Operations Research 2025
Abstract Submission

2323. Distributed workforce rostering under distance based fairness constraints

Invited abstract in session WE-5: Multiobjective Decision Making with Uncertainty, Risk and Fairness Considerations, stream Decision Theory and Multi-criteria Decision Making.

Wednesday, 16:30-18:00
Room: H7

Authors (first author is the speaker)

1. Martin Scheffler
Chair of Business Management, especially Industrial Management, TU Dresden
2. Daniel Miodowski
Chair of Business Administration, esp. Industrial Management, Dresden University of Technology
3. Emilia Dytko
TU Dresden

Abstract

We address a distributed workforce rostering problem arising in the railway construction industry, where both operational efficiency and fairness in workforce allocation are essential. We propose a mixed-integer programming model that integrates typical industry constraints such as worker-task compatibility and location-based dependencies, accounting for workers’ travel-distance-related preferences.

Several spatial fairness metrics are analyzed, including (i) total travel distance, (ii) maximum individual travel distance, and (iii) the range between the shortest and longest individual travel distances. These spatial objectives are then combined with classical fairness objectives, particularly the equitable distribution of total working hours among employees over the planning horizon.

We explore the trade-offs between these fairness criteria using Pareto frontiers to provide planners with transparent, data-driven decision support. To reflect the decentralized structure and high volume of concurrent orders in the construction industry, we use real-world data from railway construction projects provided by a German infrastructure company. Our computational results demonstrate that fairness-aware distributed scheduling is not only scalable but can significantly enhance equity among workers without sacrificing overall operational efficiency.

Keywords

Status: accepted

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