A green intermodal service network design problem with travel time uncertainty |
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Institution: | 1. School of Industrial Engineering, Operations, Planning, Accounting and Control (OPAC), Eindhoven University of Technology, Eindhoven, 5600 MB, The Netherlands;2. Institute for Production Management, WU Vienna University of Economics and Business, Vienna, Welthandelsplatz 1, Vienna 1020, Austria;1. University of Bologna, Department of Electrical, Electronic and Information Engineering, Viale Risorgimento 2, 40138 Bologna, Italy;2. Vrije Universiteit Amsterdam, FEWEB (Faculty of Economics and Business Administration), De Boelelaan, 1105 Amsterdam, Netherlands;1. School of Economics and Management, Tongji University, Shanghai 200092, China;2. Department of Marketing, Quantitative Analysis, and Business Law, Mississippi State University, Mississippi 39762, USA |
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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. |
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