Logistics centers are home to many and varied facilities, such as storage, transportation of goods, handling, reassembling, clearing, disassembling, quality control, social services and providing accommodation, so on. Providing logistical activities from one location can provide some macro advantages, as well as regional development in developing countries. For the micro level, logistics center selection has an effective role in increasing the operational efficiency and decreasing the costs of the firms. While the wrong location selection for logistics center affects the operations and costs of the companies negatively, the optimal location selection increases the performance, competitiveness, profitability of the firms and reduces the costs of the firms. Since many different qualitative and quantitative criteria are considered in the selection of the logistics center, this selection problem is an MCDM problem. A new integrated MCDM model is proposed to solve this problem for Sivas province in Turkey. This study presents two contributions to the literature. Firstly, the number of studies related to CoCoSo method is limited in the literature, therefore, the CoCoSo method is proposed in this study. Secondly, a new integrated GIS-based MCDM model comprising fuzzy SWARA and CoCoSo is introduced to literature to address the location selection problem for a logistics center. In this study, the results of CoCoSo method and the resulfts of other MCDM methods (COPRAS, VIKOR, ARAS, MOORA, and MABAC) are compared to test the accuracy of results obtained by CoCoSo. Besides, the criteria weights are changed and the possible changes in the results are tracked.

Until now, many different multiple criteria decision-making methods have been proposed for the facilitation of different business problems such as entrepreneurial. In this article, a new approach for selecting the most acceptable alternative is considered, which is based on the integration of weighted sum and weighted product approaches. The basic idea of the newly proposed approach is partly based on the multiobjective optimization by ratio analysis plus the full multiplicative form method, but also includes some features of the weighted aggregated sum product assessment and combined compromise solution methods. The proposed method is tested by using two illustrative examples that are related to the contractor and industrial robot selection. Additionally, to test its reliability, the obtained results are compared with the ones gained by using the multiple-criteria decision-making methods of the first and second generations. The proposed method predicts the application of a much simpler normalization procedure, involves four utility measures for defining the overall utility of alternatives, and enables a much easier ranking of them. The final conclusion is that the simple weighted sum product method is easy to use and contributes to the increasing of the reliability of decisions that are made. Therefore, the proposal uses a much simpler normalization procedure, i.e., four utility measures for determining the overall utility of alternatives, and allows a much easier ranking of alternatives.

Building insulation is the most respected among the compatible and effective energy conservation technologies available today, as it also reduces yearly energy costs and negative environmental effects. A building envelope is made up of various insulation materials that are important in determining a building’s thermal performance. Proper insulation material selection concludes in less energy requisition for operation. The purpose of this research is to supply information about natural fibre insulating materials used in construction insulation to maintain energy efficiency, as well as to recommend the most efficient natural fibre insulation material. As in most decision-making problems, several criteria and alternatives are involved in insulation material selection, too. Therefore, we used a novel integrated multi criteria decision making (MCDM) model including the preference selection index (PSI), method based on the removal effects of criteria (MEREC), logarithmic percentage change-driven objective weighting (LOPCOW), and multiple criteria ranking by alternative trace (MCRAT) methods to deal with the complexity of numerous criteria and alternatives. The contribution of this study is that a new hybrid MCDM method is developed. Additionally, the number of studies using the MCRAT method is very limited in the literature; therefore, this study will provide more insights into and results of this method to the literature.

The main goal of this paper is to propose a Multiple-Criteria Decision-Making (MCDM) approach that will facilitate decision-making in the field of logistics—i.e., in the selection of the optimal equipment for performing a logistics activity. For defining the objective weights of the criteria, the correlation coefficient and the standard deviation (CCSD method) are applied. Furthermore, for determining the semi-objective weights of the considered criteria, the indifference threshold-based attribute ratio analysis method (ITARA) is used. In this way, by combining these two methods, the weights of the criteria are determined with a higher degree of reliability. For the final ranking of the alternatives, the measurement of alternatives and ranking according to the compromise solution method (MARCOS) is utilized. For demonstrating the applicability of the proposed approach, an illustrative case study pointing to the selection of the best manual stacker for a small warehouse is performed. The final results are compared with the ones obtained using the other proved MCDM methods that confirmed the reliability and stability of the proposed approach. The proposed integrated approach shows itself as a suitable technique for applying in the process of logistics equipment selection, because it defines the most influential criteria and the optimal choice with regard to all of them in a relatively easy and comprehensive way. Additionally, conceiving the determination of the criteria with the combination of objective and semi-objective methods enables defining the objective weights concerning the attitudes of the involved decision-makers, which finally leads to more reliable results.

Nowadays, customers are not only interested in the quality of products, but they also want to have these products in a timely manner. The managers of an organization are faced with two problems when the distribution of products is in question, namely: (1) customers are usually geographically dispersed and (2) transportation should be performed in a cost-effective way. Although managers may have a significant experience and formal knowledge, decisions connected with the selection of an appropriate transportation company may very often be biased. For the purpose of avoiding making the inadequate decisions that might harm the operation of the organization, the application of a hybrid MCDM model is proposed in this paper. The proposed model consists of three fuzzy MCDM methods, including: the PIPRECIA, the PSI, and the CoCoSo methods. The fuzzy-PIPRECIA method is used to achieve the subjective weights of criteria, whereas the fuzzy-PSI method is used to obtain the objective weights of criteria. Fuzzy-CoCoSo is utilized to rank alternative transportation companies according to their performances. The possibilities of the proposed hybrid model are tested on a real case study pointed at the selection of an appropriate company for the transportation of ready-garments to retailers in Turkey.