The Application of Fuzzy Analytic Hierarchy Process in Sustainable Project Selection

Comparing wildlife habitat suitability models based on expert opinion with camera trap detections

Expert knowledge is used in the development of wildlife habitat suitability models (HSMs) for management and conservation decisions. However, the consistency of such models has been questioned. Focusing on 1 method for elicitation, the analytic hierarchy process, we generated expert-based HSMs for 4 felid species: 2 forest specialists (ocelot [Leopardus pardalis] and margay [Leopardus wiedii]) and 2 habitat generalist species (Pampas cat [Leopardus colocola] and puma [Puma concolor]). Using these HSMs, species detections from camera-trap surveys, and generalized linear models, we assessed the effect of study species and expert attributes on the correspondence between expert models and camera-trap detections. We also examined whether aggregation of participant responses and iterative feedback improved model performance. We ran 160 HSMs and found that models for specialist species showed higher correspondence with camera-trap detections (AUC [area under the receiver operating characteristic curve] >0.7) than those for generalists (AUC < 0.7). Model correspondence increased as participant years of experience in the study area increased, but only for the understudied generalist species, Pampas cat (β = 0.024 [SE 0.007]). No other participant attribute was associated with model correspondence. Feedback and revision of models improved model correspondence, and aggregating judgments across multiple participants improved correspondence only for specialist species. The average correspondence of aggregated judgments increased as group size increased but leveled off after 5 experts for all species. Our results suggest that correspondence between expert models and empirical surveys increases as habitat specialization increases. We encourage inclusion of participants knowledgeable of the study area and model validation for expert-based modeling of understudied and generalist species.

Using the deterministic approach model for project portfolio selection problem (PPSP) solutions

Selection of projects using a robust technique is rare as most of the techniques are not considered useful due to the limitation on the number of projects that can be selected as well as cost saving projects not being selected. This study investigated the validity of a hybrid model - integrated analytical hierarchy process-goal programming (AHP-GP) - to avoid project portfolio selection problems delaying community development. The proposed model includes two steps: AHP to determine the project criteria, the relative importance of weights, and priority preferences, while the GP model was formulated to select the optimal projects. An empirical study on government agencies was carried out to validate the proposed model, and the results compared against GP as a standalone to solve the same problem. The results proved that the hybrid model (AHP-GP) was better than the GP model. AHP-GP has proved to be a robust mechanism most suitable for managerial use due to its ability to handle multi-criteria decision-making (MCDM) situations. This study showed that the hybrid model can select more projects and will create more jobs in the communities concerned compared to the single model (GP). The novelty of this study is the introduction of an integrated model formed from two distinct models as a deterministic approach to solving project portfolio selection problems.

A comprehensive evaluation of private sector investment decisions for sustainable water supply systems using a fuzzy-analytic hierarchy process: A case study of Ha Nam province in Vietnam

This study examined the factors that affect the private sectors' willingness to invest in rural water supply. The study applied a mixed methods approach, including an overview of relevant studies, expert consultation, exploratory factor analysis using SPSS software, and a fuzzy-analytic hierarchy process to identify and evaluate the factors applicable to Ha Nam province in Vietnam. Some factors were distinguished that are significant to private investors' rural water supply investment decisions, including tax incentive policy, policies to support preferred access to loans and credit, a state risk-sharing mechanism, a mechanism to adjust water price, community support, high community demand for clean water, and input water quality. In addition, the study constructed an investment attractiveness index to evaluate the attractiveness of private sector investment for two typical rural water supply projects in Ha Nam province. This index can be used as a basis for the government to design appropriate incentives to attract investment from private investors and construct an investment attractiveness map.

Quality improvement of undergraduate courses based on fuzzy analytic hierarchy process and entropy method

Since the curriculum is the core carrier to improve the level of talent cultivation in colleges and universities, strengthening the reform of course teaching and improving the quality of course teaching are fundamental to the survival and development of colleges and universities, and also an important part of higher education reform. In this study, a fuzzy analytic hierarchy process (AHP) and an entropy method were used to determine the weight of the core evaluation indicators of undergraduate course quality improvement, including four first-level indicators of the curriculum concept, curriculum resources, curriculum organization, and curriculum effectiveness, and 12 s-level evaluation indicators and weights. Then, based on a case study of the first-class undergraduate course “Management” of Anyang Normal University, the way to evaluate the course by the AHP and entropy method was explained. Finally, according to the evaluation results, the ideas of course construction were put forward, such as changing the course concept, enriching the course resources, paying attention to the course organization, and ensuring the course effectiveness, so as to improve the quality of undergraduate courses and also to improve the quality of undergraduate talent training with the improvement of course quality as the starting point.

A Hybrid ANP Method for Evaluation of Government Data Sustainability

The evaluation of government data sustainability is a multicriteria decision making problem. The analytic network process (ANP) is among the most popular methods in determining the weights of criteria, and its limitation is the un-convergence problem. This paper proposes a hybrid ANP (H-ANP) method, which aims to improve the ANP by combining the weights obtained from the analytic hierarchy process (AHP). The proposed method is proved to be convergent since the network of the H-ANP is strongly connected. According to the simulation experiments, H-ANP is more robust than ANP under different settings of parameters. It also shows a higher Kendall cor-relationship and lower MSE with respect to AHP, compared with the existing method (e.g., the averagely connected ANP method). An empirical example is also provided, which uses H-ANP to evaluate the government data sustainability of a city.

