Using complex network theory to model supply chain network resilience: a review of current literature

Traditionally, supply networks are modelled as multi-agent systems, in order to represent explicit communications between various entities involved. However, due to the increasingly complex and interconnected nature of the global supply networks, a recent trend of research work has focussed on modelling supply networks as complex adaptive systems. This approach has enabled researchers to investigate various topological properties which give rise to resilience characteristics in a given supply network. This paper presents a critical review of the published research work on this field. Key insights provided by this paper include; (1) the importance of defining the concepts of ‘resilience’ and ‘disruptions’ as measurable variables; (2) the limitations of existing network models to realistically represent supply networks; (3) potential improvements to the currently used growth mechanisms, which rely on node ‘degree’ to derive attachment probability instead of the more realistic and relevant node ‘fitness’; (4) importance of incorporating operational aspects, such as flows, costs, and capacities of connections between the nodes as well as topological aspects; and (5) derivation of a new set of resilience metrics capturing operational as well as topological aspects. Finally, a conceptual approach incorporating the above improvements to the existing supply network modelling approach is presented

Strategic maritime container service design in oligopolistic markets

AbstractThis paper considers the maritime container assignment problem in a market setting with two competing firms. Given a series of known, exogenous demands for service between pairs of ports, each company is free to design liner services connecting a subset of the ports and demand, subject to the size of their fleets and the potential for profit. The model is designed as a three-stage complete information game: in the first stage, the firms simultaneously invest in their fleet; in the second stage, they individually design their services and solve the route assignment problem with respect to the transport demand they expect to serve, given the fleet determined in the first stage; in the final stage, the firms compete in terms of freight rates on each origin–destination movement. The game is solved by backward induction. Numerical solutions are provided to characterize the equilibria of the game

Disassortativity in Biological and Supply Chain Networks

Network science has allowed researchers to model complex real world systems as networks in order to identify non trivial topological patterns. Degree correlations (or assortativity) is one such non trivial topological property, which indicates the extent to which nodes with similar degrees tend to pair up with each other. Biological networks have long been known to display anti-degree correlations (disassortativity), where highly connected nodes tend to avoid linking with each other. However, the mechanism underlying this structural organisation remain not well understood. Recent work has suggested that in some instances, disassortativity can be observed merely as a model artefact due to simple network representations not allowing multiple link formations between the node pairs. This phenomena is known as structural disassortativity. In this paper, we analyse datasets from two distinct classes of networks, namely; man made supply chain networks and naturally occurring biological networks. We examine whether the observed disassortativity in these networks are structurally induced or owing to some external process. Degree preserving randomisation is used to generate an ensemble of null models for each network. Comparison of the degree correlation profiles of each network, against that of their degree preserving randomised counterparts reveal whether the observed disassortativity in each network is of structural nature or not. We find that in all biological networks, the observed disassortativity is of structural nature, meaning their disassortative nature can be fully explained by their respective degree distributions, without attribution to any underlying mechanism which drives the system towards disassortativity. However, in supply chain networks, we find one case where disassortativity is structurally induced and in other cases where it is mechanistically driven. We conclude by emphasizing on ruling out structural disassortativity in future research, prior to investigating mechanisms underlying disassortativity in networks

Pedestrian gap acceptance behavior in street designs with elements of shared space

Recent developments in the field of urban street design have seen the emergence of the concept of “shared space,” a term that refers to a range of streetscape treatments aiming at creating a better public realm by asserting the function of streets as places and designing more to a scale aimed at easier pedestrian movement and lower vehicle speeds. In light of this shift in focus toward the pedestrian, an examination was done on the aspect of pedestrian gap acceptance behavior and how this may have changed as a result of the implementation of street layouts with elements of shared space. With the use of video data from London’s Exhibition Road site during periods before and after its conversion from a conventional dual carriageway to a layout featuring a number of elements of shared space, the study looked at changes in key gap acceptance variables, such as waiting time, crossing time, crossing speed, and critical gap. The effects of several traffic- and pedestrian-specific attributes on gap acceptance were also investigated by means of binary logistic regression modeling. Results suggest that pedestrians felt more comfortable and confident in their interaction with vehicles post-redevelopment of the site because they not only tended to accept shorter gaps in traffic but also appeared to be more at ease when crossing. In particular, elderly people and pedestrians traveling with children seemed to benefit the most, no longer appearing to be any less comfortable when crossing the road than other pedestrians

Introducing pattern graph rewriting in novel spatial aggregation procedures for a class of traffic assignment models

