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Logistics has become an important field as the volume of world commerce expands. The World Bank (WB) has been publishing the Logistics Performance Index (LPI) for most of the countries since 2007. LPI is accepted as an important indicator of logistical performance. In this study, a model is proposed to evaluate the LPI of the OECD countries within a specific time frame. With the proposed model, the logistical performance of OECD countries between the years 2010-2018 is analyzed and compared with the existing LPI rankings. The index is calculated using six indicators. Different from the WB survey, the fuzzy analytical hierarchy method is used to determine the weighting scores of these six indicators. The grey numbers give the researcher an opportunity to obtain the numerical expressions of a time period by showing minimum and maximum values. Thus, grey additive ratio assessment (ARAS-G) method is used to evaluate the logistics performances of OECD countries by years. The data created in this study refers to the logistics performances of the OECD countries between the years 2010 and 2018. Thus, OECD countries are ranked according to the logistics performances calculated by the ARAS-G method. The rankings calculated by ARAS-G are compared to the yearly rankings calculated by the WB. Spearman ρ and Kendall's Tau correlation methods are used to investigate the relationships within the yearly rankings and the rankings calculated for the period between 2010 and 2018 by using ARAS-G. The results show that the rankings calculated by ARAS-G have the strongest relationship with years. Indeed, this study provides a different field of study for the ARAS-G method application.

In this study, it is aimed to rank the satisfaction levels of the municipality services. For this purpose, 20 municipal services included in the Life Satisfaction Survey (LSS) that the Turkish Statistical Institution regularly applies every year are considered as alternatives. In addition, the satisfaction of citizens was evaluated not only for the last year, but also for the period of 2014-2019, and these years were considered as a set of criteria. LSS statistics contains the citizens' responses which involve such opinion as abstain and refusal besides yes or no answers. For analyze the effect of all types of opinions on decision process, the participant responses constituting the dataset were converted into Picture Fuzzy Numbers (PFNs) consisting of 4 parameters (positive, neutral, negative, and refusal). Finally, we apply utilize VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) method by using PFNs arithmetic operators and evaluate the citizens’ satisfaction levels of the municipality services. As a result, it was determined that the municipal services with the highest satisfaction were graveyard (A18) and fire-fighting (A17) activities, while the services with the lowest satisfaction were zoning and city planning (A10) and control of food producing facilities (A20).

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Logistics villages are defined as a specific area of all the activities carried out by a variety of logistics-related businesses. They have specific features including size, distance to city center, accessibility, proximity to road/ airport/ railway/ maritime, office and IT infrastructure etc. Ranking logistic villages is a complicated task due to the fact that various criteria or objectives must be considered in the decision making process. Also in many real world cases the criteria are not equally important for the logistic managers and government authorities. In this study, we proposed a logistic village ranking model considering both Analytic Hierarchy Process (AHP) and VIKOR (Vise Kriterijumska Optimizacija I Kompromisno Resenje) methods. Subjective and objective opinions of logistic managers/experts turn into quantitative form with AHP. VIKOR technique is used for calculating the logistic villages' ranks. The aim of this paper is to rank the 11 logistic villages in