Abstract
We explore a freight routing problem wherein the aim is to assign optimal routes to move commodities through a multimodal transportation network. This problem belongs to the operational level of service network planning. The following formulation characteristics will be comprehensively considered: (1) multicommodity flow routing; (2) a capacitated multimodal transportation network with schedule-based rail services and time-flexible road services; (3) carbon dioxide emissions consideration; and (4) a generalized costs optimum oriented to customer demands. The specific planning of freight routing is thus defined as a capacitated time-sensitive multicommodity multimodal generalized shortest path problem. To solve this problem systematically, we first establish a node-arc-based mixed integer nonlinear programming model that combines the above formulation characteristics in a comprehensive manner. Then, we develop a linearization method to transform the proposed model into a linear one. Finally, a computational experiment from the Chinese inland container export business is presented to demonstrate the feasibility of the model and linearization method. The computational results indicate that implementing the proposed model and linearization method in the mathematical programming software Lingo can effectively solve the large-scale practical multicommodity multimodal transportation routing problem.
Highlights
We explore a freight routing problem wherein the aim is to assign optimal routes to move commodities through a multimodal transportation network
Multimodal transportation utilizes more than one transportation service on the routes that serve to move commodities from their origins to their destinations [1,2,3]
Note that the freight routing problem that this study focuses on is extremely similar to the multicommodity multimodal transportation network design problem
Summary
Chang [42] addressed the problem of selecting the best routes to move commodities through international multimodal transportation networks In his study, he considered multiobjective optimization and schedule-based transportation services and demanded delivery times and transportation economies of scale, and he formulated the route selection problem as a multiobjective multimodal multicommodity flow problem with time windows and concave costs. The authors proposed two mixed integer programming models and valid inequalities to solve the multicommodity routing problem in a truck-ocean transportation network where truck services were considered to be time flexible and uncapacitated, whereas maritime services were capacitated and operated according to schedules. Both of these problems relate to planning optimal routes to move multiple commodities through the transportation network by using multiple transportation services rationally They show obvious similarities in model formulation and solution algorithm design.
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