Document Type : Research Article
Authors
Department of Management and Accounting, College of Farabi, University of Tehran, Iran
10.30473/coam.2026.76630.1365
Abstract
This study presents a multi-objective mixed-integer linear programming (MILP) model for optimizing rotational shift scheduling of personnel at gas pressure reduction stations (CGSs) and administrative offices of the Gas Company of Qom Province, Iran. The model simultaneously addresses organizational requirements and human-centric factors, including employee shift preferences, coworker compatibility, sensitivity to mercaptan gas, public communication skills, commuting distances, and regulatory workload constraints. A two-phase solution framework is employed: the multi-objective problem is first scalarized using the ε-constraint method, and the resulting single-objective subproblems are then solved using the Gray Wolf Optimizer (GWO) and the Crow Search Algorithm (CSA). An MILP-solver-based ε-constraint baseline is also reported for benchmarking. A real-world case study involving 49 employees, 9 service locations, and a 30-day planning horizon is used to evaluate the framework. Results from 30 independent runs show that CSA obtains lower average overtime (44.2 h versus 48.6 h), higher shift-preference satisfaction (90.1% versus 87.3%), higher coworker-preference satisfaction (86.4% versus 83.6%), and shorter average runtime (109.8 s versus 121.4 s) than GWO. Wilcoxon signed-rank tests indicate statistically significant differences at the 5% level. Sensitivity analysis reveals the trade-off between individual shift preferences and coworker compatibility under alternative weight settings. The framework therefore provides a practical and adaptable decision-support approach for workforce planning in continuous and safety-critical gas distribution operations.
Highlights
- A multi-objective MILP model for rotational shift scheduling in gas distribution systems is proposed.
- Human-centric factors including mercaptan sensitivity and coworker preferences are formally modeled.
- The ε-constraint method systematically explores Pareto trade-offs among four competing objectives.
- The Crow Search Algorithm (CSA) statistically outperforms GWO across all key performance metrics.
- Gini coefficient analysis confirms equitable workload and commuting burden distribution among staff.
Keywords
- Personnel scheduling
- Gas pressure reduction station
- Multi-objective optimization
- Crow search algorithm
- Gray wolf optimizer
Main Subjects
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