An Integrated Location-Allocation Model for Temporary Disaster Debris Management under an Uncertain Environment

Purpose The hiring process for information technology (IT) personnel, given its high stakes and intricacies, demands an objective, methodical and nuanced approach. The multifaceted nature of IT roles necessitates a comprehensive evaluation methodology to identify and prioritize pertinent skills and competencies. Therefore, this study aims to devise and implement a robust multi-criteria decision-making model integrating the fuzzy analytic hierarchy process (AHP) and fuzzy technique for order preference by similarity to ideal solution (TOPSIS) for assessing and ranking the skills required for IT personnel. Design/methodology/approach This study integrates expert opinions and insights from the literature to identify five key criteria and 21 sub-criteria essential for IT personnel selection. The fuzzy Analytic Hierarchy Process (AHP) was applied to determine the relative importance of each criterion and sub-criterion. These weighted criteria were then utilized in the fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to systematically rank five candidate alternatives based on their suitability for IT roles. Findings Results identified “Objective of factors” to be the most significant criteria, whereas “Assessment Centre Score” as the most viable sub-criterion. Using the integrated model results of fuzzy AHP-TOPSIS candidate, A3 was the most fitted IT personnel, whereas Candidate A4 has emerged as the poor/ unsuitable fit. Practical implications This research helps firms and policymakers use their limited resources efficiently. Furthermore, this study acts as a guideline for future researchers to empirically investigate the impact of listed skills on employee performance. Originality/value This study is a pioneering effort in integrating fuzzy AHP and TOPSIS to address the challenges in IT personnel selection, catering to the intricate layers of human evaluative judgments. Its extended applicability and innovative approach makes it a valuable contribution to the existing body of knowledge, serving as a benchmark for future endeavors in related domains.

  Quantitative strategic planning matrix (QSPM) is an old and efficient method which is used for organizational strategic ranking. However, a new method of decision-making such as Fuzzy technique for order preference by similarity to ideal solution (TOPSIS) has attracted the attention of researchers and strategists. This is because of more reliance on the use of experts’ opinions and increase in the reliability of the results. With respect to this, this article attempts to submit a new combined approach of codifying and ranking strategies. In this article which was accomplished in Chooka Company in Iran as a case study, information was obtained with respect to analysis of effective internal and external factors on the analysis matrix of strengths, weaknesses, opportunities and threats points, and its causes of codifying organization strategies. Firstly, it prioritized using QSPM technique. In addition, strategies from pair-wise matrix with key indices, weighting, increasing reliability, the simultaneous qualitative and qualitative criteria and the selected strategies prioritized via Fuzzy TOPSIS method results proved that Fuzzy TOPSIS method has greater advantages than the QSPM technique. Therefore it is more suitable for organizational strategies.   Key words: Strategic planning, strengths and weaknesses and opportunities and threats (SWOT) matrix, quantitative strategic planning matrix (QSPM), Fuzzy technique for order preference by similarity to ideal solution (TOPSIS), analytic hierarchy process.

This paper develops a multiple criteria and multimethod approach for evaluating the suitability of alternative right-of-way (ROW) corridors to accommodate high-speed intercity passenger rail (HSIPR) operations. An alignment decision for HSIPR involves consideration of multiple criteria and trade-offs. Policy makers often face uncertainty in evaluating and selecting alternative options and usually consider alternatives in a fuzzy environment in which subjectivity and vagueness are present. This paper develops an integrated evaluation model that uses the fuzzy analytic hierarchy process (AHP) and fuzzy technique for order preference by similarity to ideal solution (TOPSIS) methods to address some of these issues. The study identifies potential sketch-planning performance metrics and demonstrates their usefulness and inclusion in the integrated fuzzy AHP–TOPSIS methodology in comparing alternative ROW corridor options. In contrast to detailed engineering evaluation, the developed sketch-planning metrics provide a cost- and time-effective way of assessing alternative suitability. A case study application with existing highway ROW in Texas demonstrates the applicability of the proposed framework. Apart from ranking alternatives, a detailed sensitivity analysis assesses the effect of performance metrics weights on the preferences between alternatives using a displacement index. The proposed framework creates a more effective and systematic decision support tool for preliminary corridor alternative evaluation.

