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.

Renewable insulation materials are produced within the scope of this study using clay, fly ash, expanded perlite, epoxidized hemp oil, and hemp fiber. Density, thermal conductivity, and compressive‐tensile strength of the produced materials are analyzed. Economic and environmental analysis of the best sample with the most appropriate characteristics for an insulation material is conducted using a hybrid mathematical model developed for this study. Mathematical formulas of an economic evaluation technique of P1‐P2 method is integrated into Simulated Annealing Algorithm as one of the metaheuristic optimization approaches. Applicability of the proposed material for externally insulated walls is tested. For this purpose, optimum insulation thickness, payback period, energy savings for 10 years of lifetime, and CO2‐SO2 emissions were calculated for 5 types of energy sources along with a sensitivity analysis. This method is developed and coded in a software platform and used for the first time for the optimization of insulation material thickness. The experimental results obtained from evaluation of the sample H36 indicate that using this material for insulation purposes in the buildings have the potential of making significant contribution for energy efficiency along with various environmental benefits.

The performance effect of construction on energy conservation substantially depends upon application of correct materials and energy saving methodologies. A sizable financial impact is accomplished through insulated walls. The criteria explaining the present wall insulating material options may have different values. Furthermore, they may alter in different aspects, i.e. higher values of certain criteria show a preferable status, while for others they denote an inferior status. In this framework, a variant of compromise is needed, which can be situated through multi-criteria assessment methodologies. To diminish the effect of different methodologies on computational results, few diverse techniques can be considered, with descriptions of the mean predicted values. Thus, drawbacks of certain multi-criteria assessment techniques could be compensated through others. A hybrid methodology through the combination of individual techniques will be accurate if there is a relationship between the values determined through diverse methodologies. In this study, the most efficient insulation material used at external walls is selected by using PSI-CRITIC based CoCoSo Method. The analytical results are important both from financial and engineering point of views as the applied methodology is commercially viable and practically implementable. Precise and up-to-date material properties are derived from the leading companies in the sector.

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