Modelling the impact of liner shipping network perturbations on container cargo routing: Southeast Asia to Europe application

Understanding how container routing stands to be impacted by different scenarios of liner shipping network perturbations such as natural disasters or new major infrastructure developments is of key importance for decision-making in the liner shipping industry. The variety of actors and processes within modern supply chains and the complexity of their relationships have previously led to the development of simulation-based models, whose application has been largely compromised by their dependency on extensive and often confidential sets of data. This study proposes the application of optimisation techniques less dependent on complex data sets in order to develop a quantitative framework to assess the impacts of disruptive events on liner shipping networks. We provide a categorization of liner network perturbations, differentiating between systemic and external and formulate a container assignment model that minimises routing costs extending previous implementations to allow feasible solutions when routing capacity is reduced below transport demand. We develop a base case network for the Southeast Asia to Europe liner shipping trade and review of accidents related to port disruptions for two scenarios of seismic and political conflict hazards. Numerical results identify alternative routing paths and costs in the aftermath of port disruptions scenarios and suggest higher vulnerability of intra-regional connectivity

Influence of spillback effect on dynamic shortest path problems with travel-time-dependent network disruptions

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Information impacts on route choice and learning behavior in a congested network

Every traveler makes route choices in an uncertain environment that includes random disruptions to the traffic system such as incidents, bad weather, and random behavior of fellow travelers. The premise underlying the development of advanced traveler information systems-that better-informed travelers make better route choices-should be tested. This paper studies en route real-time information about the occurrence of an incident and ex post information on forgone payoffs (FPs) (i.e., travel times on nonchosen routes). Data were collected from an interactive experiment in which subjects made multiple rounds of route choices on a hypothetical network subject to random capacity reductions, and travel times were determined by performance functions of route flows from the previous round. En route real-time information increased the network's travel-time savings and reliability under the experimental setting, yet FP information had the opposite effect. The most efficient information structure in terms of travel-time savings is a combination of real-time information and no FP information. Real-time information at downstream nodes encourages participants' strategic behavior at the origin. FP information appears to increase risk-seeking behavior; it encourages route switching without real-time information and suppresses it with real-time information. These results could be valuable for policy evaluations of further developments of advanced traveler information systems

Simulation Study for Multi-Echelon Multi-Depot Supply Chain System Using Live Data

The manufacturing industry is eager to implement the advancements of the fourth industrial revolution (Industry 4.0) due to the magnitude of the benefits it can provide. Hence, Industry 4.0 opens a wide avenue for researchers to explore possibilities in the field of the supply chain. This project focuses on building a decision framework for a supply chain system with disruptions. The impact of strategic decisions under the condition of unprecedented events for a vehicle routing problem (VRP) using simulation models is studied here. Those results help the supply chain managers in making sound decisions regarding different scenarios of disruption in VRP. To achieve this, multiple cases under different scenarios of facility disruption are considered. For all cases, the dependent parameter, namely, retailer service level and lost revenue, form the basis of the decision framework. The concept of live data is implemented by making retailer demand, current inventory at the depot, the position of the vehicle in the network and the current number of units in transit as the input data