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Optimizing a Heritage Railway Provider’s Volunteer Workforce Allocation: The Case of Swanage Railways

Author

Listed:
  • Nikila Suresh

    (School of Mathematical Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom)

  • Bismark Singh

    (School of Mathematical Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom)

Abstract

Heritage railways are railway operations that are not mainstream and serve as a reminiscence of past railways to build a cultural and national identity. They are among the main forms of heritage tourism. The heritage railway industry differs fundamentally from other forms of tourism and travel because of the significantly large fraction of volunteers in its workforce; additionally, heritage railways are of an extremely smaller scale than mainstream railways, with lower annual revenues. Although the regular railway industry extensively uses mathematical decision-making technologies for its daily operations, there is little evidence and few case studies demonstrating such value for heritage railways. Among the first of such studies, we present our experiences in using mathematical optimization models that improved the workforce allocation at a premier UK-based heritage railway company: Swanage Railways. Our collaboratively developed optimization models show four hours of reduction in weekly overtime for some employees during emergencies. If volunteers are efficiently integrated into the workforce, we find a reduction in overall workload by 26.7% for some of the existing employees. Finally, our models present a potential to reduce staffing costs by up to 35% if an hourly wage system is used instead of a fixed salary system.

Suggested Citation

  • Nikila Suresh & Bismark Singh, 2025. "Optimizing a Heritage Railway Provider’s Volunteer Workforce Allocation: The Case of Swanage Railways," Interfaces, INFORMS, vol. 55(4), pages 375-382, July.
  • Handle: RePEc:inm:orinte:v:55:y:2025:i:4:p:375-382
    DOI: 10.1287/inte.2024.0160
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    References listed on IDEAS

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    1. Askenazy, Philippe, 2014. "The Parameters of a National Minimum Hourly Wage," CEPREMAP Working Papers (Docweb) 1409, CEPREMAP.
    2. Silke Jütte & Marc Albers & Ulrich W. Thonemann & Knut Haase, 2011. "Optimizing Railway Crew Scheduling at DB Schenker," Interfaces, INFORMS, vol. 41(2), pages 109-122, April.
    3. Gemma Berenguer & William B. Haskell & Lei Li, 2024. "Managing Volunteers and Paid Workers in a Nonprofit Operation," Management Science, INFORMS, vol. 70(8), pages 5298-5316, August.
    4. David Lesaint & Christos Voudouris & Nader Azarmi, 2000. "Dynamic Workforce Scheduling for British Telecommunications plc," Interfaces, INFORMS, vol. 30(1), pages 45-56, February.
    5. Brian Roth & Anantaram Balakrishnan & Pooja Dewan & April Kuo & Dasaradh Mallampati & Juan Morales, 2018. "Crew Decision Assist: System for Optimizing Crew Assignments at BNSF Railway," Interfaces, INFORMS, vol. 48(5), pages 436-448, October.
    6. Vickerman, Roger, 2021. "Will Covid-19 put the public back in public transport? A UK perspective," Transport Policy, Elsevier, vol. 103(C), pages 95-102.
    7. Yang, Lixing & Li, Keping & Gao, Ziyou & Li, Xiang, 2012. "Optimizing trains movement on a railway network," Omega, Elsevier, vol. 40(5), pages 619-633.
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