Scheduling Problem of Movie Scenes Based on Three Meta-Heuristic Algorithms

Movie scenes scheduling problem (MSSP) is the NP-hard. It refers to the process of film shooting through the reasonable sequence of film scenes to minimize the total cost of film shooting. Studying scheduling problem of this kind is based on the location of the scene shooting, the cost of the scene transfer, the different remuneration of actors, and the different duration of the film scene shooting. The actors' waiting time and transfer cost during the shooting process are reduced as much as possible to make the total film shooting cost smaller. In this paper, an ILP(integer linear programming) model is established and a TABU search based method (TSBM), a particle swarm optimization based method (PSOBM) and an ant colony based method (ACOBM) are used to study the movie scenes scheduling problem. The objective is to compare and analyze relation performances of the three methods in the problem. By compared the experiment, the results show that TSBM, PSOBM and ACOBM can effectively reduce the total cost of film shooting. Comparison with the experiments show that the optimization result of ACOBM is better, the running time of TSBM is shorter, and the optimization result of TSBM is better than that of PSOBM. In addition, the running time of ACOBM is faster than that of PSOBM by setting the number of ants and the number of particles. The potential application of this study has great relevance for the optimization and ILP.