Movie scenes scheduling and rescheduling with two parallel film-shooting crews

Date

2025-8-25

Author

Dedeoğlu, Bora

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This study addresses the movie scenes scheduling problem (MSSP) and rescheduling problem for two parallel film units shooting scenes simultaneously at disjoint sets in different locations. To reflect the dynamics of real-life film productions, the model incorporates precedence relations to organize the shooting sequence of scenes at each set and establish connections between them, as well as forced relations for dependencies that cannot be captured by precedence relations. A cost structure is implemented in which some actors receive fixed salaries, while others are paid daily wages. In addition, blocking times indicating actor unavailability periods and time windows specifying shooting periods of scenes are considered. The objective of scheduling is to generate schedules with minimum cost. For this purpose, a mixed-integer linear programming (MILP) model is developed. To account for all possible disruptions that may occur during film production, the rescheduling problem is examined as well. A bi-objective MILP model is developed to minimize both the total absolute deviation of scenes completion times from the previous schedule and the cost objective, aiming to maintain schedule stability at the minimum possible cost. The rescheduling problem is solved using lexicographic and ε-constraint approaches to explore the trade-offs between the two objectives. A total of 42 instances are generated for the scheduling problem. For the rescheduling problem, 5 instances involving different solution strategies are analyzed using 85 MILP model runs. All problem instances are solved using the Gurobi Solver in Python. Optimal solutions are obtained in very short computational times for both scheduling and rescheduling problems.

Subject Keywords

Movie scenes scheduling, Rescheduling, Mixed-integer linear programming, Dedicated parallel machines scheduling, Bi-objective optimization

URI

https://hdl.handle.net/11511/116075

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Graduate School of Natural and Applied Sciences, Thesis