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Modelling the Impact of Liner Shipping Network Perturbations on Container Accident Analysis and Prevention 123 (2019) 399–410 Contents lists available at ScienceDirect Accident Analysis and Prevention jo urnal homepage: www.elsevier.com/locate/aap Modelling the impact of liner shipping network perturbations on container cargo routing: Southeast Asia to Europe application a,∗ b c Pablo E. Achurra-Gonzalez , Matteo Novati , Roxane Foulser-Piggott , a c d a Daniel J. Graham , Gary Bowman , Michael G.H. Bell , Panagiotis Angeloudis a Centre for Transport Studies, Department of Civil and Environmental Engineering, Skempton Building, South Kensington Campus, Imperial College London, London SW7 2BU, UK b Steer Davies Gleave Ltd., 28-32 Upper Ground, London SE1 9PD, UK c Faculty of Business, Bond University, 14 University Drive, Robina, QLD 4226, Australia d Institute of Transport and Logistics Studies (ITLS), University of Sydney Business School, The University of Sydney, C13-St. James Campus, Australia a r t i c l e i n f o a b s t r a c t Article history: Understanding how container routing stands to be impacted by different scenarios of liner shipping Received 30 June 2015 network perturbations such as natural disasters or new major infrastructure developments is of key Received in revised form 12 March 2016 importance for decision-making in the liner shipping industry. The variety of actors and processes within Accepted 26 April 2016 modern supply chains and the complexity of their relationships have previously led to the development Available online 3 June 2016 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 tech- Keywords: niques less dependent on complex data sets in order to develop a quantitative framework to assess the Liner shipping impacts of disruptive events on liner shipping networks. We provide a categorization of liner network per- Network perturbations turbations, differentiating between systemic and external and formulate a container assignment model Port disruption accidents Container assignment model that minimises routing costs extending previous implementations to allow feasible solutions when rout- ing 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. © 2016 Elsevier Ltd. All rights reserved. 1. Introduction environment (Mullai and Paulsson, 2011). Therefore, understand- ing the impact of liner shipping perturbations on container cargo While the effects of market cycles (Stopford, 2009) on the overall routing and their potential related accidents is crucial for decision- stability of liner shipping networks have been the subject of exten- makers in the maritime industry who strive at being better prepare sive research over the years, what is less known is how the overall for these events. liner shipping transport system can be affected by perturbations to It is unrealistic to expect remove all uncertainty related to the the established network topology caused by events such as infras- potential effects of the above-mentioned events. However, this tructure developments, natural disasters or armed conflicts. These uncertainty can be reduced applying quantitative frameworks that perturbations are important because they could significantly alter model container routing under hypothetical scenarios of network transportation capabilities between regions or result in accidents perturbations and examining historical records of accidents related that can cause loss of life, injuries, economic loss or damage to the to the events evaluated. Such frameworks, however, are not sim- ple to formulate. The variety of actors and processes within modern supply chains, and the complexity of their relationships have previ- ∗ ously led to the development of simulation-based container models Corresponding author. E-mail addresses: [email protected] whose, application have been largely compromised by their depen- (P.E. Achurra-Gonzalez), [email protected] (M. Novati), dency on extensive and complex sets of data which are generally [email protected] (R. Foulser-Piggott), [email protected] not available in a many cases and regions. (D.J. Graham), [email protected] (G. Bowman), [email protected] (M.G.H. Bell), [email protected] (P. Angeloudis). https://doi.org/10.1016/j.aap.2016.04.030 0001-4575/© 2016 Elsevier Ltd. All rights reserved. 400 P.E. Achurra-Gonzalez et al. / Accident Analysis and Prevention 123 (2019) 399–410 One of the earliest attempts to simulate maritime container 2. Methodology flows at a global scale was the Container World project (Newton, 2008; Bell et al., 2011). This study proposed a simulation approach 2.1. Classification scheme for liner shipping network in which every ship, port, liner service, shipping line, truck and rail perturbations operator was represented by a separate agent. The network was built using actual port rotations published by ocean carriers. Con- We define “network perturbation” as any change, positive or tainers were transported via each of the agents operating based on negative, to the existing state of main components of liner ship- their individual set of parameters. Although the model provided a ping networks. These include ports (nodes), routes operated by framework for global-scale container routing, it proved to be too container liner services (links), vessels size (capacity), and trans- data intensive in an competitive industry reluctant to share the port demands (origin-destination pairs). Whether a perturbation data required to maintain the model (Bell et al., 2011). This limita- is positive or negative often depends on the point of view of each tion hampers the application of such model for scenario analysis in stake holder. For example, the 1995 Port of Kobe disruption caused regions where the required data is not available. by an earthquake diverted local cargo to the ports of Osaka, Nagoya Alternative research efforts have focused on the development of and Yokohama and transhipment cargo to the ports of Busan and optimisation-based models that can operate with simpler datasets Kaohsiung improving their cargo volumes and business. Though yet are capable of delivering reliable results using computationally the port of Kobe recovered and the local cargo returned, signifi- efficient mathematical programs. The network used in these mod- cant transhipment volumes never returned (Lam, 2012). Positive els can be built from published ocean carrier schedules (Zurheide or negative perturbations impacts are not isolated to port disrup- and Fischer, 2012) or computed from a liner shipping network tions. For example, the improvement of existing infrastructure such design problem (Agarwal and Ergun, 2008). The objectives in the as the Panama Canal expansion scheduled for completion in 2016 optimisation-based literature range between minimisation of rout- will relax vessel deployment upper bound constraints through this ing costs (Wang et al., 2013), minimisation of sailing time (Bell et al., waterway. Potential impacts include transshipment cargo shift- 2011), maximisation of profit from an ocean carrier point of view ing in the Caribbean area from ports without capacity to receive (Ting and Tzeng, 2004) and maximisation of volumes transported post-panamax vessels to those with adequate infrastructure to (Song et al., 2005). Tran and Haasis (2013) provide a compre- accommodate such vessels (Rodrigue and Ashar, 2015). hensive review of previous optimisation-based works including In order to identify in which scenarios a container routing model additional relevant features such as empty container repositioning, can provide a contribution to the analysis of network perturba- deterministic or stochastic shipping demand, and container routing tions, we proposed a classification scheme which differentiates problems in time extended networks. between systemic and external perturbations. This differentiation This study seeks to contribute to the application of optimisation- allows to identify sources of disruptions which, from a modelling based models for the analysis of liner shipping cargo flows affected stand-point, dictate the main parameters of the network that will by network perturbations, building upon earlier work by Bell et al. be modified. (2013) on cost-based container assignment. The proposed applica- Previous studies have focused on the study of maritime supply tion of this model minimises expected container routing costs in chains risk with a focus on port disruptions (e.g. Lam, 2012; Loh order to assess changes in container cargo flows under scenarios of and Van Thai, 2014; Mansouri et al., 2010; Qi, 2015). However, this seismic and conflict hazards affecting the Southeast Asia to Europe initial non-exhaustive taxonomy is, to the best of our knowledge, trades. We examine previous studies of past similar disruptions the first attempt to classify perturbations that impact additional in order to discuss their potential related accidents and network liner shipping network components beyond ports as focal point parameters affected in the aftermath of the disruption scenarios such as route capacity and factors
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