In this study two novel spatial aggregation methods are presented compatible with a class of traffic assignment models. Both methods are formalized using a category theoretical approach. While this type of formalization is new to the field of transport, it is well known in other fields that require tools to allow for reasoning on complex structures. The method presented stems from a method originally developed to deal with quantum physical processes. The first benefit of adopting this formalization technique is that it provides an intuitive graphical representation while having a rigorous mathematical underpinning. Secondly, it bears close resemblances to regular expressions and functional programming techniques giving insights in how to potentially construct solvers (i.e. algorithms). The aggregation methods proposed in this paper are compatible with traffic assignment procedures utilising a path travel time function consisting out of two components, namely (i) a flow invariant component representing free flow travel time, and (ii) a flow dependent component representing queuing delays. By exploiting the fact that, in practice, most large scale networks only have a small portion of the network exhibiting queuing delays, this method aims at decomposing the network into a constant free flowing part to compute once and a, much smaller, demand varying delay part that requires recomputation across demand scenarios. It is demonstrated that under certain conditions this procedure is lossless. On top of the decomposition method, a path set reduction method is proposed. This method reduces the path set to the minimal path set which further decreases computational cost. A large scale case study is presented to demonstrate the proposed methods can reduce computation times to less than 5% of the original without loss of accuracy

An efficient event‐based algorithm for solving first order dynamic network loading problems

In this paper we will present a novel solution algorithm for the Generalised Link Transmission Model (G-LTM). It will utilise a truly event based approach supporting the generation of exact results, unlike its time discretised counterparts. Furthermore, it can also be configured to yield approximate results, when this approach is adopted its computational complexity decreases dramatically. It will be demonstrated on a theoretical as well as a real world network that when utilising fixed periods of stationary demands to mimic departure time demand fluctuations, this novel approach can be efficient while maintaining a high level of result accuracy. The link model is complemented by a generic node model formulation yielding a proper generic first order DNL solution algorithm

A lossless spatial aggregation procedure for a class of capacity constrained traffic assignment models incorporating point queues

In this paper two novel spatial aggregation procedures are proposed. A network aggregation procedure based on a travel time delay decomposition method and a zonal aggregation procedure based on a path redistribution scheme. The effectiveness of these procedures lies in the fact that they, unlike existing aggregation methods, exploit available information regarding the application context and the characteristics of the adopted traffic assignment procedure. The context considered involves all applications that require path and inter-zonal travel times as output. A typical example of such applications are quick-scan methods, which have become increasing popular in recent years. The proposed procedures are compatible with a class of traffic assignment procedures incorporating (residual) point queues. Furthermore, one can choose to combine network aggregation with zonal aggregation to increase the effectiveness of the procedure. Results are demonstrated via theoretical examples as well as a large-scale case study. In the case study it is shown that network loading times can be reduced to as little as 4% of the original situation without suffering any information loss

Long-range collision avoidance for shared space simulation based on social forces

Shared space is an innovative approach to improve environments where both pedestrians and vehicles are present, with integrated layouts to balance priority. The Social Force Model (SFM) was used to visualise pedestrian and car trajectories so that peaks of density and pressure at critical locations are avoided. This paper extends the SFM to consider a long-range collision detection and collision resolution strategy. The determination of potential conflicts is enhanced using principle component analysis for a set of agent's prior speeds and directions. This long-range collision avoidance strategy results in more realistic SFM-based trajectories for pedestrians and cars in shared spaces

The impact of the congestion charge on retail: the London experience

The effect of London's congestion charge on the retail sector has aroused considerable interest since the introduction of the scheme in February 2003. We investigate the impact of the congestion charge using a variety of econometric models applied to a total retail sales index for central London (monthly) and weekly retail sales data for the John Lewis Oxford Street store within the congestion charging zone. The analysis suggests that the charge had a significant impact on sales at the John Lewis Oxford Street store over the period studied. However, it also suggests the charge did not affect overall retail sales in central London, an area larger than but encompassing the congestion charging zone

A network science approach to analysing manufacturing sector supply chain networks: Insights on topology

Due to the increasingly complex nature of the modern supply chain networks (SCNs), a recent research trend has focussed on modelling SCNs as complex adaptive systems. Despite the substantial number of studies devoted to such hypothetical modelling efforts, studies analysing the topological properties of real world SCNs have been relatively rare, mainly due to the scarcity of data. This paper aims to analyse the topological properties of twenty-six SCNs from the manufacturing sector. Moreover, this study aims to establish a general set of topological characteristics that can be observed in real world SCNs from the manufacturing sector, so that future theoretical work modelling the growth of SCNs in this sector can mimic these observations. It is found that the manufacturing sector SCNs tend to be scale free with degree exponents below two, tending towards hub and spoke configuration, as opposed to most other scale-free networks which have degree exponents above two. This observation becomes significant, since the importance of the degree exponent threshold of two in shaping the growth process of networks is well understood in network science. Other observed topological characteristics of the SCNs include disassortative mixing (in terms of node degree as well as node characteristics) and high modularity. In some networks, we find that node centrality is strongly correlated with the value added by each node to the supply chain. Since the growth mechanism that is most widely used to model the evolution of SCNs, the Barabasi - Albert model, does not generate scale-free topologies with degree exponent below two, it is concluded that a novel mechanism to model the growth of SCNs is required to be developed