Abstract
This study explores an operational-level container routing problem in the road-rail multimodal service network. In response to the demand for an environmentally friendly transportation, we extend the problem into a green version by using both emission charging method and bi-objective optimization to optimize the CO2 emissions in the routing. Two uncertain factors, including capacity uncertainty of rail services and travel time uncertainty of road services, are formulated in order to improve the reliability of the routes. By using the triangular fuzzy numbers and time-dependent travel time to separately model the capacity uncertainty and travel time uncertainty, we establish a fuzzy chance-constrained mixed integer nonlinear programming model. A linearization-based exact solution strategy is designed, so that the problem can be effectively solved by any exact solution algorithm on any mathematical programming software. An empirical case is presented to demonstrate the feasibility of the proposed methods. In the case discussion, sensitivity analysis and bi-objective optimization analysis are used to find that the bi-objective optimization method is more effective than the emission charging method in lowering the CO2 emissions for the given case. Then, we combine sensitivity analysis and fuzzy simulation to identify the best confidence value in the fuzzy chance constraint. All the discussion will help decision makers to better organize the green multimodal transportation.
Highlights
With the rapid development of globalization, companies are seeking for specialized partners all over the world to outsource their businesses and for new markets to make more profit
Different uncertainty factors can influence the performance of multimodal transportation and should be considered already in the planning phase
All these factors increase the complexity of multimodal routing modelling and make the transportation planning very challenging in practice
Summary
With the rapid development of globalization, companies are seeking for specialized partners all over the world to outsource their businesses and for new markets to make more profit. Multimodal transportation combines different modes with different operational constraints that have to be represented in the mathematical model It can be distinguished between time-flexible modes (e.g., road) that can be used whenever they are needed and schedule-based modes (e.g., rail) that operate according to fixed schedules planned in advance. Traffic congestion results in the travel time uncertainty and delayed arrivals of the containers at the nodes, which disrupts the transshipment if the containers have to take another scheduled transportation service at the same node and possibly violates the due date constraint at the destination Congestion is another factor that should be considered in the decision-making process. (1) A mixed integer nonlinear optimization model for multimodal routing including multiple objectives (i.e., economic and environmental) and multiple uncertainty factors (i.e., road traffic congestion and rail service capacity) is defined.
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