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, Volume 23, Issue 3, pp 685–702 | Cite as

Optimization in dubbing scheduling

  • Nieves R. Brisaboa
  • Luisa Carpente
  • Ana Cerdeira-Pena
  • Silvia Lorenzo-Freire
Original Paper

Abstract

One of the main tasks in dubbing studios is to design good schedules to assign actors/actresses to dubbing sessions. This paper provides an effective tool based on the simulated annealing philosophy. The performance of the proposed heuristic is guaranteed by a binary linear programming model (BP model). By relaxing some integrality conditions in the BP model, we can achieve optimal schedules in real instances gathered from several dubbed films. Yet, in most cases, it is not possible to obtain these optimal schedules in a suitable computational time. On the contrary, the heuristic algorithm gets high quality solutions (and even the optimal ones) in just few seconds.

Keywords

Dubbing Scheduling Simulated annealing Binary linear programming 

Mathematics Subject Classification

90Cxx 90C10 90C59 

References

  1. Aarts EHL, Korst JHM, van Laarhoven PJM (1997) Simulated annealing. In: Aarts E, Lenstra JK (eds) Local search in combinatorial optimization. Wiley, New York, pp 91–120Google Scholar
  2. Álmos A (1998) Scheduling algorithms for a video-dubbing studio. In: INTCOM’98, MiskolcGoogle Scholar
  3. Anagnostopoulos A, Michel L, van Hentenryck P (2006) A simulated annealing approach to the traveling tournament problem. J Sched 9:177–193CrossRefGoogle Scholar
  4. Bouleimen K, Lecocq H (2003) A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem and its multiple mode version. Eur J Oper Res 149:268–281CrossRefGoogle Scholar
  5. Eglese RW (1990) Simulated annealing: a tool for operational research. Eur J Oper Res 46:271–281CrossRefGoogle Scholar
  6. Gunawan A, Ng KM, Poh KL (2012) A hybridized Lagrangian relaxation and simulated annealing method for the course timetabling problem. Comput Oper Res 39:3074–3088CrossRefGoogle Scholar
  7. Kendall G, Knust S, Ribeiro CC, Urrutia S (2010) Scheduling in sports: an annotated bibliography. Comput Oper Res 37:1–19CrossRefGoogle Scholar
  8. Kim KH, Moon KC (2003) Berth scheduling by simulated annealing. Transp Res Part B 37:541–560Google Scholar
  9. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680CrossRefGoogle Scholar
  10. Parr D, Thompson JM (2007) Solving the multi-objective nurse scheduling problem with a weighted cost function. Ann Oper Res 155:279–288CrossRefGoogle Scholar
  11. Thompson JM, Dowsland KA (1998) A robust simulated annealing based examination timetabling system. Comput Oper Res 25:637–648CrossRefGoogle Scholar
  12. van Laarhoven PJM, Aarts EHL, Lenstra JK (1992) Job shop scheduling by simulated annealing. Oper Res 40:113–125CrossRefGoogle Scholar
  13. Willis RJ, Terrill BJ (1994) Scheduling the Australian state cricket season using simulated annealing. J Oper Res Soc 45:276–280Google Scholar

Copyright information

© Sociedad de Estadística e Investigación Operativa 2015

Authors and Affiliations

  • Nieves R. Brisaboa
    • 1
  • Luisa Carpente
    • 2
  • Ana Cerdeira-Pena
    • 1
  • Silvia Lorenzo-Freire
    • 2
  1. 1.Database Laboratory, Department of Computer Science, Faculty of Computer ScienceUniversity of A CoruñaA CoruñaSpain
  2. 2.MODES Research Group, Department of Mathematics, Faculty of Computer ScienceUniversity of A CoruñaA CoruñaSpain

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