Multiagent Modelling and Simulation of a Physical Internet Enabled Rail-Road Intermodal Transport System

Abstract

Simulation-based analysis has been used for planning, control, and decision-making support of physical internet enabled logistics networks. However, multiagent modelling and simulation based on micro-level interactions have been rarely developed for the pre-studies of digital transformation of urban rail transit systems. This hinders a wider industrial deployment of agent technology in the physical internet enabled transport infrastructure. To fill in this knowledge gap, this work presents an agent-based simulation that explicitly models the micro-level protocols of mobile recourse units and their interaction with the physical infrastructure in a rail-road intermodal transport network. Parameterisation of the simulation model is changeable to examine the influences of different efficiency factors. This allows understanding of which structural functions and resource configuration would make an impact system-wide. Through a practical application, a multiagent system is developed for modelling and analysis of sustainable logistics with individually operated mobile resource units. An agent-based simulation assessment is performed to quantify the improvement options. The results reveal that the physical internet can prevent trucks from empty driving, which has a positive effect on the sustainable logistics operations. The proposed model can be used to support the deployment and planning of digital transformation that could be implemented in urban rail transit systems serving urban distribution and passenger transport.

Keywords

Agent-based simulation / Rail-road intermodal transport / Physical internet

References

[1] Carrara S, Thomas L. Freight futures: the potential impact of road freight on climate policy. Transp Res Part D Transp Environ, 2016, 55: 359-372

[2] Transportation Research Board of the National Academies (2014) The role of freight transportation in economic competitiveness. http://onlinepubs.trb.org/onlinepubs/conferences/2014/UTCSpotlight/Program.pdf. Accessed 1 July 2017

[3] World Economic Forum (2009) Supply chain decarbonization. http://www3.weforum.org/docs/WEF_LT_SupplyChainDecarbonization_Report_2009.pdf. Accessed 1 July 2017

[4] McKinnon A (2009) Innovation in road freight transport: achievements and challenges. In: International conference on innovation in road transport: opportunities for improving efficiency. https://trid.trb.org/view.aspx?id=1149671. Accessed 1 June 2017

[5] Caris A, Macharis C, Janssens GK. Network analysis of container barge transport in the port of Antwerp by means of simulation. J Transp Geogr, 2011, 19: 125-133

[6] Sharav N, Bekhor S, Shiftan Y. Network analysis of the Tel Aviv mass transit plan. Urban Rail Transit, 2018, 4: 23-34

[7] Aklilu A, Necha T. Analysis of the spatial accessibility of Addis Ababa’s Light Rail Transit: the case of East-West Corridor. Urban Rail Transit, 2018, 4: 35-48

[8] Khattak A, Jiang Y, Hu L, Zhu J. Width design of urban rail transit station walkway: a novel simulation-based optimization approach. Urban Rail Transit, 2017, 3: 112-127

[9] International Transport Forum (2015) Transport outlook. http://www.oecd.org/environment/itf-transport-outlook-2015-9789282107782-en.htm. Accessed 3 July 2017

[10] Landschützer C, Ehrentraut F, Jodin D. Containers for the physical internet: requirements and engineering design related to FMCG logistics. Logist Res, 2015, 8: 1-22

[11] Montreuil B. Toward a Physical Internet: meeting the global logistics sustainability grand challenge. Logist Res, 2011, 3: 71-87

[12] Colin JY, Nakechbandi M, Mathieu H. Management of mobile resources in physical internet logistic models. Int Conf Adv Logist Transp, 2015

[13] Hakami D (2012) From network to web dimension in supply chain management. Doctoral Thesis

[14] Lin YH, Meller R, Ellis KP, Thomas LM, Lombardi BJ. A decomposition-based approach for the selection of standardized modular containers. Int J Prod Res, 2014, 52: 4660-4672

[15] Sarraj R, Ballot E, Pan S, Montreuil B. Analogies between internet network and logistics service networks: challenges involved in the interconnection. J Intell Manuf, 2014, 25: 1207-1219

