Skip to main content
SpringerLink
Log in

Optimizing the Cargo Flows in Multi-modal Freight Transportation Network Under Disruptions

  • Research Paper
  • Published:
Iranian Journal of Science and Technology, Transactions of Civil Engineering Aims and scope Submit manuscript

Abstract

This study develops a multi-objective optimization approach for integrated vulnerability analysis and disturbance response preparation in intermodal freight road–rail networks under the possible risk of connection disturbance. The risk and criticality of the infrastructure components are analyzed to monitor disturbances in freight transport networks efficiently. The aim of the optimization model is to find a balance between transport charges, i.e., rerouting charges, mode shift and unsupplied demand, and the freight transport system's operation efficiency. The efficiency of the transport system is classified as the portion of consumer demand transported to destination points from the specified origin over a specified time interval. The problem is conceived as a mixed-integer linear programming paradigm and is the minimal cost flow problem extension. Two methods, including an enhanced ε-constraint approach and a heuristic routing algorithm, are suggested to solve large-size instances of the problem. The verification of the suggested algorithmic system for the control of road–rail disruptions was justified utilizing simulation experiments on real-world instances of Iranian road–rail transport networks. The results illustrate the practical ramifications of the current optimization paradigm to reduce computational time and increase freight transport performance following uncertainty in the intermodal road–rail freight network.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Almoghathawi Y, Barker K, Rocco CM, Nicholson CD (2017) A multi-criteria decision analysis approach for importance identification and ranking of network components. Reliab Eng Syst Saf 158:142–151

    Google Scholar 

  • Azad N, Hassini E, Verma M (2016) Disruption risk management in railroad networks: an optimization-based methodology and a case study. Transp Res Part B Methodol 85:70–88

    Google Scholar 

  • Azadi Moghaddam Arani A, Jolai F, Nasiri MM (2019) A multi-commodity network flow model for railway capacity optimization in case of line blockage. Int J Rail Transp 7(4):297–320

    Google Scholar 

  • Bababeik M, Khademi N, Chen A, Nasiri MM (2017) Vulnerability Analysis of Railway Networks in Case of Multi-Link Blockage. Transp Res Procedia 22:275–284

    Google Scholar 

  • Bababeik M, Nasiri MM, Khademi N, Chen A (2017) Vulnerability evaluation of freight railway networks using a heuristic routing and scheduling optimization model. Transportation 46:1–28

    Google Scholar 

  • Basirati M, Akbari Jokar MR, Hassannayebi E (2020) Bi-objective optimization approaches to many-to-many hub location routing with distance balancing and hard time window. Neural Comput Appl 32(17):13267–13288. https://doi.org/10.1007/s00521-019-04666-z

    Article  Google Scholar 

  • Baykasoğlu A, Subulan K, Taşan AS, Dudaklı N (2019) A review of fleet planning problems in single and multimodal transportation systems. Transportmetr A Transp Sci 15(2):631–697

    Google Scholar 

  • Bell MG, Kurauchi F, Perera S, Wong W (2017) Investigating transport network vulnerability by capacity weighted spectral analysis. Transp Res Part B Methodol 99:251–266

    Google Scholar 

  • Bhavathrathan BK, Patil GR (2015) Quantifying resilience using a unique critical cost on road networks subject to recurring capacity disruptions. Transp A Transp Sci 11(9):836–855

    Google Scholar 

  • Büsing C, Koster A, Kirchner S, Thome A (2017) The budgeted minimum cost flow problem with unit upgrading cost. Networks 69(1):67–82

    MathSciNet  MATH  Google Scholar 

  • Calatayud A, Mangan J, Palacin R (2017) Vulnerability of international freight flows to shipping network disruptions: a multiplex network perspective. Transp Res Part E Logist Transp Rev 108:195–208

    Google Scholar 

  • Cantillo V, Macea LF, Jaller M (2018) Assessing vulnerability of transportation networks for disaster response operations. Netw Spat Econ 19:1–31

    MathSciNet  MATH  Google Scholar 

  • Cantillo V, Macea LF, Jaller M (2019) Assessing vulnerability of transportation networks for disaster response operations. Netw Spat Econ 19(1):243–273

