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Managing complex construction projects is a challenging task because it involves multiple factors and decision-making processes. A systematic evaluation of these complex factors is imperative for achieving project success. As most of these factors are qualitative or intangible in nature, decision makers often rely on subjective judgements when comparing and evaluating them. The hybrid techniques that integrate fuzzy set theory and the analytic hierarchy process (AHP) are able to deal with such problems. This chapter discusses various hybrid techniques of the fuzzy AHP and presents an application of these techniques to the evaluation of transportation project complexity, which is essential for prioritising resource allocation and assessing project performance. Project complexity can be quantified and visualised effectively with the application of the fuzzy AHP. This chapter enhances the understanding of construction project complexity and fuzzy hybrid computing in construction engineering and management. Future research should address the calibration of fuzzy membership functions in pairwise comparisons for each individual decision maker and develop computational tools for solving optimisation problems in the constrained fuzzy AHP. In the area of construction project complexity, future research should investigate how scarce resources are allocated to better manage complex projects and how appropriate resource allocation improves their performance.

The robust appraisal of exploration drilling concepts is essential for establishing the economic viability of a prospective recovery field. This study evaluates the different concept selection methods that were considered for drilling operations at the Trym field in Norway. The construction of drilling rigs is a capital-intensive process, and it involves high levels of economic risk. These risks can be broadly categorised as aleatoric (i.e. those related to chance) and epistemic (i.e. those related to knowledge). Evaluating risks in the investment appraisal process tends to be a complicated process. Project risks are evaluated using Monte Carlo simulation (MCS) and are based on the fuzzy analytic hierarchy process (AHP). MCS provides a useful means of evaluating variabilities (i.e. aleatoric risks) in oil drilling operations. However, many of the economic risks in oil drilling processes are unanticipated, and, in some cases, are not readily expressible in quantitative values. The fuzzy AHP is therefore used to appraise the qualitatively defined indirect revenues comprising risks that affect future flexibilities, schedule certainty and health and safety performance. Both the Monte Carlo technique and the fuzzy AHP technique found that a cumulative revenue variation of up to 30% is possible in any of the considered drilling options. The fuzzy AHP technique estimates that the chances of profitability being less than NOK 1 billion over a five-year period is 0.5%, while the Monte Carlo technique estimates suggest a more conservative proportion of 10%. Overall, the fuzzy AHP technique is easy to use and flexible, and it demonstrates increased robustness and improved predictability.

The existing literature expresses a strong need to develop tools that support the manufacturing reshoring decision-making process. This paper aims to examine the suitability of analytical hierarchy process (AHP)-based tools for initial screening of manufacturing reshoring decisions.

Two AHP-based tools for the initial screening of manufacturing reshoring decisions are developed. The first tool is based on traditional AHP, while the second is based on fuzzy-AHP. Six high-level and holistic reshoring criteria based on competitive priorities were identified through a literature review. Next, a panel of experts from a Swedish manufacturing company was involved in the overall comparison of the criteria. Based on this comparison, priority weights of the criteria were obtained through a pairwise analysis. Subsequently, the priority weights were used in a weighted-sum manner to evaluate 20 reshoring scenarios. Afterwards, the outputs from the traditional AHP and fuzzy-AHP tools were compared to the opinions of the experts. Finally, a sensitivity analysis was performed to evaluate the stability of the developed decision support tools.

The research demonstrates that AHP-based support tools are suitable for the initial screening of manufacturing reshoring decisions. With regard to the presented set of criteria and reshoring scenarios, both traditional AHP and fuzzy-AHP are shown to be consistent with the experts' decisions. Moreover, fuzzy-AHP is shown to be marginally more reliable than traditional AHP. According to the sensitivity analysis, the order of importance of the six criteria is stable for high values of weights of cost and quality criteria.

The limitation of the developed AHP-based tools is that they currently only include a limited number of high-level decision criteria. Therefore, future research should focus on adding low-level criteria to the tools using a multi-level architecture. The current research contributes to the body of literature on the manufacturing reshoring decision-making process by addressing decision-making issues in general and by demonstrating the suitability of two decision support tools applied to the manufacturing reshoring field in particular.

This research provides practitioners with two decision support tools for the initial screening of manufacturing reshoring decisions, which will help managers optimize their time and resources on the most promising reshoring alternatives. Given the complex nature of reshoring decisions, the results from the fuzzy-AHP are shown to be slightly closer to those of the experts than traditional AHP for initial screening of manufacturing relocation decisions.

This paper describes two decision support tools that can be applied for the initial screening of manufacturing reshoring decisions while considering six high-level and holistic criteria. Both support tools are applied to evaluate 20 identical manufacturing reshoring scenarios, allowing a comparison of their output. The sensitivity analysis demonstrates the relative importance of the reshoring criteria.

