A hybrid meta-heuristic algorithm for optimization of crew scheduling

Crew scheduling problem is the problem of assigning crew members to the flights so that total cost is minimized while regulatory and legal restrictions are satisfied. The crew scheduling is an NP-hard constrained combinatorial optimization problem and hence, it cannot be exactly solved in a reasonable computational time. This paper presents a particle swarm optimization (PSO) algorithm synchronized with a local search heuristic for solving the crew scheduling problem. Recent studies use genetic algorithm (GA) or ant colony optimization (ACO) to solve large scale crew scheduling problems. Furthermore, two other hybrid algorithms based on GA and ACO algorithms have been developed to solve the problem. Computational results show the effectiveness and superiority of the proposed hybrid PSO algorithm over other algorithms.

.Figure optionsDownload as PowerPoint slideHighlight
► A particle swarm optimization (PSO) algorithm is proposed for solving the crew scheduling problem.
► PSO is incorporated with a local optimization heuristic based on the problem-specific knowledge of the search space.
► The proposed PSO algorithm is compared with other well-known metaheuristic algorithms.
► Computational results show the effectiveness and superiority of the proposed hybrid PSO algorithm.