    MathSciNet  MATH  Google Scholar 

  • Chargui T, Bekrar A, Reghioui M, Trentesaux D (2020) Proposal of a multi-agent model for the sustainable truck scheduling and containers grouping problem in a Road-Rail physical internet hub. Int J Prod Res 58(18):5477–5501

    Google Scholar 

  • Chen L, Miller-Hooks E (2012) Resilience: an indicator of recovery capability in intermodal freight transport. Transp Sci 46(1):109–123

    Google Scholar 

  • Chen C-C, Schonfeld P (2011) Alleviating schedule disruptions at intermodal freight transfer terminals: real-time dispatching control. Transpo Res Record J Transp Res Board 2238:32–43

    Google Scholar 

  • Chen H, Cullinane K, Liu N (2017) Developing a model for measuring the resilience of a port-hinterland container transportation network. Transp Res Part E Logist Transp Rev 97:282–301

    Google Scholar 

  • Djavadian S, Chow JYJ (2017) Agent-based day-to-day adjustment process to evaluate dynamic flexible transport service policies. Transportmetr B Transp Dyn 5(3):281–306

    Google Scholar 

  • Gedik R, Medal H, Rainwater C, Pohl EA, Mason SJ (2014) Vulnerability assessment and re-routing of freight trains under disruptions: A coal supply chain network application. Transp Res Part E Logist Transp Rev 71:45–57

    Google Scholar 

  • Ghaderi A, Burdett RL (2019) An integrated location and routing approach for transporting hazardous materials in a bi-modal transportation network. Transp Res Part E Logist Transp Rev 127:49–65

    Google Scholar 

  • Gu Y, Fu X, Liu Z, Xu X, Chen A (2020) Performance of transportation network under perturbations: reliability, vulnerability, and resilience. Transp Res Part E Logist Transp Rev 133:101809

    Google Scholar 

  • Hasannayebi E, Sajedinejad A, Mardani S, Mohammadi KSARM An integrated simulation model and evolutionary algorithm for train timetabling problem with considering train stops for praying. USA, 2012 2012. IEEE, pp 1–13

  • Hassannayebi E, Zegordi SH, Amin-Naseri MR, Yaghini M (2016a) Demand-oriented timetable design for urban rail transit under stochastic demand. J Ind Syst Eng 9(3):28–56

    Google Scholar 

  • Hassannayebi E, Sajedinejad A, Mardani S (2016) Disruption management in urban rail transit system: a simulation based optimization approach. Handbook of research on emerging innovations in rail transportation engineering, pp 420–450

  • Hassannayebi E, Zegordi SH, Amin-Naseri MR, Yaghini M (2016) Demand-oriented timetable design for urban rail transit under stochastic demand. J Ind Syst Eng 9(3):28–56

    Google Scholar 

  • Hassannayebi E, Boroun M, Jordehi SA, Kor H (2019) Train schedule optimization in a high-speed railway system using a hybrid simulation and meta-model approach. Comput Ind Eng 138:106110

    Google Scholar 

  • Hassannayebi E, Memarpour M, Mardani S, Shakibayifar M, Bakhshayeshi I, Espahbod S (2019) A hybrid simulation model of passenger emergency evacuation under disruption scenarios: a case study of a large transfer railway station. J Simul 14:1–25

    Google Scholar 

  • Hassannayebi E, Zegordi SHJC, Research O (2017) Variable and adaptive neighbourhood search algorithms for rail rapid transit timetabling problem. Comput Oper Res 78:439–453

    MathSciNet  MATH  Google Scholar 

  • Hrušovský M, Demir E, Jammernegg W, Van Woensel T (2021) Real-time disruption management approach for intermodal freight transportation. J Clean Prod 280:124826

    Google Scholar 

  • Huang M, Hu X, Zhang L (2011) A Decision method for disruption management problems in intermodal freight transport. Intell Decis Technol:13–21

  • Ip WH, Wang D (2011) Resilience and friability of transportation networks: evaluation, analysis and optimization. IEEE Syst J 5(2):189–198

    Google Scholar 

  • Jabbarzadeh A, Azad N, Verma M (2020) An optimization approach to planning rail hazmat shipments in the presence of random disruptions. Omega 96:102078

