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Estimation of the workload boundary in socio-technical infrastructure management systems: The case of Belgian railroads

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  • Topcu, Taylan G.
  • Triantis, Konstantinos
  • Roets, Bart

Abstract

Infrastructure systems are large-scale complex socio-technical systems that rely on humans for their safety critical decision-making activities. In the case of railroad networks, hierarchical organizations denoted as traffic control centers (TCCs) operate 24/7 in order to maintain successful network operations. Interacting social and technical factors influence TCC operational environments and thus the overall performance of the railroad system. This research presents a novel data envelopment analysis (DEA) application along with its implementation and validation by investigating the workload boundary of human performance through a case study built for the Belgian railway (INFRABEL) TCCs. We pursue two research foci. The first is to identify organizational, socio-economic, and technical factors that describe the performance environments in which TCC personnel operate. We use these factors to determine relatively homogeneous performance environments using multivariate statistical methods. The second focus is to design and implement on-site a socio-technical performance measurement framework, based on a new and unique dataset at the workstation level that is capable of considering socio-technical heterogeneity. Our approach consists of three steps. First, we apply a two-stage clustering approach to generate statistically relatively homogeneous groups. Second, we calculate meta - and in-cluster efficiency scores. Finally, we assess the validity of our results with INFRABEL. Results reveal three insights: (i) efficiency improvement strategies require further investigation based on temporal trends; (ii) disregarding performance environment heterogeneity leads to over estimation in target setting; and (iii) socio-technical system design could be informed by applying DEA, provided that, domain specific expertise is used in the model formulation.

Suggested Citation

  • Topcu, Taylan G. & Triantis, Konstantinos & Roets, Bart, 2019. "Estimation of the workload boundary in socio-technical infrastructure management systems: The case of Belgian railroads," European Journal of Operational Research, Elsevier, vol. 278(1), pages 314-329.
    • Handle: RePEc:eee:ejores:v:278:y:2019:i:1:p:314-329
    DOI: 10.1016/j.ejor.2019.04.009
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    Cited by:

    1. Laurens Cherchye & Bram De Rock & Dieter Saelens & Marijn Verschelde & Bart Roets, 2022. "Performance Analysis with Unobserved Inputs: An Application to Endogenous Automation in Railway Traffic Management," Working Papers ECARES 2022-06, ULB -- Universite Libre de Bruxelles.
    2. Sobrie, Léon & Verschelde, Marijn & Hennebel, Veerle & Roets, Bart, 2023. "Capturing complexity over space and time via deep learning: An application to real-time delay prediction in railways," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1201-1217.
    3. Monge, Juan F. & Ruiz, José L., 2023. "Setting closer targets based on non-dominated convex combinations of Pareto-efficient units: A bi-level linear programming approach in Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 311(3), pages 1084-1096.
    4. An, Qingxian & Tao, Xiangyang & Chen, Xiaohong, 2023. "Nested frontier-based best practice regulation under asymmetric information in a principal–agent framework," European Journal of Operational Research, Elsevier, vol. 306(1), pages 269-285.
    5. Taylan G. Topcu & Konstantinos Triantis, 2022. "An ex-ante DEA method for representing contextual uncertainties and stakeholder risk preferences," Annals of Operations Research, Springer, vol. 309(1), pages 395-423, February.
    6. Dai, Qianzhi & Li, Yongjun & Lei, Xiyang & Wu, Dengsheng, 2021. "A DEA-based incentive approach for allocating common revenues or fixed costs," European Journal of Operational Research, Elsevier, vol. 292(2), pages 675-686.

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