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Computational considerations in building inter-firm networks

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  • Johan Joubert
  • Sumarie Meintjes

Abstract

We rarely associate social networks with the movement of freight vehicles. Yet, taking a network perspective on supply chains has seen a strong interest in recent literature. It allows for a variety of system-level analysis that is not possible when taking a single focal firm view as is often the case in more classical supply chain approaches. Creating the network of connectivity on which the analyses are based can be quite a daunting and computationally challenging task. In this paper we create a large-scale network from the movement of commercial vehicles in a metropolitan area in South Africa, using the direct trip between consecutive facilities as a proxy for a tie, or dyad, in the network. We analyse how density-based clustering parameters influence the completeness of the network—that is the number of nodes included—as well as the computational burden of extracting the network. The results of the multi-objective analysis confirm the sensitivity of the resulting network, and suggest much smaller search radii and fewer points per cluster. We also report on a number of node- and network-level properties of the complex network using the proposed clustering configuration on the Nelson Mandela Bay Metropole network in South Africa. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Johan Joubert & Sumarie Meintjes, 2015. "Computational considerations in building inter-firm networks," Transportation, Springer, vol. 42(5), pages 857-878, September.
    • Handle: RePEc:kap:transp:v:42:y:2015:i:5:p:857-878
    DOI: 10.1007/s11116-015-9650-x
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    References listed on IDEAS

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    1. Dugundji, Elenna R. & Páez, Antonio & Arentze, Theo A. & Walker, Joan L. & Carrasco, Juan A. & Marchal, Fabrice & Nakanishi, Hitomi, 2011. "Transportation and social interactions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(4), pages 239-247, May.
    2. Joubert, J.W. & Axhausen, K.W., 2011. "Inferring commercial vehicle activities in Gauteng, South Africa," Journal of Transport Geography, Elsevier, vol. 19(1), pages 115-124.
    3. Johan Joubert & Kay Axhausen, 2013. "A complex network approach to understand commercial vehicle movement," Transportation, Springer, vol. 40(3), pages 729-750, May.
    4. Sharman, Bryce W. & Roorda, Matthew J., 2013. "Multilevel modelling of commercial vehicle inter-arrival duration using GPS data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 56(C), pages 94-107.
    5. Mukherjee, Satyam, 2014. "Quantifying individual performance in Cricket — A network analysis of batsmen and bowlers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 624-637.
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    1. Joubert, Johan W. & Meintjes, Sumarie, 2015. "Repeatability & reproducibility: Implications of using GPS data for freight activity chains," Transportation Research Part B: Methodological, Elsevier, vol. 76(C), pages 81-92.
    2. Viljoen, Nadia M. & Joubert, Johan W., 2019. "Supply chain micro-communities in urban areas," Journal of Transport Geography, Elsevier, vol. 74(C), pages 211-222.
    3. Trent, Nadia M. & Joubert, Johan W., 2022. "Logistics sprawl and the change in freight transport activity: A comparison of three measurement methodologies," Journal of Transport Geography, Elsevier, vol. 101(C).
    4. Frank Goetzke & Regine Gerike & Antonio Páez & Elenna Dugundji, 2015. "Social interactions in transportation: analyzing groups and spatial networks," Transportation, Springer, vol. 42(5), pages 723-731, September.

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