| Multimodal transportation has become increasingly important and dominating in the freight transportation industry. Sustainability concerns and availability of eco-friendly transportation modes, such as high-speed rail, have called for an investigation on environmental impacts of multimodal operations. Motivated by a real-life business case, this research studies multimodal transportation problems with operational constraints and environmental considerations. Specifically, a mathematical model is developed, and a heuristic algorithm is proposed to solve the problem effectively and efficiently. Environmental and financial impacts are further studied for several model configurations.;First, we study an operations scheduling problem in a multimodal transportation network, subject to shipping capacity limits, resource availability, transshipment delays, customer service requirements, and environmental concerns. The objective is to minimize the total shipping costs and penalty costs due to delivery delays. To this end, we model the problem as a mixed integer linear program (MILP), which determines the routing of customer orders, the transportation mode to use on each route segment, and the corresponding departure time of the selected mode.;Second, in view of the problem's large scale and computational complexity, we propose a Lagrangian relaxation model, and decompose the original MILP model into smaller-size subproblems. These subproblems are independent to each other, and hence can be solved in parallel to significantly speed up the computation. A sub-gradient heuristic is developed to solve the Lagrangian model by effectively searching for bounds and feasible solutions. We tested the proposed heuristic on 28 different problem settings, each with 30 randomly generated test cases. The results show that the proposed heuristic is effective in finding near-optimal solutions for small to medium sized problems benchmarked by the Gurobi MILP solver, and for large-scale problems, the heuristic outperforms Gurobi in both solution time and quality.;Finally, based on the aforementioned model and its variants, we perform simulation analysis and quantify the financial and environmental impacts of four scenarios. (1) We quantify the environmental benefits of multimodal transportation, compared to truck-only transportation. (2) We quantify the impacts on carbon emissions by varying usage of highspeed rail. (3) We investigate the financial and environmental impacts on logistics companies by imposing carbon emissions quota as an operational constraint. (4) For shipping capacities, the logistics company may use a pay-per-use scheme or a fixed-volume subscription. We compare two models and their resulting impacts on firm's operational costs and carbon emissions. Based on the numerical results of these analysis, we provide insights and suggestions on economic and environmental considerations for the future of multimodal transportation. |