Since the demand for energy has dramatically increased in the countries which have fast-growing population and economy, they have faced with a critical problem of how to evaluate a set of potential energy sources (i.e. nuclear, natural gas, bio, geothermal, hydro, wind and solar) and choose the ultimate energy source for their needs. On the other hand, this critical problem turns into a multiple-criteria decision-making (MCDM) in the presence of a set of energy source alternatives and evaluation criteria. In literature, there are many MCDM methods introduced to solve for different kinds of problems. The purpose of this paper is to present an integrated approach for evaluating energy sources using fuzzy AHP and GRA, with a case for Turkey.

In this paper, the analytic hierarchy process (AHP) and grey relational analysis (GRA) methods are used because of their advantages for similar problems. On the other hand, due to the fact that the conventional AHP by a nine-point scale and GRA method using a scale with crisp values can be unable to handle to capture the right judgments of a decision-maker(s), to reflect the vagueness and uncertainty on the judgments of a decision-maker, the fuzzy logic is integrated with the AHP and GRA.

The contributions of the paper to the literature are given in two dimensions as follows: it presents an integrated approach for complex decision processes with subjective data or vague information; the proposed approach, the fuzzy AHP-GRA method for energy source selection, is unique for the related problem in literature. The results of the proposed model from the case of Turkey will help practitioners and experts of how to apply it to the similar problems in the field of energy management.

In short, in this paper, an integrated approach is proposed through the fuzzy AHP and the fuzzy GRA methods. As the fuzzy AHP is used to determine the weights of evaluation criteria, the fuzzy GRA is used to rank energy source alternatives.

In addition, a case study for Turkey is presented to show the applicability of the proposed approach for potential practitioners who are authority in the field of energy in public and private sectors.

On the other hand, the proposed approach, the fuzzy AHP-GRA for energy source selection can also be an intelligent tool for public and private energy companies in Turkey, as well as others in the world.

On the other hand, in this paper, to the best of the authors’ knowledge, the study contributes to the literature that the first time, they use the fuzzy alpha-cut AHP and GRA in fuzzy environment for energy source evaluation problem.

The purpose of this paper is to provide a good insight into the use of fuzzy Analytical Hierarchy Process (fuzzy AHP) approach that is a multi‐criteria decision‐making methodology in evaluating the benefits of information‐sharing decision problems.

In this study, the integration of AHP with the fuzzy synthetic extent analysis method (fuzzy AHP) is proposed in evaluating the benefits of information‐sharing decision problems as a framework to guide managers.

Findings demonstrate that the customer requirement and operational information alternatives are the preferred key decisions, which all supply chain partners might agree to share with one another. Further, it can also be concluded that the planning and financial information alternatives have almost the same importance.

Fuzzy AHP is a highly complex methodology and requires more numerical calculations in assessing composite priorities than the traditional AHP and hence it increases the effort. In addition, fuzzy methodology could be extended with the other multi‐criteria decision‐making (MCDM) methods such as Analytical Network Process (ANP), TOPSIS, ELECTRE and DEA techniques in solving such a problem.

There is a lack of research in the literature to deal directly with the uncertainty of human judgements in evaluating the benefits of various information‐sharing decisions in a supply chain. Therefore, fuzzy AHP is an appropriate methodology to select the various types of information and has the ability to be used as a decision‐making analysis tool since it handles uncertain and imprecise data. In addition, the paper is especially of interest to managers as they make decisions on which types of information they should share with their supply chain partners.

Contractor prequalification is a typical multiple criteria decision‐making problem that includes both quantitative and qualitative criteria. The conditions surrounding the prequalification decision are often imprecise, subjective and uncertain; assessments are consequently made using linguistic approximations. Fuzzy set theory is specifically designed to handle qualitative and linguistic data based on approximations and provides a method of representing in numerical form the linguistic approximations used to describe the decision‐maker judgments. However, fuzzy set theory has a weakness in identification of the relative weights of the decision criteria. On the other hand, one of the most accurate and easy methods for identifying the relative weights is the analytic hierarchy process (AHP). This paper seeks to address these issues.

The main objective of this paper is to develop a new integrated decision model composed of fuzzy set theory and analytic hierarchy process (AHP) methodologies approach that takes full advantages of the fuzzy set theory and the AHP. Two fuzzy approaches are considered, namely Chang's extent analysis and Jaskowski aggregated group decision analysis.

Both approaches are applied and validated on actual contractors in the Egyptian construction market. A software tool is developed to automate the calculations and a case study is provided.

This research produced a new integrated decision model composed of fuzzy‐AHP methodology approach that takes full advantages of the fuzzy set theory and AHP for tackling the uncertainty and imprecision of contractor prequalification during the prequalification stage, where the decision‐makers comparison judgments are represented as fuzzy triangular numbers. The default criteria used in this model had been collected through the literature review and experts’ opinion for building projects.

