Payame Noor University (PNU) Control and Optimization in Applied Mathematics 2383-3130 Articles in Press 2026 08 10 Multi-Objective Shift Scheduling Optimization for Gas Pressure Reduction Stations: A Crow Search Algorithm and Gray Wolf Optimizer Approach 13243 10.30473/coam.2026.76630.1365 EN Mohammad Javad Pahlevanzadeh Department of Management and Accounting, College of Farabi, University of Tehran, Iran Mohammad Reza Fathi Department of Management and Accounting, College of Farabi, University of Tehran, Iran Amirhossein Saffarinia Department of Management and Accounting, College of Farabi, University of Tehran, Iran 0009-0005-9523-7354 Journal Article 2025 11 24 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. https://mathco.journals.pnu.ac.ir/article_13243_342a494e3fdc8ff05cfa2d6ce3b9adb8.pdf