Kriterieviktning för optimerad lokalisering av distributionslagersplats/crossdock för LTL-transporter: AHP och TOPSIS mot COG

Abstract

Summary In modern supply chain management, the strategic placement of distribution warehouses plays a critical role in minimizing transport costs and environmental impact, especially in industries such as automotive manufacturing where supplier networks are complex and Just-In-Time logistics are central. This thesis explores how mathematical decision-making models can be used to optimized warehouse location, focusing on Less-Than-Truckload (LTL) transport scenarios involving multiple suppliers and one final destination. The study applies two Multi Criteria Decision Analysis (MCDA) methods, Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and compares them with the more traditional Center of Gravity (COG) method. A case study was conducted in collaboration with the automotive company Aurobay, using real-world supplier and transport data to evaluate potential warehouse locations. Through a combination of literature review and semi-structured expert interviews, four quantifiable and relevant criteria were identified and weighted using AHP: proximity to suppliers, proximity to logistic hubs, access to renewable energy and labor costs. These were then applied in a TOPSIS analysis for the case study, where six European cities were evaluated as potential warehouse locations. The findings in the study show that methods like AHP and TOPSIS provide a more extensive basis for decision-making by incorporating both economical and sustainability-related factors. In contrast, the COG method which only accounts for geographical location and weight, fails to reflect the full complexity of strategic logistics planning. The comparison reveals differences in recommended locations depending on the chosen method. The results emphasize that modern warehouse location decisions benefit from combining quantitative modeling with expert judgement. The presented approach is adaptable and can support companies in similar industries aiming for cost-efficient and environmentally conscious supply chain decisions. This Bachelor’s Thesis is written in Swedish.