In this paper, two popular multiple-criteria decision-making (MCDM) methods with hesitant fuzzy logic approach; hesitant fuzzy analytic hierarchy process (hesitant F-AHP) and hesitant fuzzy the technique for order preference by similarity to ideal solution (HF-TOPSIS) are integrated as HF-AHP-TOPSIS to evaluating a set of enterprise resource planning (ERP) alternatives and rank them by weight to reach to the ultimate one that satisfies the needs and expectations of a company.

Selecting the best ERP software package among the rising number of the options in market has been a critical problem for most companies for a long time because of the reason that an improper ERP software package might lead to many issues (i.e. time loss, increased costs and a loss of market share). On the other hand, finding the best ERP alternative is a comprehensive MCDM problem in the presence of a set of alternatives and several potentially competing quantitative and qualitative criteria.

In this integrated approach, the hesitant F-AHP is used to determine the criteria weights, as the hesitant F-TOPSIS is utilized to rank ERP package alternatives. The proposed approach was also validated in a numerical example that has five ERP package alternatives and 12 criteria by three decision-makers in order to show its applicability to potential readers and practitioners.

If the number of the alternatives and criteria are dramatically increased beyond reasonable numbers, the reaching to final solution will be so difficult because of the great deal of fuzzy based calculations. Therefore, the number of criteria and alternatives should be at reasonable numbers.

The proposed approach was also validated in a illustrated example with the five ERP package options and 12 criteria by the three decision-makers in order to show its applicability to potential readers and practitioners.

Furthermore, in literature, to the best of our knowledge, the authors did not come cross any work that integrates the HF-AHP with the HF-TOPSIS for ERP software package selection problem.

The purpose of this paper is to provide an insight into the use of an integrated approach of fuzzy analytical hierarchy process (fuzzy AHP) and TOPSIS in evaluating the performance of global third party logistics service providers for effective supply chain management.

In this study, the integration of fuzzy AHP with TOPSIS is proposed in determining the relative importance (weight) of criteria and then ranking of 3PLs.

Findings show that the logistics cost and service quality are two most important criteria for performance rating of 3PLs. Deciding the relative importance of various criteria for 3PLs evaluation is a complex task. The superiority of one criterion over the other varies from person to person and firm to firm. Therefore, to capture the variability in decision fuzzy extended AHP is very useful tool. Finally, the preference raking of alternatives are found using TOPSIS.

Fuzzy AHP is a complex methodology and requires more numerical calculations than the traditional AHP and hence it increases the effort. But in this paper single stage fuzzy AHP is used to simplify the process. Fuzzy AHP is integrated with TOPSIS for preference ranking of 3PL, which provides a good methodology to rank 3PLs.

There is a lack of research in the literature to deal directly with the uncertainty of human decisions in evaluating the relative importance of multiple criteria. Therefore, fuzzy AHP is an appropriate methodology to find the relative importance of the criteria to rank the 3PLs using TOPSIS.

The purpose of this paper is to provide a methodology for assessing supply risk for a product category. The fuzzy based analytic hierarchy process (Fuzzy AHP) has been used for the purpose. The technique is used to determine the supply related risk and its potential impact on the buyer organization.

The research uses case analysis approach to understand the supply related risks and then applying fuzzy AHP for prioritization of the same.

The result of the case example indicates that the suggested approach of fuzzy AHP methodology seems feasible for risk assessment in supply chain management as it allows the personnel to express their judgments regarding the importance of each type of risk elements and evaluate the situation from their own perspective and then have this input coordinated in a quantitative fashion.

Supply chain managers can use this research as a base for developing risk assessment tool. The result can help as a benchmarking tool for the supply management professionals. By having a better understanding of which element has the greatest effect on risk, they can focus their attention on reducing the chance that risk can have on their organizations success and profitability.

This paper provides practitioners with a methodology for analyzing supply side risk as an objective of creating customer value by delivering perfect order.

The purpose of this paper is to develop an integrated closed loop performance management model for service industries.

The service performance of any organization is measured by considering qualitative and quantitative dimensions. The qualitative dimension includes the service quality factors. In order to measure the service quality precisely, fuzzy analytical hierarchy process (FAHP) has been employed in this paper. The data pertaining to both qualitative and quantitative dimensions are combined using extended Brown‐Gibson (EBG) model to measure the service performance. As an improvement process, fuzzy quality function deployment (FQFD) has been employed to redesign the existing services. A case study from automobile repair shops illustrates the usability of the model.

The developed model quantifies service performance and ensures the improvement of the service process. The proposed model takes into account the uncertainty that occurs while capturing the subjective assessment from customers and service engineers. The case study shows that the model can be used to gain service process understanding and identify significant factors for redesigning. Detailed results are presented.

The paper describes a novel method for service performance management. Fuzzy assessment of customers' feedback and service managers' feedback is much closer to human thinking than methods based on crisp numbers.