In logistics, performance measurement has been considered as a key competency to acquire world class performance. In light of this, we presented a robust methodology to establish an analysis framework for measuring logistics performance. The proposed hybrid methodology is a combination of criteria importance through intercritera correlation (CRITIC), simple additive weighting (SAW), and Peters' fuzzy regression methods. To the best of our knowledge, country-based logistics performance is seldom studied in the literature. Therefore, we measured the logistics performance of Organization for Economic Cooperation and Development (OECD) countries using the devised model based on the data of Logistics Performance Index 2014 provided by the World Bank. The introduced methodology, which is suitable to model imprecise relationships among system parameters, appears to be a practical alternative approach for the assessment of logistics performance. It should be noted that the evaluation framework presented in this paper is not confined to performance measurement case and can also be exploited in addressing other multiple criteria decision-making problems incorporating uncertainty.

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Supplier selection is a crucial multi-criteria decision-making (MCDM) problem which requires a tradeoff between multiple criteria denoting vagueness and imprecision with the involvement of a group of experts. Fuzzy MCDM methods allow for modeling the decision processes involving imprecise and subjective information expressed as linguistic variables or fuzzy numbers by decision-makers. In this paper, considering information fusion between criteria and linguistic terms, a robust supplier selection algorithm based on the hybrid use of a modified fuzzy Analytic Hierarchical Process (FAHP) and generalized Choquet fuzzy integral (GCFI) methods is proposed. The FAHP is employed in order to obtain importance weights of the evaluation criteria and then the GCFI is used to achieve the overall performance values of supplier alternatives. The suggested methodology has been conducted in a real case application for supplier selection problem in a steelproducing company operating in Turkey. We also showed the robustness of the devised methodology through a sensitivity analysis and compared the obtained results with other MCDM methods. Consequently, it is concluded that the proposed integrated approach can effectively handle supplier selection and other MCDM problems especially when there are interdependent subcriteria in a complex hierarchy of evaluation criteria.

Selection of appropriate operating conditions is an important attribute to pay attention for in electrical discharge machining (EDM) of steel parts. The achievement of EDM process is affected by many input parameters; therefore, the computational relations between the output responses and controllable input parameters must be known. However, the proper selection of these parameters is a complex task and it is generally made with the help of sophisticated numerical models. This study investigates the capacity of Adaptive Nero-Fuzzy Inference System (ANFIS), genetic expression programming (GEP) and artificial neural networks (ANN) in the prediction of EDM performance parameters. The datasets used in modelling study were taken from experimental study. According to the results of estimating the parameters of all models in the comparison in terms of statistical performance is sufficient, but observed that ANFIS model is slightly better than the other models.

Purpose – In an effort to help policy makers develop competitive postal service strategies, the purpose of this paper is to evaluate the comparative operating efficiencies of postal services across the Organization for Economic Cooperation and Development (OECD) nations and then identify room for service improvement. Design/methodology/approach – As a better alternative to the conventional data envelopment analysis (DEA) which requires the proportional improvements of inputs and outputs simultaneously, the authors propose the combined use of both context-dependent and measure-specific DEAs to measure the relative attractiveness and progress of the national postal operators of OECD countries. Findings – Defying the conventional notion that public enterprises operate less efficiently than private enterprises, the author discovered that some state-owned public enterprises such as postal service operators could still be efficient if managed properly. Even inefficient postal services operators could significantly improve their service performances, once they identified the root causes of their service failures. Through a series of model experiments and testing, the authors found that proposed context-dependent and measure-specific DEA models were more useful for finding such causes than the conventional DEA model. Practical implications – For public officials and policy makers, the proposed DEAs can pinpoint what it takes to become more efficient and what steps need to be taken to improve postal service operations gradually. Originality/value – This paper is the first to combine the context-dependent DEA with measure-specific DEA to evaluate the comparative efficiency (or progress) and inefficiency (or regress) of the national postal operators of 25 OECD countries.

Due to the high uncertainty of business environment, the complexity and diversity of enterprise resource planning (ERP) projects and conflicting assessment criteria, appropriate ERP software selection can be viewed as a multi-criteria decisionmaking (MCDM) problem. Among the MCDM methods, extent analysis method (EAM) has been employed in many applications due to its computational simplicity. However, in EAM, inconsistency rises as the numbers of criteria or alternatives increase. In order to address this inconsistency problem, fuzzy linguistic preference relations (FLPR) has been proposed to obtain consistent solutions in the decision-making processes. This study proposes a robust ERP selection framework where the weights of the evaluation criteria are computed through FLPR and the ranking of the alternative ERP systems is obtained via Fuzzy TOPSIS. A real life application has been executed in a Turkish wholesale food company.

Several studies have addressed subcontractor selection problem t 2 o date. However, most of them considered subcontractor selection in construction industry and other fields have been disregarded. In order to fill this gap, this study focuses on selecting the best subcontractor under fuzzy environment for a Turkish textile company. The case problem is considered as a multiple criteria decision making (MCDM) problem and a hybrid methodology combining Fuzzy Preferences Programming (FPP) Method and Graph Theoretical Matrix Approach (GTMA) is proposed for the solution. In the application, the FPP is used to model the interrelations and interdependences among attributes and the GTMA is utilized to choose the best subcontractor for the company under consideration. The obtained results reveal that the devised methodology can successfully tackle with any type of MCDM problems as well as subcontractor selection.

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