Uncertainty in Intermodal and Synchromodal Transport: Review and Future Research Directions
Abstract
:1. Introduction
2. Methodology
3. Strategic Decisions
3.1. Planning Problems
3.2. Solution Methods
4. Tactical Decisions
4.1. Planning Problems
4.1.1. Service Network Design
4.1.2. Network Flow Planning
4.2. Solution Methods
5. Operational Decisions
5.1. Planning Problems
5.1.1. Replanning
5.1.2. Resource Management
5.2. Solution Methods
6. Discussion and Future Research Directions
- The share of road transport is higher when considering stochastic demand, transit times or capacity compared to a deterministic setting.
Author Contributions
Funding
Conflicts of Interest
Appendix A
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Planning Level | Total | Transit Times | Demand | Capacity | Costs | Hub Failures | Departure Times and Cancellations |
---|---|---|---|---|---|---|---|
Strategic | 7 | 3 | 4 | 1 | 2 | 1 | |
Tactical | 23 | 12 | 11 | 7 | 1 | ||
Operational | 12 | 6 | 7 | 2 | 1 | 1 |
Authors (Year) | Reference | Transport Modes | Transit Times | Demand | Capacity | Hub Failures | Costs | Solution Method 1 | Uncertainty Mitigation |
---|---|---|---|---|---|---|---|---|---|
Sim et al. (2009) | [35] | Unspecified | x | A: Heuristic | Scenario generation | ||||
Ishfaq and Sox (2012) | [36] | Rail, road | x | A: Tabu-search metaheuristic | Scenario generation | ||||
Meraklı and Yaman (2016) | [37] | Unspecified | x | E: Benders decomposition | Robust optimisation | ||||
Fotuhi and Huynh (2017) | [38] | Rail, road | x | x | A: Genetic algorithm | Robust optimisation | |||
Karimi et al. (2018) | [39] | Unspecified | x | E: Solver | Scenario generation | ||||
Wang et al. (2018) | [40] | Rail, road | x | x | x | A: Memetic algorithm | Scenario generation | ||
Abbassi et al. (2019) | [41] | Ship, road | x | x | A: Simulated annealing metaheuristic | Robust optimisation |
Authors (Year) | Reference | Modes | Transit Times | Demand | Capacity | Costs | Late Deliveries | Solution Method 1 | Uncertainty Mitigation |
---|---|---|---|---|---|---|---|---|---|
Andersen and Christiansen (2009) | [50] | Rail | x | Penalty for delivery time variability | E: Solver | Scenario generation | |||
Lium et al. (2009) | [51] | Unspecified | x | Ad hoc capacity increase | E | Scenario generation | |||
Hoff et al. (2010) | [52] | Unspecified | x | Ad hoc capacity increase | A: Metaheuristic | Scenario generation | |||
Crainic et al. (2011) | [53] | Unspecified | x | Ad hoc capacity increase | A: Tabu-search metaheuristic | Scenario generation, recourse | |||
Puettmann and Stadtler (2010) | [54] | Road, ship | x | Not allowed | E: Solver | Collaboration | |||
Bai et al. (2014) | [55] | Unspecified | x | Ad hoc capacity increase, rerouting | E: Solver | Rerouting | |||
Meng et al. (2015) | [56] | Barge, rail, road | x | Ad hoc capacity increase | A: SAA, matheuristic | Recourse | |||
Demir et al. (2016) | [19] | Barge, rail, road | x | x | Penalty cost | A: SAA, solver | Recourse | ||
Yang et al. (2016) | [46] | Air, rail, road | x | x | Travel time in objective | A: Simulated annealing metaheuristic | Scenario generation | ||
Hrušovský et al. (2018) | [57] | Barge, rail, road | x | Ad hoc capacity increase | A: Simulation–optimisation | Scenario generation | |||
Zhao et al. (2018) | [48] | Rail, ship | x | Penalty and nonfulfilment cost | A: Genetic algorithm | Scenario generation | |||
Zhao et al. (2018) | [58] | Rail, Ship | x | x | Penalty cost | A: SAA, ant colony optimisation metaheuristic | Recourse | ||
Layeb et al. (2018) | [59] | Barge, rail, road | x | x | Penalty cost | A: Simulation–optimisation | Scenario generation | ||
Sun et al. (2018) | [60] | Rail, road | x | Not applicable | E: Solver | Scenario generation |
