Optimisation in Synchromodal Logistics: From Theory to Practice

Description

This book introduces the advances in synchromodal logistics and provides a framework to classify various optimisation problems in this field. It explores the application of this framework to solve a broad range of problems, such as problems with and without a central decision-maker, problems with and without full information, deterministic problems, problems coping with uncertainty, optimisation of a full network design problem. It covers theoretical constructs, such as discrete optimisation, robust optimisation, optimisation under uncertainty, multi-objective optimisation and agent based equilibrium models. Moreover, practical elaborated use cases are presented to deepen understanding.  The book gives both researchers and practitioners a good overview of the field of synchromodal optimisation problems and offers the reader practical methods for modelling and problem-solving. Preface References Acknowledgements Contents Acronyms Part I Introduction 1 Categorisations of Optimisation Problems in Synchromodal Logistics Introduction Context of Synchromodal Logistics Literature Optimisation Framework Changing Position in the Framework Complexity and Self-Organisation Uncertainty and Scope of Optimisation Conclusion References 2 Framework of Synchromodal Transportation Problems Introduction Literature Framework Identifiers and Elements Identifiers Elements Notation Six-Field Notation Two-Column Notation On the Two Notations Examples Solution Method Mapping Relationship to VRP Terminology Discussion References Part II Solving MCMCF Problems 3 Deterministic Container-to-Mode Assignment Introduction Modelling the Problem as a MCMC Flow Problem on a Space–Time Network Space–Time Networks Minimum-Cost Multi-Commodity Flow Allowing Lateness with Virtual Sinks Solving to Optimality Infinite Resource Models and the Corresponding Graph Reductions Double Matrix Infinite Resources Other Or No Infinite Resources Numerical Results Discussion Added Value Conclusion References 4 Stochastic Container-to-Mode Assignment Introduction Concepts and Definitions Transit Ideas and Transit Instances Request Ideas and Request Instances Omnifutures Finite Window Methods and Rolling Window Methods Locked Futures and Future Trees Demifutures Solving to Optimality Two-Stage Stochastic Programming Multistage Stochastic Programming: An Illustrative Example Why Multistage Stochastic Programming Is Not Used Markov Decision Processes Single Future Iteration Heuristics Expected Future Iteration Partially Pessimistic Future Iteration Numerical Results Discussion Added Value Conclusion References 5 Deterministic Operational Freight Planning Introduction Notation of Variables and Parameters Problem Features A Note on Labour Conditions Solving to Optimality ILP Formulation Speed-up from Additional Constraints Greedy Gain Heuristic Compatibility Clustering Heuristic Used Metrics Description of Algorithm Numerical Results Discussion Added Value Conclusion References 6 Alternative Performance Indicators for Optimising Container Assignment in a Synchromodal TransportationNetwork Introduction Attributes Robustness Flexibility Definitions Customer Satisfaction Conclusions References 7 Decision Making in a Dynamic Transportation Network: A Multi-Objective Approach Introduction Multi-Objective Analysis Multi-Objective Approach Proposed Approach Example Conclusions and Future Work Reference 8 Reduction of Variables for Solving Logistic Flow Problems Introduction Multi-Commodity Network Design Problem Variable Reductions Commodity Reductions Vehicle Reductions Arc Reductions Location Reductions Time Reductions Results Conclusion References 9 Cutting Planes for Solving Logistic Flow Problems Introduction Cutting Planes General Cuts Symmetry Breaking Cut Arc Residual Capacity Cut Cutset Cut Strong Cut Results and Conclusions References Part III Synchromodal Logistics as Selfish Systems 10 Optimising Routing in an Agent-Centric Synchromodal Network with Shared Information Introduction Literature Review Models Assumptions Description of Simulation Public Information Models Model 1: Minimum-Cost Routing Without Rerouting Model 2: Minimum-Cost Routing with Rerouting Full Information Model Model 3: Full Information, User Equilibrium Routing Results Conclusions References 11 User Equilibrium in a Transportation Space-Time Network Introduction Literature Review User Equilibrium in STN Numerical Examples Conclusions References 12 Fair User Equilibrium in a Transportation Space-Time Network Introduction Fair User Equilibrium in STN Finding Connected Components in STN Tolls on Orders Finding a User Equilibrium Existence of Solutions Path Tolls Based on Order Fairness Finding a User Equilibrium Existence of Solutions Conclusions and Future Research References Part IV Applications 13 Simulation Approach for Container Assignment underUncertainty Introduction Problem Description Simulation Approach Start of the Algorithm Decision Space Trivial Decisions Decision (ta, TRot) to (ta + 1, TRot) Decision (ta, Origin) to (ta + 7, TRot) Remaining Decisions Solving the ILP Results Design of Experiments Benchmark Solution Methods Numerical Results Conclusions and Further Research References 14 Optimising and Recognising 2-Stage Delivery Chains with Time Windows Introduction Optimisation Problem Description First Stage Fixed Penalty Penalty as Function of Delay Penalty is Random Variable, Independent of X Second Stage Second Time Slot Without Penalty in the First Time Slot Propagation of Penalty: Second Time Slot with Penalty Case Recognising Time Windows in Data Analysis Limitations Conclusions References 15 Two-Step Approach for the Multi-Objective Container Assignment Problem with Barge Scheduling Introduction Schedule Construction Methodology Illustrative Example Container Assignment Conclusion References 16 A Robust Optimisation Approach to Synchromodal Container Transportation Introduction Use Case Practical Setting Base Instance Deterministic Problem Formulation Deterministic Model Additional Remarks Robust Problem Formulation Robust Optimisation Paradigm The Robust Counterpart Adjustable Robust Optimisation Robust Optimisation for Mixed Integer Programs Robust Model Computational Results Instance Generation Results of Deterministic Model Results of Robust Model High Lateness Penalties Low Lateness Penalties Discussion References Index