[16] Montreuil B, Ballot E, Tremblay W (2014) Modular design of physical internet transport, handling and packaging containers. Prog Mater Handl Res. https://hal.univ-lorraine.fr/ENSMP_CGS/hal-01487239v1

[17] Monteruil B, Meller D, Ballot E. Physical internet foundations. Serv Orientat Holonic Multi-agent Manuf Control, 2013

[18] Jeffery M. The information highway gets physical. Science, 2014, 344: 1104

[19] Sternberg H, Norrman A. The physical internet: review, analysis and future research agenda. Int J Phys Distrib Logist Manag, 2017, 47: 736-742

[20] Tako A, Robinson S. The application of discrete event simulation and system dynamics in the logistics and supply chain context. Decis Support Syst, 2012, 52: 802-815

[21] Holmgren J, Davidsson P, Persson J, Ramstedt L. TAPAS: a multi-agent based model for simulation of transport chains. Simul Model Pract Theory, 2012, 23: 1-18

[22] Tang M, Hu Y. Pedestrian simulation in transit stations using agent-based analysis. Urban Rail Transit, 2017, 3: 54-60

[23] Ballot E Physical internet enabled open hub network design for distributed networked operations. Serv Orient Holonic Multi-agent Manuf Control, 2012

[24] Hakami D, Montreuil B, Sarraj R, Ballot R, Pan S (2012) Simulating a physical internet enabled mobility web: the case of mass distribution in France. In: 9th international conference on modeling, optimization and simulation. https://hal.archives-ouvertes.fr/hal-00728584. Accessed 13 May 2017

[25] Furtado P, Fakhfakh R, Frayret JM, Biard P (2013) Simulation of a physical internet based transportation network. In: Proceedings of 2013 international conference on industrial engineering and systems management. http://ieeexplore.ieee.org/document/6761501/?reload=true&arnumber=6761501. Accessed 13 May 2017

[26] Sarraj R, Ballot E, Pan S, Hakimi D, Montreuil B. Interconnected logistic networks and protocols: simulation-based efficiency assessment. Int J Prod Res, 2014, 52(11): 3185-3208

[27] Pan S, Ballot E. Open tracing container repositioning simulation optimization: a case study of FMCG supply chain. Serv Orientat Holonic Multi-agent Manuf, 2015

[28] Pan Perspectives of inventory control models in the physical internet: a simulation study. Comput Ind Eng, 2015, 84: 122-132

[29] Yang Y, Pan S, Ballot E. Innovative vendor-managed inventory strategy exploiting interconnected logistics services in the physical internet. Int J Prod Res, 2017, 55: 2685-2702

[30] Anand N, Meijer D, Vuin J, Tavasszy L, Meijer S. Validation of an agent based model using a participatory simulation gaming approach: the case of city logistics. Transp Res Part C Emerg Technol, 2016, 71: 489-499

[31] Caris A, Macharis C, Janssens GK. Network analysis of container barge transport in the port of Antwerp by means of simulation. J Transp Geogr, 2011, 19: 125-133

[32] Davidsson P, Henesey L, Ramstedt L, Törnquist J, Wernstedt F. An analysis of agent-based approaches to transport logistics. Transp Res Part C Emerg Technol, 2005, 13: 255-271

[33] Banks J, Chwif L. Warnings about Simulation. J Simul, 2011, 5: 279-291

[34] Hakimi D, Montreuil B, Labarthe O (2010) Retail store demand estimation for supply network simulation. In: 8th international conference of modeling and simulation. http://www.enim.fr/mosim2010/articles/361.pdf. Accessed 13 May 2017

[35] Sun Y, Hrušovský M, Zhang C, Lang M. A time-dependent fuzzy programming approach for the green multimodal routing problem with rail service capacity and road traffic congestion. Complexity, 2018, 2018: 1-22.

Funding

Shandong Provincial Higher Educational Social Science Program of China(J18RA053)