An efficient implementation of a VNS heuristic for the weighted fair sequences problem
Bruno J.S. Pessoa
Centro de Informática, Universidade Federal da Paraíba, João Pessoa, 58055-000 Brazil
Search for more papers by this authorCorresponding Author
Daniel Aloise
GERAD and Polytechnique Montréal, Montreal, H3T 1J4 Canada
Corresponding author.
Search for more papers by this authorLucidio A. Cabral
Centro de Informática, Universidade Federal da Paraíba, João Pessoa, 58055-000 Brazil
Search for more papers by this authorBruno J.S. Pessoa
Centro de Informática, Universidade Federal da Paraíba, João Pessoa, 58055-000 Brazil
Search for more papers by this authorCorresponding Author
Daniel Aloise
GERAD and Polytechnique Montréal, Montreal, H3T 1J4 Canada
Corresponding author.
Search for more papers by this authorLucidio A. Cabral
Centro de Informática, Universidade Federal da Paraíba, João Pessoa, 58055-000 Brazil
Search for more papers by this authorAbstract
In the weighted fair sequences problem (WFSP), one aims to schedule a set of tasks or activities sthat the maximum product between the largest temporal distance between two consecutive executions of a task and its priority is minimized. The WFSP covers a large number of applications in different areas, ranging from automobile production on a mixed-model assembly line to the sequencing of interactive applications to be aired in a digital TV environment. This paper proposes an iterative heuristic method for the WFSP centered on an efficient implementation of a variable neighborhood search heuristic. Computational experiments on benchmark instances show that the proposed metaheuristic outperforms the state-of-the-art method proposed to the problem, obtaining comparable solution values in much less computational time.
References
- Acharya, S., Alonso, R., Franklin, M., Zdonik, S., 1995. Broadcast disks: data management for asymmetric communication environments. SIGMOD Record 24, 2, 199–210.
10.1145/568271.223816 Google Scholar
- Anily, S., Glass, C.A., Hassin, R., 1998. The scheduling of maintenance service. Discrete Applied Mathematics 82, 1, 27–42.
- Audsley, N.C., Burns, A., Davis, R.I., Tindell, K.W., Wellings, A.J., 1995. Fixed priority pre-emptive scheduling: an historical perspective. Real-Time Systems 8, 2, 173–198.
- Baker, K., 1974. Introduction to Sequencing and Scheduling. Wiley, Hoboken, NJ.
- Baker, K.R., Trietsch, D., 2009. Principles of Sequencing and Scheduling. Wiley, Hoboken, NJ.
10.1002/9780470451793 Google Scholar
- Bar-Noy, A., Bhatia, R., Naor, J.S., Schieber, B., 2002. Minimizing service and operation costs of periodic scheduling. Mathematics of Operations Research 27, 3, 518–544.
- Bar-Noy, A., Ladner, R.E., 2003. Windows scheduling problems for broadcast systems. SIAM Journal on Computing 32, 4, 1091–1113.
- Baruah, S.K., 1995. Fairness in periodic real-time scheduling. Proceedings of the 16th IEEE Real-Time Systems Symposium. IEEE Computer Society, Washington, DC.
- Baruah, S.K., Cohen, N.K., Plaxton, D.A. C. G.and Varvel, 1996. Proportionate progress: a notion of fairness in resource allocation. Algorithmica 15, 6, 600–625.
- Bollapragada, S., Bussieck, M.R., Mallik, S., 2004. Scheduling commercial videotapes in broadcast television. Operations Research 52, 5, 679–689.
- Brucker, P., 2010. Scheduling Algorithms ( 5th edn). Springer, Berlin.
- Chan, M.Y., Chin, F., 1993. Schedulers for larger classes of pinwheel instances. Algorithmica 9, 5, 425–462.
- Cheng, S.C., Stankovic, J.A., Ramamritham, K., 1989. Scheduling algorithms for hard real-time systems: a brief survey. In Tutorial: Hard Real-Time Systems. IEEE Computer Society Press, Los Alamitos, CA, pp. 150–173.
- Coppersmith, D., Lee, J., Leung, J., 1999. A polytope for a product of real linear functions in 0/1 variables. IBM research report RC21568. Available at https://citeseerx.ist.psu.edu/viewdoc/download.
- Corominas, A., García-Villoria, A., Pastor, R., 2008. Solving the response time variability problem by means of multi-start and grasp metaheuristics. Proceedings of the 2008 Conference on Artificial Intelligence Research and Development: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence. IOS Press, Amsterdam, The Netherlands, pp. 128–137.
- Corominas, A., Kubiak, W., Palli, N.M., 2007. Response time variability. Journal of Scheduling 10, 2, 97–110.
- Corominas, A., Kubiak, W., Pastor, R., 2010. Mathematical programming modeling of the response time variability problem. European Journal of Operational Research 200, 2, 347–357.
- Costa, L.R., Aloise, D., Mladenović, N., 2017. Less is more: basic variable neighborhood search heuristic for balanced minimum sum-of-squares clustering. Information Sciences 415, 247–253.
- Dhamala, T.N., Kubiak, W., 2005. A brief survey of just-in-time sequencing for mixed-model systems. International Journal of Operations Research 2, 2, 38–47.
- Erhard, M., Schoenfelder, J., Fügener, A., Brunner, J.O., 2018. State of the art in physician scheduling. European Journal of Operational Research 265, 1, 1–18.
- García-Villoria, A., Salhi, S., 2014. Scheduling commercial advertisements for television. International Journal of Production Research 53, 4, 1198–1215.
- Han, C., Lin, K., Hou, C., 1996. Distance-constrained scheduling and its applications to real-time systems. IEEE Transactions on Computers 45, 7, 814–826.
