Upstream Logistics Optimization from Shanghai, China to Kasur, Pakistan: An Implementation of Mixed-Integer Linear Programming

Our foremost purpose with this research is to specify a new method of freight optimization that shippers can utilize to satisfy their budget conditions while confronting the market’s necessities. This paper designs an optimization model for tactical planning that minimizes the cost of highway transportation operations. The findings and analysis of China and Pakistan’s shipping sectors shed light on the implications of cost trade-offs between multimodal and intermodal transportation. As this study is based on the ExWorks incoterms, we developed a mixed-integer linear programming (MILP) model to formulate the cost-minimization problem, bearing in mind the internal cost constraints of transportation. The Pareto optimal solutions are generated by a multi-objective genetic algorithm (MOGA) in the MATLAB optimization solver with the support of the Pareto fitness function to balance the costs of multimodal and intermodal transportation in delivering 40 ft container units from China to Pakistan. The solution to the problem demonstrates that the cost of multimodal freight transportation is higher than that of intermodal freight transportation. However, significant changes occur when rail carrying capacity is increased. The cost of intermodal transportation for a 40 ft container from Shanghai, China, to Kasur, Pakistan, is 4712 USD, while multimodal transportation costs 7119 USD. Additionally, this study can be expanded by examining transit times associated with multimodal and intermodal freight distribution for imports and exports.