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Despite being a low-tech industry, woodwork manufacturing industry that includes furniture and cabinet making, witnessed technological leaps in production technologies due to technical developments in computer numerical control (CNC) machining processes. The managers of this industry have attached high importance to the selection of efficient machines as their decisions directly affect the quality and performance of products produced by the firms. Improper selection process can result in a significant decrease in productivity and flexibility. Therefore, a systematic decision-making procedure is needed to prevent inaccurate investments on machines. The purpose of this paper is to purpose a hesitant fuzzy analytic hierarchy process (HFAHP) based multi-criteria decision making (MCDM) system for CNC router selection in small- and medium-sized enterprises (SMEs) in woodwork manufacturing.

The study proposes a hierarchical model consisting of 4 main criteria and 11sub-criteria for woodwork manufacturing. Technical, personnel, economic and vendor aspects constitute the main criteria. Because of the hierarchical structure of the model, HFAHP is utilized to define the importance weights of the criteria, and to select the most appropriate CNC alternative for a manufacturing company under focus. In a selection procedure, the judgments of decision makers may have vagueness to specify the importance of criteria affecting the decision process. In the literature, the fuzzy set theory has been utilized to deal with such uncertainties. However, when the ideas of the managers have high potential to fall into contradiction in pairwise comparisons, a novel approach is needed to overcome the obstacles. HFAHP allows the membership degree having a set of possible values. It is specifically useful in compromised decisions where experts cannot agree on a single value and prefer to come up with an interval of linguistic variables.

It is revealed that for SMEs in woodwork manufacturing, the most important criterion in selecting the CNC routers is the technical aspects. It may seem counter intuitive that they do not refrain finding the technical criteria superior to the economic aspects, even though they have limited budgets compared to large-scale firms. This demonstrates that in current competitive environment, SMEs understand the need for high-quality production strategy. The weights of the remaining two criteria (personnel and vendor aspects) are relatively low because they expect that they can easily overcome the problem of adapting the workers by training, and all vendors have quality standard qualifications so they can offer a satisfactory service and supplementary systems.

The ready-to-use model proposed is specialized for SMEs in woodwork manufacturing. However, to make it an easily adaptable model for every company in the woodwork industry regardless of its size, the calculation process of the priority weights is illustrated in detail with a numerical example. Any company can follow the process using their own preferences to end up with a specific model that will perfectly reflect their own specific priorities. For demonstrating the application of the model, a case study is conducted in a woodwork manufacturing SME to select the best CNC router among three alternatives.

The originality and value of the paper is twofold. First, to the best of our knowledge, this is the first study that proposes a woodworking-specific CNC router selection for SMEs. Second, to handle the high uncertainty in the judgements, and to facilitate consensus among the experts during face to face meetings to develop compromised matrices, a very recently developed method, HFAHP is used.

ERP selection is a multi-faceted process and needs to be successful in dealing with high uncertainty. The purpose of this paper is to propose a novel multi-criteria decision making (MCDM) approach, titled as cloud-based design optimization (CBDO), for ERP selection problem to handle high uncertainty with a computationally effective way.

CBDO has been utilized as an alternative method to fuzzy set theory and stochastic programming, and proposes robust findings for worst case scenario. In order to assess the proposed methodology, a numerical study is conducted by taking into account existing state-of-the-art study on the ERP selection problem for the small medium enterprises. The outputs of the existing state-of-the-art study are assumed as uncertain and varying across time as it is expected in real life; therefore, different scenarios are created in order to reveal the effect of uncertainty on decisions.

In the methodology, the results given under uncertain conditions are compared with the results obtained under stable conditions. It is clearly seen that ERP system selection problem area has high sensitivity to the uncertain environment, and decision makers should not undervalue the unsteadiness of criteria during the ERP system selection process, especially within volatile economies.

This study contributes to the relevant literature by utilizing CBDO as a MCDM tool in the selection of the ERP software as a first time, and validating the impact of unsteadiness on the ERP selection procedure. It is the first CBDO-based study that validates the effect of distributional differences on uncertainties in the ERP selection processes.

The purpose of this paper is to develop a systematic and comprehensive project selection model utilizing fuzzy multi-objective linear programming (FMOLP) that deals with the imprecise data in IS projects and uncertain judgment of decision makers.

First, projects are prioritized by considering both quantitative and qualitative factors. A fuzzy analytical hierarchical process (FAHP) is used in order to obtain weights of each project that indicates their priorities. At the second step, project selection decision is completed by using FMOLP. Then, the sensitivity analysis is performed to evaluate the robustness of the proposed integrated model.

The result of this study indicates that an integrated approach utilizing FAHP and FMOLP can be used as a supportive tool for project selection in IS context. It decreases the uncertainty caused from uncertain judgment of decision makers.

Future studies are suggested to design models having fuzzy constraints such as budget and resources. Moreover, for future studies, non-linear membership functions can be used.

Actual projects are provided from the Turkish IS company for prioritizing process and a hypothetical mathematical model is demonstrated using illustrative data.

This study contributes to the relevant literature by proposing a comprehensive model considering many conflicting ideas of decision makers on quantitative and qualitative criteria, and evaluating projects in an integrated way including FAHP and FMOLP.

The purpose of this study is to develop a fuzzy expert system to design robust forecast of return quantity in order to handle uncertainties from the return process in reverse logistic network.

The most important factors which have impact on return of products are defined. Then the factors which have collinearity with others are eliminated by using dimension redundancy analysis. By training data of selected factors with fuzzy expert system, the return amounts of alternative cities are forecasted.

The performance metrics of the proposed model are found as satisfactory. That means the result of this study indicates that fuzzy expert systems can be used as a supportive tool for forecasting return quantity of alternative areas.

In the future, the proposed model can be used for forecasting other uncertain parameters such as return quality and return time. Other fuzzy systems such as type-2 fuzzy sets can be used, or other expert systems such as artificial neural networks can be integrated into fuzzy systems.

An application at an e-recycling facility is conducted for clarifying how the method is used in a real decision process.

It is the first study which aims to model an alternative forecasting by utilizing fuzzy expert system. Furthermore, a comprehensive factor list which includes predictors of the system is defined. Then, a dimension redundancy analysis is developed to reveal factors having significant impact on the return process and eliminate the rest.

Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems use fuzzy logic to handle uncertainties generated by imprecise, incomplete and/or vague information. The purpose of this paper is to present a comprehensive review of the methods and applications in fuzzy expert systems.

The authors have carefully reviewed 281 journal publications and 149 conference proceedings published over the past 37 years since 1982. The authors grouped the journal publications and conference proceedings separately accordingly to the methods, application domains, tools and inference systems.

The authors have synthesized the findings and proposed useful suggestions for future research directions. The authors show that the most common use of fuzzy expert systems is in the medical field.

Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods. In this study, the authors present a comprehensive review of the methods and applications in fuzzy expert systems which could be useful for practicing managers developing expert systems under uncertainty.