Authors (Year) | Reference | Modes | Transit Times | Demand | Capacity | Late Deliveries | Solution Method 1 | Uncertainty Mitigation |
---|---|---|---|---|---|---|---|---|
Li et al. (2004) | [61] | Barge, rail, road | x | Cost and time in objective function | E | Scenario generation | ||
Huang et al. (2011) | [49] | Rail, road, ship | x | No penalty cost | E: Depth-first search | Rerouting | ||
Meng et al. (2012) | [62] | Ship | x | Excess demand is lost | A: SAA, Lagrangian relaxation | Scenario generation | ||
Chen and Miller-Hooks (2012) | [12] | Barge, rail, road | x | x | Minimum service level | E: Benders decomposition, column generation | Scenario generation | |
Miller-Hooks et al. (2012) | [14] | Unspecified | x | x | Not applicable | E: L-shaped method | Scenario generation | |
Li et al. (2015) | [26] | Barge, rail, road | x | x | Penalty cost | E: Linear programming | Receding horizon approach | |
Uddin and Huynh (2016) | [17] | Rail, road | x | No delivery and penalty cost | A: SAA, solver | Scenario generation | ||
Sun et al. (2018) | [47] | Rail, road | x | x | Not allowed | E: Solver | Scenario generation | |
Uddin and Huynh (2019) | [18] | Rail, road | x | No delivery and penalty cost | E: Solver | Scenario generation |
Authors (Year) | Reference | Transport Modes | Transit Times | Demand | Capacity | Other | Solution Method 1 | Uncertainty Mitigation |
---|---|---|---|---|---|---|---|---|
Bock (2010) | [66] | Road | x | x | A: Metaheuristic | Rerouting | ||
Burgholzer et al. (2013) | [67] | Barge, rail, road | x | x | A: Simulation | Rerouting | ||
Escudero et al. (2013) | [68] | Unspecified | x | A: Genetic algorithm | Rerouting | |||
van Riessen et al. (2015) | [69] | Barge, rail, road | departure times and cancellations | E: Linear programming | Rerouting | |||
Sun and Schonfeld (2016) | [70] | Unspecified | x | A: Direct search | Holding decisions | |||
Rivera and Mes (2017) | [71] | Barge, road | x | A: Approximate dynamic programming | Replanning | |||
Rivera and Mes (2017) | [72] | Road | x | x | A: Matheuristic | Replanning | ||
Qu et al. (2019) | [25] | Barge, rail, road | x | x | E: Solver | Replanning |
Authors (Year) | Reference | Problem Type | Transport Modes | Transit Times | Demand | Other | Solution Method 1 | Uncertainty Mitigation |
---|---|---|---|---|---|---|---|---|
Lam et al. (2007) | [75] | empty container repositioning | Ship | x | A: Approximate dynamic programming | Scenario generation | ||
Topaloglu (2007) | [74] | empty vehicle repositioning | Rail | x | x | A: Approximate dynamic programming | Replanning | |
Di Francesco et al. (2013) | [76] | empty container repositioning | Ship | hub failures | E: Stochastic integer programming | Scenario generation | ||
van Riessen et al. (2016) | [77] | container allocation | Barge, rail, road | x | A: Heuristic | Machine learning |
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Delbart, T.; Molenbruch, Y.; Braekers, K.; Caris, A. Uncertainty in Intermodal and Synchromodal Transport: Review and Future Research Directions. Sustainability 2021, 13, 3980. https://doi.org/10.3390/su13073980
Delbart T, Molenbruch Y, Braekers K, Caris A. Uncertainty in Intermodal and Synchromodal Transport: Review and Future Research Directions. Sustainability. 2021; 13(7):3980. https://doi.org/10.3390/su13073980
Chicago/Turabian StyleDelbart, Thibault, Yves Molenbruch, Kris Braekers, and An Caris. 2021. "Uncertainty in Intermodal and Synchromodal Transport: Review and Future Research Directions" Sustainability 13, no. 7: 3980. https://doi.org/10.3390/su13073980
APA StyleDelbart, T., Molenbruch, Y., Braekers, K., & Caris, A. (2021). Uncertainty in Intermodal and Synchromodal Transport: Review and Future Research Directions. Sustainability, 13(7), 3980. https://doi.org/10.3390/su13073980