    Google Scholar 

  • Jafarian-Moghaddam AR, Yaghini M (2019) An effective improvement to main non-periodic train scheduling models by a New Headway definition. Iran J Sci Technol Trans Civ Eng 43(4):735–745

    Google Scholar 

  • Ke GY (2020) Managing rail-truck intermodal transportation for hazardous materials with random yard disruptions. Ann Oper Res. https://doi.org/10.1007/s10479-020-03699-1

    Article  MATH  Google Scholar 

  • Khaled AA, Jin M, Clarke DB, Hoque MA (2013) Determination of criticality of freight railroad infrastructure based on flow optimization under heavy congestion. Transportation research board 92nd annual meeting, Washington DC, United States

  • Khaled AA, Jin M, Clarke DB, Hoque MA (2015) Train design and routing optimization for evaluating criticality of freight railroad infrastructures. Transp Res Part B Methodol 71:71–84

    Google Scholar 

  • Khanmohamadi M, Bagheri M, Khademi N, Ghannadpour SF (2018) A security vulnerability analysis model for dangerous goods transportation by rail–Case study: chlorine transportation in Texas-Illinois. Saf Sci 110:230–241

    Google Scholar 

  • Kim NS, Park B, Lee K-D (2016) A knowledge based freight management decision support system incorporating economies of scale: multimodal minimum cost flow optimization approach. Inf Technol Manage 17(1):81–94

    Google Scholar 

  • Kurauchi F, Uno N, Sumalee A, Seto Y (2009) Network evaluation based on connectivity vulnerability. In: Lam WHK, Wong SC, Lo HK (eds) Transportation and traffic theory 2009: golden jubilee. Springer, pp 637–649

  • Lee H, Choo S (2016) Optimal decision making process of transportation service providers in maritime freight networks. KSCE J Civ Eng 20(2):922–932

    Google Scholar 

  • Li Q, Nie YM, Vallamsundar S, Lin J, Homem-de-Mello T (2016) Finding efficient and environmentally friendly paths for risk-averse freight carriers. Netw Spat Econ 16(1):255–275

    MathSciNet  MATH  Google Scholar 

  • Lim C, Smith JC (2007) Algorithms for discrete and continuous multicommodity flow network interdiction problems. IIE Trans 39(1):15–26

    Google Scholar 

  • Mavrotas G (2009) Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Appl Math Comput 213(2):455–465

    MathSciNet  MATH  Google Scholar 

  • Mavrotas G, Florios K (2013) An improved version of the augmented ε-constraint method (AUGMECON2) for finding the exact pareto set in multi-objective integer programming problems. Appl Math Comput 219(18):9652–9669

    MathSciNet  MATH  Google Scholar 

  • Mesa-Arango R, Ukkusuri SV (2017) Minimum cost flow problem formulation for the static vehicle allocation problem with stochastic lane demand in truckload strategic planning. Transportmetr A Transp Sci 13(10):893–914

    Google Scholar 

  • Mirzahossein H, Safari F, Hassannayebi E (2020) Estimation of highway capacity under environmental constraints vs. conventional traffic flow criteria: A case study of Tehran. Journal of Traffic and Transportation Engineering (English Edition)

  • Musolino G, Polimeni A, Vitetta A (2018) Freight vehicle routing with reliable link travel times: a method based on network fundamental diagram. Transp Lett 10(3):159–171

    Google Scholar 

  • Nicholson CD, Barker K, Ramirez-Marquez JE (2016) Flow-based vulnerability measures for network component importance: experimentation with preparedness planning. Reliab Eng Syst Saf 145:62–73

    Google Scholar 

  • Ouyang M, Pan Z, Hong L, He Y (2015) Vulnerability analysis of complementary transportation systems with applications to railway and airline systems in China. Reliab Eng Syst Saf 142:248–257

    Google Scholar 

  • Pant R, Barker K, Ramirez-Marquez JE, Rocco CM (2014) Stochastic measures of resilience and their application to container terminals. Comput Ind Eng 70:183–194

    Google Scholar 

  • Pourhejazy P, Kwon OK, Lim H (2019) Integrating sustainability into the optimization of fuel logistics networks. KSCE J Civ Eng 23(3):1369–1383

    Google Scholar 

  • Rowan E, Snow C, Choate A, Rodehorst B, Asam S, Hyman R, Kafalenos R, Gye A (2014) Indicator approach for assessing climate change vulnerability in transportation infrastructure. Transp Res Rec 2459(1):18–28

