Abstract
This paper addresses the multi-objective optimization for the road–rail intermodal routing problem that aims to minimize the total costs and carbon dioxide emissions of the routes. To achieve high timeliness of the entire transportation process, pickup and delivery services are simultaneously improved based on the employment of fuzzy soft time windows to measure their service levels. The modeling of road–rail intermodal routing considers fixed schedules of rail and time flexibility of road to match the real-world transportation scenario, in which travel times and carbon dioxide emission factors of road services are considered to be time-varying. To improve the feasibility of the routing, uncertainty of travel times and carbon dioxide emission factors of road services and capacities of rail services are incorporated into the problem. By applying trapezoidal fuzzy numbers to formulate the uncertainty, we propose a fuzzy multi-objective nonlinear optimization model for the routing problem that integrates the truck departure time planning for road services. After processing the model with fuzzy chance-constrained programming and linearization, we obtain an auxiliary equivalent crisp linear model and solve it by designing an interactive fuzzy programming approach with the Bounded Objective Function method. Based on an empirical case study, we demonstrate the validity of the proposed approach and discuss the effects of improving the confidence levels and service levels on the optimization results. The case analysis reveals several managerial insights that help to realize an efficient transportation organization by making effective trade-offs among lowering costs, reducing emissions, improving service levels, and enhancing feasibility.
Highlights
Intermodal transportation uses at least two transportation modes in a transportation chain to distribute goods within the same loading unit (usually a twenty-foot equivalent unit (TEU) container) from origins to destinations [1, 2]
(4) Truck departure time planning for road services is incorporated into the road–rail intermodal routing problem considering its potential in making trade-offs among lowering in-transit inventory costs, reducing carbon dioxide emissions, and improving service levels
This study investigates a green road–rail intermodal routing problem with improved pickup and delivery services under uncertainty
Summary
Intermodal transportation uses at least two transportation modes (e.g., water, air, road, and rail) in a transportation chain to distribute goods within the same loading unit (usually a twenty-foot equivalent unit (TEU) container) from origins to destinations [1, 2]. Combining multiple sources of uncertainty (e.g., time uncertainty and capacity uncertainty) may lead to the routing optimization that yields higher feasibility, Delbart et al [23] indicated that intermodal transportation planning under uncertainty is a research topic still having great potential to investigate. (3) Travel times of road services are modeled as a timevarying and uncertain parameter This uncertainty and capacity uncertainty of rail services are combined in the routing problem to improve the feasibility of the optimization and are addressed by fuzzy set theory and fuzzy programming. (4) Truck departure time planning for road services is incorporated into the road–rail intermodal routing problem considering its potential in making trade-offs among lowering in-transit inventory costs, reducing carbon dioxide emissions, and improving service levels. The conclusions of this study are drawn in “Conclusions”
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
