Warehouse site selection for the automotive industry using a fermatean fuzzy-based decision-making approach

Abstract

The automotive industry is one of the most competitive sectors, and it requires a well-structured logistics system to meet the industry' vital requirements such as just-in-time, lean and agile supply chain operations, productivity and sustainability. Well-located and well-designed warehouses can make reaching these aims for the automotive industry possible and more accessible. Hence, determining a location for a warehouse is a highly critical, tactical, and managerial resolution for the automotive industry, as there is a strong correlation between well-located warehouses and the well-structured logistics network in the automotive industry. Although the WSS is a significant decision-making problem, we observed four critical and severe gaps in the existing literature: (1) the authors preferred to apply traditional objective & subjective frames, and they overlooked existing highly complicated uncertainties. (2) The number of studies focusing on the WSS problem in the automotive industry is surprisingly scarce. (3) It is not sufficiently clear how these factors used in the previous studies were determined, which causes doubts about their reliability. (4) there is no satisfactory evidence of which approaches were used to identify the factors in the previous papers. By considering these gaps, we propose two approaches which can be accepted as a novelty of the paper. First is the extension of the Delphi techniques based on the Fermetean fuzzy sets (FFs) used for identifying the criteria. It also combines the two traditional approaches (i.e., literature review and professionals' evaluations to identify the criteria) with the FF-Delphi technique. The second is the Double Normalized MARCOS approach based on FFs (FF- DN MARCOS) implemented to identify the weights of the criteria and ranking performance of the alternatives. The proposed model was implemented to identify the best warehouse location for the automotive manufacturing company. The results show that the C1 “energy availability & cost” criterion is the most influential criterion and the C5 proximity to port and customs criterion is the second most crucial factor. Then we executed a comprehensive sensitivity analysis, and the results approved the suggested model's validity and robustness despite excessive modifications in the criteria weights. © 2022 Elsevier Ltd