    Google Scholar 

  • Sadeghi S, Seifi A, Azizi E (2017) Trilevel shortest path network interdiction with partial fortification. Comput Ind Eng 106:400–411

    Google Scholar 

  • Shakibayifar M, Hassannayebi E, Jafary H, Sajedinejad, (2017) Stochastic optimization of an urban rail timetable under time-dependent and uncertain demand. Appl Stoch Models Bus Ind. 33(6):640–661

    MathSciNet  Google Scholar 

  • Shakibayifar M, Hassannayebi E, Mirzahossein H, Taghikhah F, Jafarpur A (2019) An intelligent simulation platform for train traffic control under disturbance. Int J Model Simul 39(3):135–156. https://doi.org/10.1080/02286203.2018.1488110

    Article  Google Scholar 

  • Shakibayifar M, Sheikholeslami A, Corman F, Hassannayebi E (2020) An integrated rescheduling model for minimizing train delays in the case of line blockage. Oper Res Int Journal 20(1):59–87

    Google Scholar 

  • Shariat Mohaymany A, Nikoo N (2020) Designing large-scale disaster response routes network in mitigating earthquake risk using a multi-objective stochastic approach. KSCE J Civ Eng. https://doi.org/10.1007/s12205-020-1844-x

    Article  Google Scholar 

  • Ta C, Goodchild AV, Pitera K (2009) Structuring a definition of resilience for the freight transportation system. Transp Res Rec 2097(1):19–25

    Google Scholar 

  • Uddin M, Huynh N (2019) Reliable routing of road-rail intermodal freight under uncertainty. Netw Spat Econ 19(3):929–952

    MATH  Google Scholar 

  • Van Riessen B, Negenborn RR, Lodewijks G, Dekker R (2015) Impact and relevance of transit disturbances on planning in intermodal container networks using disturbance cost analysis. Maritime Econ Logist 17(4):440–463

    Google Scholar 

  • Whitman MG, Barker K, Johansson J, Darayi M (2017) Component importance for multi-commodity networks: application in the Swedish railway. Comput Ind Eng 112:274–288

    Google Scholar 

  • Wu Y-J, Hayat T, Clarens A, Smith BL (2013) Climate change effects on transportation infrastructure: scenario-based risk analysis using geographic information systems. Transp Res Rec 2375(1):71–81

    Google Scholar 

  • Xu X, Chen A, Yang C (2017) An optimization approach for deriving upper and lower bounds of transportation network vulnerability under simultaneous disruptions of multiple links. Transp Res Procedia 23:645–663

    Google Scholar 

  • Yee H, Gijsbrechts J, Boute R (2021) Synchromodal transportation planning using travel time information. Comput Ind 125:103367

    Google Scholar 

  • You SI, Chow JYJ, Ritchie SG (2016) Inverse vehicle routing for activity-based urban freight forecast modeling and city logistics. Transportmetr A Transp Sci 12(7):650–673

    Google Scholar 

  • Zhang X, Li L (2019) An integrated planning/pricing decision model for rail container transportation. Int J Civ Eng 17(10):1537–1546

    Google Scholar 

  • Zhang L, Xiong C (2017) A novel agent-based modelling framework for travel time reliability analysis. Transportmetr B Transp Dyn 5(1):78–95

    MathSciNet  Google Scholar 

  • Zhang Q, Liu S, Gong D, Zhang H, Tu Q (2019) An improved multi-objective quantum-behaved particle swarm optimization for railway freight transportation routing design. IEEE Access 7:157353–157362

    Google Scholar 

Download references

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. School of Civil Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran

    Mahmuod Ahmady & Yones Eftekhari Yeghaneh

Authors
  1. Mahmuod Ahmady
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yones Eftekhari Yeghaneh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mahmuod Ahmady.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ahmady, M., Eftekhari Yeghaneh, Y. Optimizing the Cargo Flows in Multi-modal Freight Transportation Network Under Disruptions. Iran J Sci Technol Trans Civ Eng 46, 453–472 (2022). https://doi.org/10.1007/s40996-021-00631-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40996-021-00631-w

Keywords

Search

Navigation