Multi-objective optimization for transportation mode selection: A case study in logistics

Selection of transportation mode (air, sea, road etc.) for sourcing goods is a critical decision in supply chain management because it directly affects cost efficiency, service flexibility and reliability. Freight cost and delivery lead time are two major decision criteria that dictates the selection process. Traditional methods often fail to account for the dynamic nature of the decision criteria in constrained logistics infrastructure. To address the challenges is logistics decision making, this study proposed a data driven multi-objective optimization (MOO) framework using Mixed-Integer Linear Programming (MILP) to minimize freight cost and lead time simultaneously. The study also developed a weighted sum approach to optimize the selection process. MATLAB intlinprog solver was utilized to solve the optimization problem and find the best route and sensitivity analysis was performed to check the model robustness and response to changes in the weight of decision criteria. A case study of 504 KG yarn shipment from India to Bangladesh was employed to validate the model effectiveness. The results highlighted that the road is the best option that minimizes freight cost and lead time for most of the cases for a given freight charges and lead time in this shipment route. The adoption of this model ensured tangible benefits for the company in terms of freight cost reduction. This optimization model could be an effective tool for flexible decision support system in logistics transportation system. • MILP with a weighted-sum objective minimizes cost and lead time, enabling strategic trade-offs. • From 504 kg India–Bangladesh data: road is best overall; air for urgency, sea for lowest cost. • Lambda (λ) sensitivity shows small weight changes can shift the optimal transport mode. • The model saved $50k+/year; scalable across routes and extendable to factors like emissions.