A green intermodal service network design problem with travel time uncertainty. (November 2016)

• Record Type: Journal Article
• Authors: Demir, Emrah; Burgholzer, Wolfgang; Hrušovský, Martin; Arıkan, Emel; Jammernegg, Werner; Woensel, Tom Van
• Is Part Of: Transportation research. Volume 93:Part B(2016)
• Page Start: 789
• Page End: 807
• Publication Date: 2016-11
• Subjects: Stochastic service network design problem -- Intermodal transportation -- CO2-equivalent emissions -- Travel time uncertainty -- Demand uncertainty -- Sample average approximation method; Transportation -- Research -- Periodicals; Transportation -- Mathematical models -- Periodicals
• DOI: 10.1016/j.trb.2015.09.007
• Languages: English
• ISSNs: 0191-2615

Abstract

Highlights: A green intermodal service design problem with travel time uncertainty is introduced. A stochastic mathematical formulation using sample average approximation is developed. A real-life case study along with extensive computational study is presented. Abstract: In a more and more competitive and global world, freight transports have to overcome increasingly long distances while at the same time becoming more reliable. In addition, a raising awareness of the need for environmentally friendly solutions increases the importance of transportation modes other than road. Intermodal transportation, in that regard, allows for the combination of different modes in order to exploit their individual advantages. Intermodal transportation networks offer flexible, robust and environmentally friendly alternatives to transport high volumes of goods over long distances. In order to reflect these advantages, it is the challenge to develop models which both represent multiple modes and their characteristics (e.g., fixed-time schedules and routes) as well as the transhipment between these transportation modes. In this paper, we introduce a Green Intermodal Service Network Design Problem with Travel Time Uncertainty (GISND-TTU) for combined offline intermodal routing decisions of multiple commodities. The proposed stochastic approach allows for the generation of robust transportation plans according to different objectives (i.e., cost, time and greenhouse gas (GHG) emissions) by considering uncertainties in travel times as well as demands with the help of the sample average approximation method. The proposed methodology is applied to a real-world network, which shows the advantages of stochasticity in achieving robust transportation plans.