An Iterated Local Search for the Talent Scheduling Problem with Location Costs

The talent scheduling problem seeks to determine the movie shooting sequence that minimizes the total cost of the actors involved, which usually accounts for a significant portion of the cost of any real-world movie production. This paper introduces an extension of the talent scheduling problem that takes into account both the costs of filming locations and actors. To better capture reality, we consider that the rental cost for a filming location can vary across the planning horizon. The objective is to find the shooting sequence as well as the start date for each scene that minimizes the total cost, including actor and location costs, while ensuring all scenes are completed within the planning horizon. We first formulate the problem as a mixed integer linear programming (MILP) model, from which small instances can be solved to optimality by MILP solvers. Next, an iterated local search heuristic that can efficiently solve larger instances is developed. Then we provide a new benchmark data set for our new variance of the talent scheduling problem. The results of computational experiments upon new benchmark instances suggest that our heuristic can outperform the MILP model solved by a commercial solver in terms of both solution quality and runtime.