain technology in construction organizations: risk assessment using trapezoidal fuzzy ordinal priority approach
Purpose: In the digital transformation era, the construction industry is not immune to unintended consequences and disruptions of distributed ledger technologies like blockchain. At the micro-level, construction organizations need an in-depth understanding of blockchain risks to take proactive strategies for being on the safe side. This study seeks to answer “What are the risks associated with blockchain technology from the firm-level perspective? And how can this disruptive technology overshadow the business objectives and impact organizational criteria?”
Design/methodology/approach: The current research proposes a novel model for risk assessment based on the trapezoidal fuzzy ordinal priority approach (OPA-F) in the multiple-criteria decision-making (MCDM) context. The proposed model handles uncertainties of experts’ judgment around three primary parameters: the importance of organizational criteria, the impact of blockchain risks on criteria and the probability of risk occurrence.
Findings: The case study shows that organizational “communication and information” is exposed to the most blockchain risk. On the contrary, blockchain has less to do with an organization’s “corporate social responsibility.” Furthermore, effective blockchain risk management can bring about cost efficiency, quality and improved customer experience for this case study. In the end, the authors develop a conceptual blockchain risk management framework based on findings.
Research limitations/implications: This study will broaden researchers’ horizons regarding “blockchain in construction context” and “blockchain risk management.”
Practical implications: Furthermore, executives looking for blockchain-based solutions can benefit from research findings and lessons learned from this case study before decision-making. Lastly, the risk assessment model based on trapezoidal OPA-F can be used both for research purposes and industrial decision problems.
Originality/value: To the best of the authors’ knowledge, it is for the first time that the OPA-F is employed in a risk assessment model. Also, the original OPA-F is extended to trapezoidal OPA-F using trapezoidal fuzzy numbers, and it is the first attempt to evaluate blockchain risks facing construction organizations and develop a blockchain risk management framework accordingly.
Keywords: Distributed ledger technology, Blockchain technology, Construction organizations, Risk assessment, Multiple-criteria decision-making, Fuzzy ordinal priority approachSee the full paper : Blockchain technology in construction organizations: risk assessment using trapezoidal fuzzy ordinal priority approach
Prioritizing Requirements for Implementing Blockchain in Construction Supply Chain Based on Circular Economy: Fuzzy Ordinal Priority Approach
The synergy between blockchain technology and circular economy can lead to environmental and economic sustainability by protecting natural resources, optimizing processes, fostering system effectiveness, and designing out waste. Nevertheless, industries and their supply chain need to consider several requirements for blockchain implementation to realize a circular economy. To achieve this goal, requirements for implementing blockchain technology in construction supply chain based on circular economy attributes were prioritized. For making our preference decision, we employed the new-founded multiple-attribute decision-making (MADM) method, the “Fuzzy Ordinal Priority Approach (Fuzzy OPA).” Under uncertainty conditions, this method can determine fuzzy weights of circular economy attributes and fuzzy scores of blockchain requirements using linguistic variables. The results obtained from the case study show that three high-ranked requirements for implementing blockchain are (1) Developing circular economy attributes on platforms, (2) Intra-organizational considerations, and (3) Technological requirements and collaboration infrastructure. To discuss the circular economy, we used the ReSOLVE framework, consisting of six attributes “Regenerate, Share, Optimize, Loop, Virtualize, and Exchange.” According to research findings, blockchain implementation can move the construction industry toward a circular economy through (1) Regenerating environment, (2) Optimizing systems performance, and (3) Looping and circulating products.