Abstract
The resource constraint job scheduling problem considered in this work is a difficult optimization problem that was defined in the context of the transportation of minerals from mines to ports. The main characteristics are that all jobs share a common limiting resource and that the objective function concerns the minimization of the total weighted tardiness of all jobs. The algorithms proposed in the literature for this problem have a common disadvantage: they require a huge amount of computation time. Therefore, the main goal of this work is the development of an algorithm that can compete with the state of the art, while using much less computational resources. In fact, our experimental results show that the biased random key genetic algorithm that we propose significantly outperforms the state-of-the-art algorithm from the literature both in terms of solution quality and computation time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Almeida, B.F., Correia, I., Saldanha-da Gama, F.: A biased random-key genetic algorithm for the project scheduling problem with flexible resources. Top 26(2), 283–308 (2018)
Ballestin, F., Trautmann, N.: An iterated-local-search heuristic for the resource-constrained weighted earliness-tardiness project scheduling problem. Int. J. Prod. Res. 46, 6231–6249 (2008)
Bertsekas, D.P., Tsitsiklis, J.N., Wu, C.: Rollout algorithms for combinatorial optimization. Journal of Heuristics 3(3), 245–262 (1997)
Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. 35, 268–308 (2003)
Brent, O., Thiruvady, D., Gómez-Iglesias, A., Garcia-Flores, R.: A parallel lagrangian-ACO heuristic for project scheduling. In: IEEE Congress on Evolutionary Computation (CEC 2014), pp. 2985–2991. IEEE (2014)
Brucker, P., Drexl, A., Möhring, R., Neumann, K., Pesch, E.: Resource-constrained project scheduling: notation, classification, models, and methods. Eur. J. Oper. Res. 112, 3–41 (1999)
Cohen, D., Gómez-Iglesias, A., Thiruvady, D., Ernst, A.T.: Resource constrained job scheduling with parallel constraint-based ACO. In: Wagner, M., Li, X., Hendtlass, T. (eds.) ACALCI 2017. LNCS (LNAI), vol. 10142, pp. 266–278. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-51691-2_23
Demeulemeester, E., Herroelen, W.: Project Scheduling: A Research Handbook. Kluwer, Boston (2002)
Ernst, A.T., Singh, G.: Lagrangian particle swarm optimization for a resource constrained machine scheduling problem. In: Li, X. (ed.) 2012 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE, Institute of Electrical and Electronics Engineers, United States (2012). https://doi.org/10.1109/CEC.2012.6256177
de Faria Alixandre, B.F., Dorn, M.: D-BRKGA: a distributed biased random-key genetic algorithm. In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 1398–1405 (2017)
Gonçalves, J.F., Resende, M.G.C.: Biased random-key genetic algorithms for combinatorial optimization. J. Heuristics 17(5), 487–525 (2011)
Júnior, B., Pinheiro, P., Coelho, P.: A parallel biased random-key genetic algorithm with multiple populations applied to irregular strip packing problems. Math. Probl. Eng. 2017, 1–11 (2017). https://doi.org/10.1155/2017/1670709
Lawler, E.L.: A “pseudopolynomial” algorithm for sequencing jobs to minimize total tardiness. Ann. Discrete Math. 1, 331–342 (1977)
López-Ibáñez, M., Dubois-Lacoste, J., Pérez Cáceres, L., Birattari, M., Stützle, T.: The irace package: iterated racing for automatic algorithm configuration. Oper. Res. Perspect. 3, 43–58 (2016)
Marriott, K., Stuckey, P.: Programming with Constraints. MIT Press, Cambridge (1998)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1998)
Neumann, K., Schwindt, C., Zimmermann, J.: Project Scheduling with Time Windows and Scarce Resources. Springer, Berlin (2003)
Nguyen, S., Thiruvady, D., Ernst, A.T., Alahakoon, D.: A hybrid differential evolution algorithm with column generation for resource constrained job scheduling. Comput. Oper. Res. 109, 273–287 (2019)
Pinto, B.Q., Ribeiro, C.C., Rosseti, I., Plastino, A.: A biased random-key genetic algorithm for the maximum quasi-clique problem. Eur. J. Oper. Res. 271(3), 849–865 (2018)
Singh, G., Ernst, A.T.: Resource constraint scheduling with a fractional shared resource. Oper. Res. Lett. 39(5), 363–368 (2011)
Thiruvady, D., Singh, G., Ernst, A.T., Meyer, B.: Constraint-based ACO for a shared resource constrained scheduling problem. Int. J. Prod. Econ. 141(1), 230–242 (2012)
Thiruvady, D., Singh, G., Ernst, A.T.: Hybrids of integer programming and ACO for resource constrained job scheduling. In: Blesa, M.J., Blum, C., Voß, S. (eds.) HM 2014. LNCS, vol. 8457, pp. 130–144. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07644-7_10
Thiruvady, D., Wallace, M., Gu, H., Schutt, A.: A lagrangian relaxation and ACO hybrid for resource constrained project scheduling with discounted cash flows. J. Heuristics 20(6), 643–676 (2014)
Thiruvady, D., Blum, C., Ernst, A.T.: Maximising the net present value of project schedules using CMSA and parallel ACO. In: Blesa Aguilera, M.J., Blum, C., Gambini Santos, H., Pinacho-Davidson, P., Godoy del Campo, J. (eds.) HM 2019. LNCS, vol. 11299, pp. 16–30. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05983-5_2
Thiruvady, D., Blum, C., Ernst, A.T.: Solution merging in metaheuristics for resource constrained job scheduling (2019, working paper)
Wolsey, L.A.: Integer Programming. Wiley-Interscience, New York (1998)
Acknowledgements
This work was partially funded by the Doctoral Program “Vienna Graduate School on Computational Optimization”, Austrian Science Foundation (FWF) Project No. W1260-N35. Moreover, this work was partially supported by the EU H2020 Research and Innovation Program under the LOGISTAR project (Grant Agreement No. 769142).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Blum, C., Thiruvady, D., Ernst, A.T., Horn, M., Raidl, G.R. (2019). A Biased Random Key Genetic Algorithm with Rollout Evaluations for the Resource Constraint Job Scheduling Problem. In: Liu, J., Bailey, J. (eds) AI 2019: Advances in Artificial Intelligence. AI 2019. Lecture Notes in Computer Science(), vol 11919. Springer, Cham. https://doi.org/10.1007/978-3-030-35288-2_44
Download citation
DOI: https://doi.org/10.1007/978-3-030-35288-2_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-35287-5
Online ISBN: 978-3-030-35288-2
eBook Packages: Computer ScienceComputer Science (R0)