Abstract
Genetic Programming (GP) is a well-known technique for generating dispatching rules for scheduling problems. A simple and cost-effective local search technique for many-objective combinatorial optimization problems is Pareto Local Search (PLS). With some success, researchers have looked at how PLS can be applied to many-objective evolutionary algorithms (MOEAs). Many MOEAs’performance can be considerably enhanced by combining local and global searches. Despite initial success, PLS’s practical application in GP still needs to be improved. The PLS is employed in the literature that uniformly distributes reference points. It is essential to maintain solution diversity when using evolutionary algorithms to solve many-objective optimization problems with disconnected and irregular Pareto-fronts. This study aims to improve the quality of developed dispatching rules for many-objective Job Shop Scheduling (JSS) by combining GP with PLS and adaptive reference point approaches. In this research, we propose a new GP-PLS-II-A (adaptive) method that verifies the hypothesis that PLS’s fitness-based solution selection mechanism can increase the probability of finding extremely effective dispatching rules for many-objective JSS. The effectiveness of our new algorithm is assessed by comparing GP-PLS-II-A to the many-objective JSS algorithms that used PLS. The experimental findings show that the proposed method outperforms the four compared algorithms because of the effective use of local search strategies with adaptive reference points.
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References
Błażewicz, J., Domschke, W., Pesch, E.: The job shop scheduling problem: conventional and new solution techniques. Eur. J. Oper. Res. 93(1), 1–33 (1996)
Chen, B., Zeng, W., Lin, Y., Zhang, D.: A new local search-based multiobjective optimization algorithm. IEEE Trans. Evol. Comput. 19(1), 50–73 (2015)
Cheng, R., Jin, Y., Olhofer, M., Sendhoff, B.: A reference vector guided evolutionary algorithm for many-objective optimization. IEEE Trans. Evol. Comput. 20(5), 773–791 (2016)
Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Trans. Evol. Comput. 18(4), 577–601 (2014)
Dubois-Lacoste, J., López-Ibáñez, M., Stützle, T.: Anytime pareto local search. Eur. J. Oper. Res. 243(2), 369–385 (2015)
Holthaus, O., Rajendran, C.: Efficient jobshop dispatching rules: further developments. Prod. Plann. Control 11(2), 171–178 (2000)
Ishibuchi, H., Murata, T.: A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 28(3), 392–403 (1998)
Ishibuchi, H., Setoguchi, Y., Masuda, H., Nojima, Y.: Performance of decomposition-based many-objective algorithms strongly depends on Pareto front shapes. IEEE Trans. Evol. Comput. 21(2), 169–190 (2016)
Jain, H., Deb, K.: An improved adaptive approach for elitist nondominated sorting genetic algorithm for many-objective optimization. In: Purshouse, R.C., Fleming, P.J., Fonseca, C.M., Greco, S., Shaw, J. (eds.) EMO 2013. LNCS, vol. 7811, pp. 307–321. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37140-0_25
Jain, H., Deb, K.: An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, Part II: handling constraints and extending to an adaptive approach. IEEE Trans. Evol. Comput. 18(4), 602–622 (2014)
Koza, J.R.: A genetic approach to the truck backer upper problem and the inter-twined spiral problem. In: Proceedings of IJCNN International Joint Conference on Neural Networks, vol. IV, pp. 310–318. IEEE Press (1992)
Lee, Y.H., Bhaskaran, K., Pinedo, M.: A heuristic to minimize the total weighted tardiness with sequence-dependent setups. IIE Trans. 29(1), 45–52 (1997)
Masood, A., Chen, G., Mei, Y., Al-Sahaf, H., Zhang, M.: Genetic programming with pareto local search for many-objective job shop scheduling. In: Liu, J., Bailey, J. (eds.) AI 2019. LNCS (LNAI), vol. 11919, pp. 536–548. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35288-2_43
Masood, A., Chen, G., Mei, Y., Al-Sahaf, H., Zhang, M.: A fitness-based selection method for pareto local search for many-objective job shop scheduling. In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2020)
Masood, A., Chen, G., Mei, Y., Al-Sahaf, H., Zhang, M.: Genetic programming hyper-heuristic with gaussian process-based reference point adaption for many-objective job shop scheduling. In: 2022 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2022)
Masood, A., Mei, Y., Chen, G., Zhang, M.: Many-objective genetic programming for job-shop scheduling. In: IEEE WCCI 2016 Conference Proceedings, IEEE (2016)
Nguyen, S.: Automatic Design of Dispatching Rules for Job Shop Scheduling with Genetic Programming. Ph.D. thesis (2013)
Nguyen, S., Mei, Y., Ma, H., Chen, A., Zhang, M.: Evolutionary scheduling and combinatorial optimisation: applications, challenges, and future directions. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 3053–3060. IEEE (2016)
Nguyen, S., Zhang, M., Johnston, M., Tan, K.C.: Automatic programming via iterated local search for dynamic job shop scheduling. IEEE Trans. Cybern. 45(1), 1–14 (2015)
Pinedo, M.L.: Scheduling: Theory, Algorithms, and Systems. Springer, Berlin (2012)
Taillard, E.: Benchmarks for basic scheduling problems. Eur. J. Oper. Res. 64(2), 278–285 (1993)
Wilcoxon, F.: Individual comparisons by ranking methods. In: Kotz, S., Johnson, N.L. (eds.) Breakthroughs in Statistics. Springer Series in Statistics. Springer, New York (1992). https://doi.org/10.1007/978-1-4612-4380-9_16
Zhang, Q., Zhou, A., Zhao, S., Suganthan, P.N., Liu, W., Tiwari, S.: Multiobjective optimization test instances for the CEC 2009 special session and competition, pp. 1–30 (2008)
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Masood, A., Chen, G., Mei, Y., Al-Sahaf, H., Zhang, M. (2024). Genetic Programming with Adaptive Reference Points for Pareto Local Search in Many-Objective Job Shop Scheduling. In: Liu, T., Webb, G., Yue, L., Wang, D. (eds) AI 2023: Advances in Artificial Intelligence. AI 2023. Lecture Notes in Computer Science(), vol 14472. Springer, Singapore. https://doi.org/10.1007/978-981-99-8391-9_37
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