An ANP based TOPSIS approach for Taiwanese service apartment location selection

This study combines analytic network process (ANP) with technique for order preference by similarity to ideal solution (TOPSIS) to assess the performance of Taiwanese tour guides. Interviews of practitioners and reviewing of studies are used to collect assessment criteria. Questionnaires based on 9 point Likert scale are sent to 48 senior tour guides to obtain their opinions about the importance of criteria. After discussions with 3 experts, the top 12 criteria, Communication, Interpretation, Emergency, Polite, Friendliness, Neat, Atmosphere, Help, Money, Caution, Conscientiousness and Honest are sorted into 3 perspectives: Ability, Customer and Firm, to structure the hierarchy for assessing the performance of Taiwanese tour guides. Considering the interdependence among criteria, ANP is used to obtain their weights, while TOPSIS is used to rank the tour guides. By integrating ANP and TOPSIS, this study can make better assessments of the performance of Taiwanese tour guides. Moreover, to illustrate how ANP and TOPSIS may be applied to real-world performance assessment, a case study of assessment is conducted. Key words: Analytic network process, technique for order preference by similarity to ideal solution, tour guide.

This paper presents a strategic roadmap to handle the issue of resource allocation among the green supply chain management (GSCM) practices. This complex issue for supply chain stakeholders highlights the need for the application of supply chain finance (SCF). This paper proposes the five Vs of big data (value, volume, velocity, variety, and veracity) as a platform for determining the role of GSCM practices in improving SCF implementation. The fuzzy analytic network process (ANP) was employed to prioritize the five Vs by their roles in SCF. The fuzzy technique for order preference by similarity to ideal solution (TOPSIS) was then applied to evaluate GSCM practices on the basis of the five Vs. In addition, interpretive structural modeling (ISM) was used to visualize the optimum implementation of the GSCM practices. The outcome is a hybrid self-assessment model that measures the environmental maturity of SCF by the coherent application of three multicriteria decision-making techniques. The development of the Basic Readiness Index (BRI), Relative Readiness Index (RRI), and Strategic Matrix Tool (SMT) creates the potential for further improvements through the integration of the RRI scores and ISM results. This hybrid model presents a practical tool for decision-makers.

Prioritization of equipment is an important factor for decision making to optimize maintenance management in Reliability Centered Maintenance (RCM). Many factors must be considered as part of the prioritization of equipment for maintenance activities. Consequently, evaluation procedures involve several objectives and it is often necessary to compromise among conflicting tangible and intangible factors. Multiple Criteria Decision Making (MCDM) is a useful approach to solve these problems. In this study, a hybrid model is developed for prioritizing the equipment in hybrid flow systems. The first stage involves identifying the criteria. The second stage is prioritizing the different criteria using fuzzy Analytical Network Process (ANP), in which the weight of each criterion is calculated using modified fuzzy Logarithmic Least Square Method (LLSM) to overcome the criticism of inconsistency, unbalanced scale of judgments, uncertainty and imprecision in the pair-wise comparison process, then finally ranking of equipment using fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS).

Manufacturing organisations are witnessing a transformation in the manufacturing paradigm due to the increasing competition. Agile manufacturing (AM) is an operations concept that is intended to improve the com- petitiveness of firms. When market conditions are unfavourable, a firm needs to stay competitive in order to function well and remain in good health. In such situ- ations, it becomes essential that an organisation optimises its manufacturing processes so that it would adapt to changes in an unpredictable market scenario and remain competitive. AM principles enable an organisation to sus- tain in the competitive market scenario. Concept selection for an AM system is a typical multi-criteria decision mak- ing (MCDM) problem. In order to enhance the effective- ness of concept selection, a unique combination of fuzzy decision making trial and evaluation laboratory (DEMATEL), fuzzy analytical network process (ANP) and fuzzy technique for order preference by similarity to ideal solution (TOPSIS) was used in the study. The study is aimed at selecting the best concept design of an auto- mobile component. The selected design was subjected to implementation in the case organisation.

