Leave-Aware Multi-Skilled Crew Scheduling: A Bi-Objective Model and Evaluation of NSGA-II and Non-dominated Sorting Firefly Algorithm

Document Type : Original Article

Authors

1 MSc, Industrial Engineering Department, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran

2 Professor, Industrial Engineering Department, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran

10.22091/jemsc.2026.17220.1422

Abstract

Airline crew scheduling is a challenging optimization problem that affects operational cost, crew utilization, and service reliability. This study addresses multi-skilled cabin crew scheduling through a bi-objective integer programming formulation that considers leave-request fulfillment and crew employment cost. The first objective minimizes denied leave requests, while the second minimizes assignment costs together with penalties for exceeding the allowable leave level and cost savings associated with unused allowance. The model considers three cabin positions—purser, flight attendant, and substitute—and enforces position-specific daily staffing requirements, mutually exclusive daily assignments, and upper bounds on granted leave. Two multi-objective metaheuristics, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Non-dominated Sorting Firefly Algorithm (NSFA), are applied. A instance with 40 multi-skilled crew members and a 28-day planning horizon is evaluated using runtime, number of Pareto solutions, diversity metric (DM), and mean ideal distance (MID). Across five runs, NSGA-II requires substantially less computational time, with an average runtime of approximately 12 s compared with 116 s for NSFA. In contrast, NSFA achieves a higher average DM (0.294 versus 0.037) and a lower average MID (0.0012 versus 0.0045). The results demonstrate an instance-specific trade-off between computational efficiency and the measured characteristics of the obtained Pareto solutions.

Keywords

Main Subjects

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Journal of Engineering Management and Soft Computing

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Available Online from 01 October 2026

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