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In recent decades, many managers have conceived the radical role of knowledge in acquiring a competitive advantage and pursuing organizational strategic goals. Therefore, knowledge management is now a competitive requirement in organizations. However, if proper infrastructures and necessary prerequisites are not provided for executing such a process, the result will be nothing except failure and resource waste. Therefore, it is necessary that organizations identify effective factors for their readiness in executing knowledge management. The purpose of this study is identifying and prioritizing of effective constructs in readiness of knowledge management implementation by using fuzzy analytic hierarchy process (AHP).

In the present study, the relevant literature is initially reviewed and then seven factors and 19 affecting indicators on knowledge management readiness are identified by using a questionnaire for knowledge management elites which also constitute the research conceptual model. Finally, the weight of each factor is determined by fuzzy analytical hierarchy process and the readiness priority and preference of each factor are discussed.

The results of fuzzy analytic hierarchy process show that the management factor is recognized as the most effective factor in knowledge management execution readiness.

The model proposed in this study is applicable to the academic culture. Modification of the proposed model may be needed in applying this model to other environments.

As limited studies have been carried out on identifying and prioritizing of effective constructs in readiness of knowledge management implementation by using fuzzy analytic hierarchy process, the findings of this study will be useful in identifying and assessing the main components in readiness of knowledge management implementation in the research and training centers.

The purpose of this paper is to propose a disaster logistics center location selection decision support system, based on analytic hierarchy/fuzzy analytic hierarchy process methods, which will serve to fulfill the needs of disaster victims and rescue teams after a possible earthquake, and to implement the proposed systems for Istanbul.

Determining the appropriate location from among the possibilities by taking many sophisticated and inter‐related processes and parameters into consideration under stringent constraints is one of the keystones of logistics. Disaster logistics center location selections are extremely complex and vital. In this paper, analytic hierarchy/fuzzy analytic hierarchy process methods are used to compose a decision support system for determining the location of disaster logistics centers. The criteria and the weighting for the criteria that are used are determined via a questionnaire technique applied to specialists working in the Istanbul Center of Disaster Coordination.

Results gathered from the implementation of the proposed models to the chosen case illustrate that systems successfully determine the location, and both models point out the same result with different weights.

The paper introduces two disaster logistics center location selection decision support systems (fuzzy and crisp) and presents an empirical case study of the proposed models for Istanbul. The proposed model and outcomes from the application may shed light on future work, especially in the field of disaster logistics management.

The need for the thermal insulation of masonry buildings in Algeria is no longer debated. This paper aims to propose an integrated fuzzy multi-criteria decision aid method for the thermal insulation of masonry buildings to rank the thermal insulation solutions.

The proposed method combines the fuzzy analytical hierarchy process with the fuzzy preference ranking organization method for enrichment evaluation.

A case study using the proposed method is detailed in this paper. The building users’ preferences obtained by the fuzzy analytical hierarchy process had a higher level of consistency and accuracy. The case study demonstrates how in a highly uncertain field such as thermal insulation of masonry buildings, the fuzzy preference ranking organization method for enrichment evaluation can prevent the loss of valuable evaluation data and overcome difficulty in integrating linguistic assessments of the thermal insulation alternatives.

The proposed method extends current knowledge by using the fuzzy analytical hierarchy process to consider uncertainties regarding the building users’ preferences, and the fuzzy preference ranking organization method for enrichment evaluation to get a complete ranking of the thermal insulation solutions taking into account the uncertainties related to the alternatives’ evaluations.

One of the most important issues in supply chain (SC) management is the identification and management of the risk involved in it. The purpose of this paper is to propose a comprehensive model of supply chain risk management (SCRM) in the product life cycle (PLC) and the operational process cycle (OPC). To decrease the risks in a fuzzy environment, the model considers the organizational performance factors (OPF) and the risk operational practices (ROP).

Fuzzy analytic hierarchy process is used to determine the weights of the relationships between the PLC, OPC and OPF in the hierarchical structure of the decision problem. In addition, the fuzzy technique for order preference by similarity to ideal solution is employed to recognize the priority of ROPs in dealing with the performance factors. The integrated framework is evaluated using the case study of an automotive company in Iran.

The results demonstrated that the proposed model can be used to formulate an appropriate method for prioritizing defined alternatives to decrease risk and improve the organizational performance in SCRM under fuzzy conditions.

A major limitation of the study is that a few of the selected criteria for risk assessment are focused only on economic factors. Another limitation of the current study is related to the PLC, OPC and OPF being based on the work of Xia and Chen (2011).

The current study identified the more important stage in the PLC. More significant process in each stage of the PLC and weightier risk factors in each process of the OPC were determined. Some strategies for reducing risk in each stage of the PLC were presented. The best alternatives for reducing risks in SC were indicated.

It is worth mentioning that previous studies have not applied multiple criteria and alternatives to decrease the risks involved in the PLC and OPC parts of the SC under fuzzy conditions. However, it should be stated that some academics have used these techniques separately, in other specialized areas of the SC.

The purpose of this paper is to present a detailed literature review of the recent applications of the analytic hierarchy process (AHP) and analytic network process (ANP) group decision‐making methodologies.