Employee selection is a multi-criteria decision-making (MCDM) problem for selecting suitable applicants from a ready pool. The selection aims to make use of their knowledge, relevant skills, and other characteristics to perform a specific job. The aim of this study is to develop a systematic approach for selecting the best candidates among the air traffic controllers (ATCs) for aviation in Saudi Arabia. Three integrated methods were employed for decision-making in this study. First, a fuzzy decision tree was applied to determine the criteria weights, then the fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed to rank the attributes. In the last step, the Data Envelopment Analysis (DEA) was used to transform the qualitative variables into quantitative equivalences. A survey was conducted by national and international decision- makers to elicit the necessary information on the criteria and sub-criteria of the air traffic control system. The decision problem was formulated by employing five criteria and ten applicants. The relationship between the fuzzy TOPSIS and fuzzy-weighted average was very positive for decision-making. The outcomes of the fuzzy TOPSIS and DEA encouraged the development of a decision support system for the selection of ATCs.

In the classical multiple attribute decision-making or MADM methods, the ratings and the weights of the criteria are known precisely. However, in eco-material selection exercises, the available data are typically inadequate because of the selection dual quantitative and qualitative natures. Some of the qualitative selection criteria can be rated in several classes rather being expressed by exact numerical values; hence the application of fuzzy concepts in decision-making seems attractive to deal with such kind of ratings. Thusly, the presented study attempts to propose an eco-material selection approach specific to the automobile body panels using a fuzzy technique for order preference by similarity to ideal solution (TOPSIS), to incorporate both numerical and rating-based criteria into one holistic sustainability model. TOPSIS and fuzzy logic can aid the material selection process in translating the design goals and parameters into usable numbers that in turn can be used to rank candidate materials in their closeness to the ideal solution. An additional uniqueness of this study stems from using the fuzzy-TOPSIS as a scoring tool without any assigned weights for the different selection attributes, in order to avoid the bias that is typically associated with other classical MADM, such as quality function deployment, analytical hierarchy process and digital logic.

Purpose – The purpose of this paper is to present a hybrid intuitionistic fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method, called intuitionistic fuzzy ordered weighted averaging weighted averaging (OWAWA) distance TOPSIS (IFOWAWAD-TOPSIS) method for intuitionistic fuzzy multiple-criteria decision making (MCDM) problems. Design/methodology/approach – Based on the OWAWA operator, the authors develop the intuitionistic fuzzy OWAWA distance measure, then the IFOWAWAD-TOPSIS method is obtained by using the IFOWAWAD and traditional TOPSIS. Findings – The developed IFOWAWAD-TOPSIS method can overcome the drawback of traditional TOPSIS method that cannot consider both the subjective information of attributes and the attitudinal character of decision maker. Research limitations/implications – Clearly, this paper is devoted to the OWA operator, MCDM and intuitionistic fuzzy theory. Practical implications – The developed method is applicable in a wide range of situations such as decision-making, statistics, engineering and economics. A numerical example concerning investment selection is given to illustrate the practicability and usefulness of the proposed approach. Originality/value – This paper fulfils an identified need to study how to make a decision considering both the subjective information of attribute and the attitudinal character of decision maker in intuitionistic fuzzy environment.