- Hansen, P., Mladenović, N., 2003. Variable Neighborhood Search. Springer US, Boston, MA, pp. 145–184.
- Hansen, P., Mladenović, N., Moreno Pérez, J.A., 2008. Variable neighbourhood search: methods and applications. 4OR 6, 4, 319–360.
- Hansen, P., Ruiz, M., Aloise, D., 2012. A vns heuristic for escaping local extrema entrapment in normalized cut clustering. Pattern Recognition 45, 12, 4337–4345.
- Holte, R., Mok, A., Rosier, L., Tulchinsky, I., Varvel, D., 1989. The pinwheel: a real-time scheduling problem. 22nd Hawaii International Conference on System Sciences, Kailua-Kona, pp. 693–702.
- Kenyonand, Schabanel, 2003. The data broadcast problem with non-uniform transmission times. Algorithmica 35, 2, 146–175.
- Kim, E.S., Glass, C.A., 2014. Perfect periodic scheduling for three basic cycles. Journal of Scheduling 17, 1, 47–65.
- Kimms, A., Muller-Bungart, M., 2007. Revenue management for broadcasting commercials: the channel's problem of selecting and scheduling the advertisements to be aired, 1, 28–44.
- Kubiak, W., 1993. Minimizing variation of production rates in just-in-time systems: a survey. European Journal of Operational Research 66, 3, 259–271.
- Kubiak, W., 2004. Fair sequences. In J.Y.-T. Leung (ed.) Handbook of Scheduling: Algorithms, Models and Performance Analysis. CRC Press, Boca Raton, FL.
- Kubiak, W., Sethi, S., 1994. Optimal just-in-time schedules for flexible transfer lines. International Journal of Flexible Manufacturing Systems 6, 2, 137–154.
10.1007/BF01328809 Google Scholar
- Lehoczky, J., Sha, L., Ding, Y., 1989. The rate monotonic scheduling algorithm: exact characterization and average case behavior. Proceedings. Real-Time Systems Symposium. IEEE, Piscataway, NJ, pp. 166–171.
- Leinbaugh, D.W., 1980. Guaranteed response times in a hard-real-time environment. IEEE Transactions on Software Engineering SE-6, 1, 85–91.
- Leung, J., Kelly, L., Anderson, J.H., 2004. Handbook of Scheduling: Algorithms, Models, and Performance Analysis. CRC Press, Boca Raton, FL.
10.1201/9780203489802 Google Scholar
- Leung, J., Merrill, M., 1980. A note on preemptive scheduling of periodic, real-time tasks. Information Processing Letters 11, 3, 115–118.
- Leung, J.Y.T., Whitehead, J., 1982. On the complexity of fixed-priority scheduling of periodic, real-time tasks. Performance Evaluation 2, 4, 237–250.
- Liu, C.L., Layland, J.W., 1973. Scheduling algorithms for multiprogramming in a hard-real-time environment. Journal of the ACM 20, 1, 46–61.
- Lourenço, H.R., Martin, O.C., Stützle, T., 2003. Iterated Local Search. Springer US, Boston MA, pp. 320–353.
- Miltenburg, J., 1989. Level schedules for mixed-model assembly lines in just-in-time production systems. Management Science 35, 2, 192–207.
- Mladenović, N., Alkandari, A., Pei, J., Todosijević, R., Pardalos, P.M., 2020. Less is more approach: basic variable neighborhood search for the obnoxious p-median problem. International Transactions in Operational Research 27, 1, 480–493.
- Mladenović, N., Hansen, P., 1997. Variable neighborhood search. Computers and Operations Research 24, 11, 1097–1100.
- Mladenović, N., Todosijević, R., Urošević, D., 2016. Less is more: basic variable neighborhood search for minimum differential dispersion problem. Information Sciences 326, 160–171.
- Monden, Y., 1983. Toyota Production System: Practical Approach to Production Management. Industrial Engineering and Management Press, Institute of Industrial Engineers, Norcross, GA, pp. 233–246.
- Morris, S., 2005. Interactive TV Standards: A Guide to MHP, OCAP, and JavaTV. Elsevier, San Diego, CA.
- Pessoa, B.J.S., Aloise, D., Cabral, L.A., 2018. The weighted fair sequences problem. Computers and Operations Research 91, Supplement C, 121–131.
10.1016/j.cor.2017.11.008 Google Scholar
- Pinedo, M.L., 2008. Scheduling: Theory, Algorithms, and Systems ( 3rd edn). Springer, Berlin.
- Ramamurthy, S., Moir, M., 2000. Static-priority periodic scheduling on multiprocessors. Proceedings 21st IEEE Real-Time Systems Symposium, pp. 69–78. Available at http://www.cs.unc.edu/techreports/01-016.pdf.
- Sha, L., Abdelzaher, T., årzén, K.E., Cervin, A., Baker, T., Burns, A., Buttazzo, G., Caccamo, M., Lehoczky, J., Mok, A.K., 2004. Real time scheduling theory: a historical perspective. Real-Time Systems 28, 2, 101–155.
- Sinnl, M., 2021. An iterative exact algorithm for the weighted fair sequences problem. https://doi.org/10.48550/arXiv.2108.03024.
10.48550/arXiv.2108.03024 Google Scholar
- Tari, F.G., Alaei, R., 2013. Scheduling TV commercials using genetic algorithms. International Journal of Production Research 51, 16, 4921–4929.
- Waldspurger, C.A., Weihl, W.E., 1995. Stride scheduling: deterministic proportional-share resource management. Technical report, Cambridge, MA.