Among more than 600 related papers published in the period 2005‐2009, a total of 232 application articles published in highly reputed international academic journals were selected and referenced in this paper. Papers were categorized according to application areas, subject titles, publication date, country of origin, academic journals, and integrated methodologies, and are summarized herein by various tables and charts.

The findings show that during the years 2005‐2009, use of the AHP technique has continued to increase exponentially. Moreover, it is expected that ANP will gain more popularity in the future, as the benefits of ANP become better understood. Applications of AHP have been dominant in manufacturing, followed by the environmental management and agriculture field, power and energy industry, transportation industry, construction industry, and healthcare. Other remarkable application fields include education, logistics, e‐business, IT, R&D, telecommunication industry, finance and banking, urban management, defense industry and military, government, marketing, tourism and leisure, archaeology, auditing, and the mining industry.

The study does not consider theoretical based AHP or ANP articles. Also the search excluded conference proceedings, masters' theses, and doctoral dissertations.

It is hoped that this study will guide practitioners in future work towards advancement of these techniques and will help the managers to select better decisions by making use of these methodologies.

The paper presents a comprehensive literature review of recent applications of AHP, and also ANP decision tools over the period 2005‐2009. Furthermore, the paper covers fuzzy AHP and fuzzy ANP extensions that are becoming popular methods in some application areas of traditional AHP and ANP.

This paper aims to describe an application of hybrid Multi Criteria Decision Making (MCDM) technique for the selection of wastewater treatment (WWT) technology for treating wastewater.

The proposed approach is based on Analytical Hierarchy Process (AHP) under fuzzy environment, Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) and hierarchy Grey Relation Analysis (GRA) techniques. Two models are proposed to evaluate the best WWT. The first model, Fuzzy Analytical Hierarchy Process (FAHP) is integrated with Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE) technique. The second model, FAHP is integrated with hierarchy Grey Relation Analysis (GRA) technique. The Fuzzy Analytical Hierarchy Process (FAHP) is used to determine the weights of criteria and then ranking of the WWT technology is determined by PROMETHEE and GRA.

An efficient pair‐wise comparison process and ranking of alternatives can be achieved for WWT technology selection through the integration of FAHP and PROMETHEE, FAHP and GRA.

The paper highlights a new insight into MCDM techniques to select an optimum WWT technology selection for the paper manufacturing industry.

With numerous and ambiguous sets of information and often conflicting requirements, construction management is a complex process involving much uncertainty. Decision makers may be challenged with satisfying multiple criteria using vague information. Fuzzy multi-criteria decision-making (FMCDM) provides an innovative approach for addressing complex problems featuring diverse decision makers’ interests, conflicting objectives and numerous but uncertain bits of information. FMCDM has therefore been widely applied in construction management. With the increase in information complexity, extensions of fuzzy set (FS) theory have been generated and adopted to improve its capacity to address this complexity. Examples include hesitant FSs (HFSs), intuitionistic FSs (IFSs) and type-2 FSs (T2FSs). This chapter introduces commonly used FMCDM methods, examines their applications in construction management and discusses trends in future research and application. The chapter first introduces the MCDM process as well as FS theory and its three main extensions, namely, HFSs, IFSs and T2FSs. The chapter then explores the linkage between FS theory and its extensions and MCDM approaches. In total, 17 FMCDM methods are reviewed and two FMCDM methods (i.e. T2FS-TOPSIS and T2FS-PROMETHEE) are further improved based on the literature. These 19 FMCDM methods with their corresponding applications in construction management are discussed in a systematic manner. This review and development of FS theory and its extensions should help both researchers and practitioners better understand and handle information uncertainty in complex decision problems.

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.

This paper aims to propose a new approach called “analytical hierarchy process weighted fuzzy linear programming model (AHP‐FLP)” for supplier selection.

A hybrid method of supplier selection, AHP‐FLP is applied to a real industry case. The weights of the various criteria, taken as local weights from a given judgment matrix, are calculated using analytical hierarchy process (AHP) that are also considered as the weights of the fuzzy linear programming model. This new model is compared with the classical AHP method.

This study concluded that the AHP‐FLP method outperforms the AHP method for supplier selection with respect to restricted supplier selection criteria. Drawing on a real case, Supplier 1 was identified to be the best supplier through the AHP model under no restrictions, which contradicts the finding that Supplier 2 was selected as the best supplier by the AHP‐FLP model subject to constraints.

More research is definitely called for within the context of studying a more complex supply chain with multiple supply network and nodes. There is also a crucial need for investigating other hybrid methods to find the optimum supplier.

The findings of this study indicate that the weights of supplier selection criteria calculated by the AHP‐FLP model are in line with the actual supplier selection decision of purchasing managers. Since the AHP‐FLP model is relatively more difficult to implement compared with the crisp AHP, its application will be more appropriate for high‐value components where stringent purchasing criteria are required. In contrast, AHP remains an appropriate approach for relatively lower value components (C class).

The novelty of this study lies in the application of a hybrid approach to a real industry case. This study has dealt with one of the most important subjects in supply chain management, providing a better decision for supplier selection using appropriate quantitative techniques.