PurposeThe objective of this paper is to present the employment of the new hierarchical fuzzy technique for order preference by similarity to ideal solution (TOPSIS) approach to evaluate the most suitable business process outsourcing (BPO) decision.Design/methodology/approachThe paper explains the importance of selection criteria for evaluation of BPO. It then describes briefly the fuzzy hierarchical TOPSIS methodology. There then follows a discussion of the application of the fuzzy hierarchical TOPSIS with some sensitivity analysis to the BPO evaluation problem. Finally, some concluding remarks and perspectives are offered.FindingsUse of the hierarchical fuzzy TOPSIS methodology offers a number of benefits. It is a more systematic method than the other fuzzy multi‐criteria decision‐making (FMCDM) methods and it is more capable of capturing a human's appraisal of ambiguity when complex multi‐criteria decision‐making problems are considered. The hierarchical fuzzy TOPSIS is superior to the other FMCDM methods, such as fuzzy analytic hierarchy process (FAHP) and classical fuzzy TOPSIS methods, since the hierarchical structure without making pairwise comparisons among criteria, sub‐criteria, and alternatives is considered. Hierarchical fuzzy TOPSIS is an excellent tool to handle qualitative assessments about BPO evaluation problems, and its calculations are faster than FAHP. Also, hierarchical fuzzy TOPSIS makes it possible to take into account the hierarchical structure in the evaluation model. However, there are drawbacks. The classical fuzzy TOPSIS is a highly complex methodology and requires more numerical calculations in assessing the ranking order of the alternatives than the hierarchical fuzzy TOPSIS methodology and hence it increases the effort, thus limiting its applicability to real world problems.Originality/valueThe proposed model will be very useful to managers in the manufacturing sector, as this method makes decision making easier, systematic, efficient and effective.

Selection of the suitable material handling equipment (MHE) is a very difficult task for the manufacturing companies because of the considerable capital investment required. There are many tangible and intangible factors for choosing the suitable MHE. Multiple criteria decision making (MCDM) has been found to be a useful approach to analyze these conflicting factors. The evaluation of MHE alternatives within the frame of various subjective criteria and the weights of the criteria are usually expressed in linguistic terms. This makes fuzzy logic a more natural approach to this kind of problems. This paper proposes a combined MCDM methodology for evaluation and selection of MHE types for a company in the steel construction industry in Istanbul, Turkey. Fuzzy analytic network process (FANP) is utilized for assigning weights of the criteria for MHE selection and fuzzy technique for order preference by similarity to ideal solution (TOPSIS) is used to determine the most proper system alternative using the criteria weights attained by FANP. The selection is based on the compatibility between MHE and production characteristics. Objective is to select the most efficient MHE considering also the cost efficiency. The study was followed by the sensitivity analyses of the results.

The Purpose research is to develop a new interval value fuzzy by integrating Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for multiple criteria group decision-making model (FMCGDM). The Interval Value Analytic Hierarchy Process (IVFAHP) approach is capable of elaborating complex multi-criteria problems into a hierarchical structure and determining the optimal weight in multi-criteria decision-making. In determining the best alternative, the Interval Value Fuzzy Technique for Order Preference by Similarity to Ideal Solution (IVFTOPSIS) approach is well-suited for being able to make multi-decisions based on the alternative that has the shortest distance from the ideal solution positive and furthest from the ideal negative solution. The results study is a new framework Interval Value Fuzzy (AIVF) decision-making model with flexible linguistic preferences and a high degree of accuracy. The method is implemented in mapping strategy selection problem for measurement e-learning.

In decision making science, an important aspect is to select one strategy from available ones and to prioritize. Multi criteria decision making methods, especially fuzzy MCDM have made their way in to this field for several years. Among them, analytical hierarchy process (AHP) method and technique for order preference by similarity to ideal solution (TOPSIS) have been employed more than other technique and methods have. Function productivity is among many different factors. In the climate of decision making to increase productivity. Therefore, raised question is that on which factor and how much we should put emphasis. This study tries to answer this question using MCDM models. For this reason, after primary data was collected with identification questionnaire and effective factors were categorized by using statistical analysis done with SPSS software, a primary refinement was carried out on factors and criteria. Next these factors are ranked by analytical hierarchy process (AHP), fuzzy technique for order preference by similarity to ideal solution (FUZZY TOPSIS), and Fuzzy AHP methods. Which are among the most important multi criteria techniques?Given that the results from above methods.In some cases, are not in agreement with each other, combined POSET technique was used reach consensus on ranking criteria. Finally disagreement between the results was examined by using freedman’s statistical test and spearman’s correlation coefficient. With regard to the results of this study, a combined ranking method, taking ranking means, was employed to make decisions on prioritizing productivity objectives of west region power corporation. Since it was impossible to choose optimal rankingmethod from fuzzy and non fuzzy methods. Eventually, important criteria in making policy on human force productivity were identified from management factors. Human force and customers, with management factors being the most important ones separated by management information system index.