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The purpose of this paper is to propose a novel weighting algorithm for fuzzy information axiom (IA) and to apply it to the evaluation process of 3D printers.

As a decision-making tool, IA method is presented to evaluate the performance of any design. Then, weighted IA methods are investigated and a new weighting procedure is introduced to the literature. Then, the existing axiomatic design methods and the proposed new method are classified into two groups: weighting based on information content and weighting based on design ranges. The weighting based on information content approach consists of four methods including pessimistic and optimistic approaches. The philosophy of the weighting based on design ranges is to narrow design ranges in order to decrease fuzziness in the model. To prove the robustness and the performance of the proposed weighting method, the results are compared with the existing methods in the literature. Then, the new approach is applied to evaluate 3D printers.

The results of the proposed study show that the proposed weighting algorithm has better performance than the old ones for IA. Therefore, the proposed weighting algorithm should be used for the weighting tool of IA thereafter.

An effective weighting method compatible with the philosophy of IA method has been proposed. Furthermore, the performances of 3D printers are compared by using the proposed method.

The companies are struggling to collect invoices due to the decrease in the economic growth. This global trend does not only affect undeveloped countries, but it also has a strong impact on the developed countries. Improving the debt collection process become a significant element to maintain financial stability. The institutions that are specialized on collecting payments, debt collection agencies and their call centers, with their expertise in the field can improve the payment process. Yet, managing evaluating the performance of debt collection agencies is a very hard process that involves uncertainty and imprecision. Performance measurement (PM) is a combination of numerically expressed characteristics which give insight about the success or degree of accomplishment of an activity. PM can be handled in various levels such as individual, team, department or company. The aim of this study is to present a systematic and objective PM method for call centers.

In this study, first an exploratory approach is used to understand the call center measurement problem. Several meetings are done with the representatives of both call center firms and the parent firms that outsource debt collection process. Simultaneously, a broad literature review is conducted. An iterative approach is selected to reach deeper knowledge on the process. New meetings are planned and scope of the literature review has changed based on this iterative approach. After these steps, the problem has been considered as the multi-criteria decision-making problem since more than one criteria should be considered for evaluating the performances of call centers. The result of the literature review and the meetings with experts show that defining the weights for the criteria is very crucial for evaluating the performances accurately. Collecting human judgment for defining the weights of call center criteria necessitates dealing with vagueness and uncertainty. The intuitionistic fuzzy sets excellent tools for representing uncertainty. Interval valued intuitionistic fuzzy sets can easily represent the human judgments. Thus, in this study, an intuitionistic fuzzy multi-criteria decision making approach is used to design the proposed methodology. Incomplete interval-valued intuitionistic preference relations are used to determine the weights of the indicators aggregating linguistic evaluations of the decision makers.

The proposed approach provides an objective calculation of performance measurement. In order to provide objectivity, indicator performance functions are proposed for the first time in this study. Nine different functions and related parameters are defined to objectively measure indicator performances.

The paper proposes an objective and easy-to-modify approach for call-center PM, which can be used by call center managers. It presents a new fuzzy multi-criteria decision-making (MCDM) method for call center performance evaluation, which can consider the multi-experts' judgments under vagueness and impreciseness, which may be conflicting and incomplete interval-valued intuitionistic fuzzy preference relations. Also nine new functions are defined for indicator performance.

To develop a multi‐attribute decision making model for evaluating and selecting among logistic information technologies.

First a multi‐attribute decision making model for logistic information technology evaluation and selection consisting of 4 main and 11 sub criteria is constructed, then a hierarchical fuzzy TOPSIS method is developed to solve the complex selection problem with vague and linguistic data. Sensitivity analysis is presented.

Reviews the literature and provides a structured hierarchical model for logistic information technology evaluation and selection based on the premise that the logistic information technology evaluation and selection problem can be viewed as a product of tangible benefits, intangible benefits, policy issues and resources. Defines tangible benefits as cost savings, increased revenue, and return on investment; intangible benefits as customer satisfaction, quality of information, multiple uses of information, and setting tone for future business; policy issues as risk and necessity level; resources as costs and completion time. Presents a methodology that is developed for the complex, uncertain and vague characteristics of the problem.

Comparisons with other multi‐attribute decision making techniques such as AHP, ELECTRE, PROMETHEE and ORESTE under fuzzy conditions can be done for further research.

This article is a very useful source of information both for logistic managers and stakeholders in making decisions about logistic information technology investments.

This paper addresses the logistic information technology evaluation and selection criteria for practitioners and proposes a new multi‐attribute decision making methodology, hierarchical fuzzy TOPSIS, for the problem.

The purpose of this paper is to show how intelligence techniques have been used in information management systems.

The results of a literature review on intelligence decision systems used in enterprise information management are analyzed. The intelligence techniques used in enterprise information management are briefly summarized.

Intelligence techniques are rapidly emerging as new tools in information management systems. Especially, intelligence techniques can be used to utilize the decision process of enterprises information management. These techniques can increase sensitiveness, flexibility and accuracy of information management systems. The hybrid systems that contain two or more intelligence techniques will be more used in the future.

The intelligence decision systems are briefly introduced and then a literature review is given to show how intelligence techniques have been used in information management systems.

The purpose of this paper is to discuss the applicability of investment decision‐making techniques under fuzziness.

The paper explains how fuzzy sets can be used in investment decision making.

It was found that any classical investment analysis technique can be converted easily to a fuzzy case.

The paper indicates the necessity for usage of the fuzzy set theory in case of incomplete information.