Abstract
Aviation ordnance handling is critical to the firepower projection of the time-critical cyclic flight operation on aircraft carriers. The complexity of the problem depends on the supply and demand features of ordnance. This paper examines the scheduling of aviation ordnance handling of an operational aircraft carrier under the framework of hybrid flow shop scheduling (HFS) and derives a method based on the simulated annealing (SA) algorithm to get the HFS problem's solution. The proposed method achieves the minimum possible flow time by optimizing the ordnance assignment through different stages. The traditional SA algorithm depends heavily on the heuristic scheme and consumes too much time to compute the optimal solution. To solve the problem, this paper improves the SA by embedding a task-based encoding method and a matrix perturbation method. The improved SA remains independent of the heuristic scheme and effectively propagates the local search process. Since the performance of SA is also influenced by its embedded parameters, orthogonal tests were carried out to carefully compare and select these parameters. Finally, different ordnance loading plans were simulated to reveal the advantage of the improved SA. The simulation results show that the improved SA (ISA) can generate better and faster solution than the traditional SA. This research provides a practical solution to stochastic HFS problems.
Highlights
Aviation ordnance handling is critical to the firepower projection of the time-critical cyclic flight operation on aircraft carriers. e complexity of the problem depends on the supply and demand features of ordnance. is paper examines the scheduling of aviation ordnance handling of an operational aircraft carrier under the framework of hybrid flow shop scheduling (HFS) and derives a method based on the simulated annealing (SA) algorithm to get the HFS problem’s solution. e proposed method achieves the minimum possible flow time by optimizing the ordnance assignment through different stages. e traditional SA algorithm depends heavily on the heuristic scheme and consumes too much time to compute the optimal solution
Introduction is study focuses on the ordnance dispatching scheduling problem observed onboard the aircraft carrier flight operation, which plays an important role in the air wing firepower projection in its sortie generation [1]. e ordnance handling process involves many stages, equipment, and hundreds of personnel operating in a limited work space [2]; finding an optimal dispatch scheduling for a given ordnance load plan, plus the time critical nature of cyclic flight window requires, is a challenging problem
Many general schemes on improving the performance of simple heuristics have been successfully developed, most of which are named as metaheuristics, such as genetic algorithm (GA) [14], ant colony optimization (ACO) [15], tabu search (TS) [16], neural networks (NN) [17], artificial immune systems (AIS) [18], and simulated annealing (SA) [19]. ey inhere with higher level of abilities in searching the vast solution space, which have better performance than the simple heuristic methods
Summary
Weapons Assembling. e ordnances are preassembled in the staging area of the hangar deck, with a sufficient lead time to meet the short turnaround time of the flight schedule. e assembling time TaKss varies with the types of ordnances. E ordnances are preassembled in the staging area of the hangar deck, with a sufficient lead time to meet the short turnaround time of the flight schedule. E assembling time TaKss varies with the types of ordnances. Note that the assembling time of the staff fluctuates in the real world. Erefore, the interval of assembling time was set to [−Tf1, Tf1]. E real assembling time is denoted as TaKss + t1, where t1 is a random number within [−Tf1, Tf1] Note that the assembling time of the staff fluctuates in the real world. erefore, the interval of assembling time was set to [−Tf1, Tf1]. e real assembling time is denoted as TaKss + t1, where t1 is a random number within [−Tf1, Tf1]
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