Title of PhD dissertation: Real-time Co-planning in Synchromodal Transport Networks using Model Predictive Control 
Author: Rie Brammer Larsen
Co-authors on parts: Bilge Atasoy and Rudy R. Negenborn
 TU Delft, Faculty of Mechanical, Maritime and Materials Engineering (3mE), Department of Maritime and Transport Technology (M&TT)
ORCID: 0000-0003-3519-3140
2022

***General Introduction***
This dataset is a collection of the results and the data behind the figures displayed in the PhD dissertation ``Real-time Co-planning in Synchromodal Transport Networks using Model Predictive Control".
The research presented in this dissertation provides insights into how container transport can realistically be planned at the operational level in real-time when several different stakeholders own the vehicles.
The research has received funding from the Netherlands Organisation for Scientific Research (NWO) under the project "Complexity Methods for Predictive Synchromodality" (project 439.16.120).

*** File introductions***
The data collections is comprised of 4 zip files, each containg data from one chapter in the thesis:
- Integrating Routing Decisions for Containers and Vehicles: 
   Presents a centralized, model predictive control based method that integrate the routing of containers and vehicles in a synchromodal transport network. The core of this chapter is published in Rie B. Larsen, Bilge Atasoy, and Rudy R. Negenborn. Model predictive control for simultaneous planning of container and vehicle routes. European Journal of Control, volume 57, pages 273–283, 2021.
   Files: DEMANDPROFILE_Balanced, DEMANDPROFILE_MoreImport (the transport demand profiles used in the numerical experiments)
          Fig3_2_CostCPUhorizonLength, Fig3_3_DemandProfile, Fig3_5_VehiclesDriving, Fig3_6_BargeTrainUtilization (original matlab figures from which the exact data points can be extracted, named as they appear in Chapter 3 of the dissertation)

- Real-time Co-planning for Efficient Container Transport:
   Presents a real-time co-planning method that lets a logistics service provider reconsider container routing plans based on the feedback from a transport provider.
   Files: Demand (demand profile used in the numerical experiments)
          TransportNetwork (details of the transport network, both scheduled and flexible services, from the LSPs point of view used in the numerical experiments)
          Fig4_4_CaseStudyResults, Fig4_6_TransportNetwork (original figures from which the exact data points can be extracted, named as they appear in Chapter 4 of the dissertation)

- Learning Discrete Actions over Time:
   Present a method to parallelize the computation of the mixed-integer optimization problem in a model predictive controller for switched linear systems. Proves stability and recursive feasibility. The core of this chapter is published in Rie B. Larsen, Bilge Atasoy, Rudy R. Negenborn. Model Predictive Control with Memory-based Discrete Search for Switched Linear Systems. IFAC-PapersOnLine for IFAC World Congress, volume 53, pages 6769-6774, 2020.
   Files: Fig5_2_AccumulatedCost TransportNetwork (original matlab figure from which the exact data points can be extracted, named as it appear in Chapter 5 of the dissertation)
          AdditionalFigResults_RealisedDiscreteDecisions (additinal matlab figure that provides extra insights but has not yet been published)

- Co-planning with Learning:
   Presents a co-planning method where a barge operator optimizes barge departure times in real-time based on feedback from a transport organizer (truck company). Learning ideas are used to bridge the information gap. The initial work has been presented in Rie B. Larsen, Bilge Atasoy, and Rudy R. Negenborn. Learning-based co-planning for improved container, barge and truck routing. In Eduardo Lalla-Ruiz, Martijn Mes, and Stefan Voss, editors, In Proc. of the International Conference on Computational Logistics.
   Files: Fig6_6_Demand_peaks, Fig6_7_CPUhorizon, Fig6_8_impactN, Fig6_9_ImpactAlphaBeta, Fig6_10_ImpactSearchSpace, Fig6_11a_ImpactAlphaUnbalanced, Fig6_11b_ImpactSearchSpaceUnbalanced, Fig6_12_ResultingDeparturesUnbalanced  (original matlab figures from which the exact data points can be extracted, named as they appear in Chapter 6 of the dissertation)

Keywords:
Co-planning
Synchromodal transport planning
Cooperation
Model Predictive Control
Multi agent systems
Learning Cooperate
switched linear systems
Integrated Planning
Synchromodal transport