Warehouses link suppliers and customers throughout the entire supply chain. The location of the warehouse has a significant impact on the logistics process. Even though all other warehouse activities are successful, if the product dispatched from the warehouse fails to meet the customer needs in time, the company may face with the risk of losing customers. This affects the performance of the whole supply chain therefore the choice of warehouse location is an important decision problem. This problem is a multi-criteria decision-making (MCDM) problem since it involves many criteria and alternatives in the selection process. This study proposes an integrated grey MCDM model including grey preference selection index (GPSI) and grey proximity indexed value (GPIV) to determine the most appropriate warehouse location for a supermarket. This study aims to make three contributions to the literature. PSI and PIV methods combined with grey theory will be introduced for the first time in the literature. In addition, GPSI and GPIV methods will be combined and used to select the best warehouse location. In this study, the performances of five warehouse location alternatives were assessed with twelve criteria. Location 4 is found as the best alternative in GPIV. The GPIV results were compared with other grey MCDM methods, and it was found that GPIV method is reliable. It has been determined from the sensitivity analysis that the change in criteria weights causes a change in the ranking of the locations therefore GPIV method was found to be sensitive to the change in criteria weights.
Multi-criteria decision-making methods (MCDM) represent a very powerful tool for making decisions in different areas. Making a rational and reliable decision, while respecting different factors, is a challenging and difficult task; MCDM models have a great impact on achieving this goal. In this paper, a new MCDM technique is presented—ranking alternatives by defining relations between the ideal and anti-ideal alternative (RADERIA), which was tested for the evaluation of human resources (HR) in a transportation company. The RADERIA model has three key advantages that recommend it for future use: (1) the RADERIA model has a new approach for data normalization that enables defining the normalization interval according to the judgments of a decision-maker; (2) an adaptive model for data normalization of the RADERIA model allows tough conversion into various forms of decreasing functions (linear, quadratic equation, etc.); and (3) the resistance of the RADERIA model to the rank reversal problem. Furthermore, in many simulations, the RADERIA method has shown stability when processing a larger number of datasets. This was also confirmed by a case study with 36 alternatives, as considered in this paper. The results and verification of the proposed new method were acquired through a comprehensive verification of the complexity of the results. The complexity of the results was executed through (1) comparison with four other multi-criteria methods, (2) checking the resistance of the RADERIA model to the rank reversal problem, and (3) the analysis of the impact of changes in the measurement scale on the ranking results.
Pedestrians as a vulnerable category of traffic participants demand a special attention, particularly regarding their behavior at unsignalized pedestrian crossings. Unquestionably, when crossing a road at these types of pedestrian crossings, there is a potential risk, for both the pedestrians and other traffic participants, as well. Accordingly, this article shows the research on pedestrians’ behavior at unsignalized intersections, conducted at four locations in the urban environment of Novi Sad. The main goals of this study are reflected in developing a multiphase model by integrating different approaches into one original unique model. First, the efficiency of the observed locations of pedestrian crossings was determined by applying a model consisting of DEA (Data Envelopment Analysis), fuzzy DEA, entropy, CRITIC (CRiteria Importance Through Intercriteria Correlation), fuzzy FUCOM (Full Consistency Method), fuzzy PIPRECIA (PIvot Pairwise RElative Import Criteria Assessment), and fuzzy MARCOS (Measurement of alternatives and ranking according to COmpromise solution). Then, the following aim of this study is to determine the values of the critical interval and then to compare these values with the accepted interval, which can be considered one of the criteria of safe pedestrians’ crossing the roadway. Apart from this, the aim is related to determining the characteristics of pedestrians’ behavior at unsignalized crossings, with a special reference to gender differences, as well to the fact whether the pedestrian crosses the roadway as an individual or within a group. After the empirical research and data classification, efficiency calculation, an extensive statistical and verification analysis was conducted to determine the set goals. The results imply that the relationship of the values of the accepted and critical intervals indicates the occurrence of the risky behavior of a certain number of pedestrians, which is reflected in accepting the intervals that are not completely safe for crossing the roadway and which can negatively affect the sustainable functioning of the traffic system.
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