A Fuzzy Multicriteria Group Decision Making Approach for Evaluating and Selecting Fintech Projects

The use of financial technologies (Fintech) has increased recently due to their support to financial institutions in managing their financial operations and achieving competitive advantages. Even though there are several benefits with Fintech development and implementation, selecting the most suitable Fintech project can be complex. This is due to the involvement of numerous decision makers, the conflicting nature of multiple evaluation criteria, and fuzzy data derived from imprecise judgments of qualitative performance ratings. Interval-valued based intuitionistic fuzzy numbers are used to deal with the inherent vagueness and imprecision of the evaluation process. An algorithm based on an interval-valued intuitionistic fuzzy weighted geometric (IIFWG) and the concept of ideal solutions is developed. As a result, the most suitable Fintech project alternative can be selected across all evaluation criteria. To demonstrate the effectiveness of the approach, a Fintech project selection problem is presented.

Identification of Factors Affecting Risk Appetite of Organizations in Selection of Mega Construction Projects

Risk appetite is a crucial component that plays a key role in the decision-making process of project risk management. Despite rising scholarly interest in project risk management, risk appetite has received little attention thus far. A well-defined risk appetite ensures that all decisions taken throughout the course of a project are consistent with an organization’s ultimate strategic aim. This research aims to identify the factors affecting the risk appetite of megaproject selection in the construction sector. The study adds to the knowledge of risk appetite in mega construction project selection and qualitatively examines the factors affecting risk appetite. Exploratory research design is used to identify these factors. The factors are identified using semi-structured interviews of 30 practitioners from the top and middle management working on mega construction projects. Thematic analysis was performed using NVIVO academic software. The most highlighted factors are financial attributes, board of directors’ agility, political factors, project location and demographics. The proposed conceptual framework identifies the factors affecting the risk appetite of mega construction project selection. These factors may be utilized as a starting point by construction project organizations to evaluate the risk appetite of a mega construction project. Risk appetite-based project selection will decrease chances of failures, delays, and cost overruns in mega-projects. These factors can be used by researchers as a rationale for developing predictive or descriptive models of project selection based on risk appetite.

Decision analysis of warehouse productivity performance indicators to enhance logistics operational efficiency

Purpose

The warehouse industry is one of the backbones in the logistics operation which involves several activities i.e. storage, receiving, picking and shipping of goods/cargoes. This study analyzes the most important warehouse productivity indicators for improving warehouse operation efficiency.

Design/methodology/approach

This study presents an empirical methodology of the fuzzy analytical hierarchy process (FAHP) method, an integration between the fuzzy logic method with an analytical hierarchy process (AHP) method incorporated with the adoption of quantitative and systems theories under the modern management theory approach.

Findings

The results indicate that the weight values of the main criteria which lead by the criterion “Space (0.4005)” at the top ranking, followed by Information System (0.2445), Labor (0.2065) and Equipment (0.1484). In addition, the weight values and ranking of the 16 sub-criteria are also highlighted which the sub-criterion “Warehouse Management System (0.2445)” scores the highest weight value and followed by Storage Space Utilization (0.1043) and Throughput (0.0722) accordingly.

Research limitations/implications

Finally, this research contributed to enrich the literature, while highlighting a series of recommendations on the top three most significant productivity performance indicators that can be useful in further research.

Originality/value

A generic analysis model developed with the adoption of three study theories: quantitative, system and productivity theories.

Driving Sustainability through Engineering Management and Systems Engineering

The COVID-19 pandemic has highlighted the somewhat precarious nature of our lives, including the way we work and our lifestyles [...]

Symmetric and Asymmetric Data in Solution Models

This Special Issue covers symmetric and asymmetric data that occur in real-life problems. We invited authors to submit their theoretical or experimental research to present engineering and economic problem solution models that deal with symmetry or asymmetry of different data types. The Special Issue gained interest in the research community and received many submissions. After rigorous scientific evaluation by editors and reviewers, seventeen papers were accepted and published. The authors proposed different solution models, mainly covering uncertain data in multi-criteria decision-making problems as complex tools to balance the symmetry between goals, risks, and constraints to cope with the complicated problems in engineering or management. Therefore, we invite researchers interested in the topics to read the papers provided in the Special Issue.

Discussing the Use of Complexity Theory in Engineering Management: Implications for Sustainability

What is the state-of-the-art literature regarding the adoption of the complexity theory (CT) in engineering management (EM)? What implications can be derived for future research and practices concerning sustainability issues? In this conceptual article, we critically discuss the current status of complexity research in EM. In this regard, we use IEEE Transactions on Engineering Management, because it is currently considered the leading journal in EM, and is as a reliable, heuristic proxy. From this journal, we analyze 38 representative publications on the topic published since 2000, and extrapolated through a rigorous keyword-based article search. In particular, we show that: (1) the adoption of CT has been associated with a wide range of key themes in EM, such as new product development, supply chain, and project management. (2) The adoption of CT has been witnessed in an increasing amount of publications, with a focus on conceptual modeling based on fuzzy logics, stochastic, or agent-based modeling prevailing. (3) Many key features of CT seem to be quite clearly observable in our dataset, with modeling and optimizing decision making, under uncertainty, as the dominant theme. However, only a limited number of studies appear to formally adhere to CT, to explain the different EM issues investigated. Thus, we derive various implications for EM research (concerning the research in and practice on sustainability issues).