Abstract
In this paper, we study the Capacitated Vehicle Routing Problem (CVRP) and implemented a simulation-based algorithm with different random number generators. The Binary-CWS-MCS algorithm has been integrated with six different random number generators and their variations. The random number generators used in this study gathered with respect to two perspectives, the first is to compare the mostly known and used RNGs in simulation-based studies which are Linear Congruential Generator (LCG) and its shift variant, Multiple Recursive Generator (MRG) and its shift variant and the second perspective is based on the improvements in the random number generator algorithms which are Mersenne Twister Pseudo Random Generator (MT) and Permuted Congruential Generator (PCG). The results of experiments showed that the PCG and MT pseudo random generators can generate better results than the other random number generators.
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09 July 2023
A correction has been published.
References
Bauke, H., Mertens, S.: Random numbers for large-scale distributed monte Carlo simulations. Phys. Rev. E: Stat., Nonlin, Soft Matter Phys. 75(6), 066701 (2007)
Bird, G.: Monte-Carlo simulation in an engineering context. Prog. Astronaut. Aeronaut. 74, 239–255 (1981)
Chan, W.K.V.: Theory and applications of monte Carlo simulations. BoD-Books on Demand (2013)
Charles, P.: Capacitated vehicle routing problem library. http://vrp.galgos.inf.puc-rio.br/index.php/en/updates (2014)
Cordeau, J.F., Laporte, G., Savelsbergh, M.W., Vigo, D.: Vehicle routing. Handbooks Oper. Res. Management Sci. 14, 367–428 (2007)
Dantzig, G.: Linear programming and extensions. Princeton University Press (2016)
Demirci, I.E., Özdemir, Ş.E., Yayla, O.: Comparison of randomized solutions for constrained vehicle routing problem. In: 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), pp. 1–6. IEEE (2020)
L’ecuyer, P.: Tables of linear congruential generators of different sizes and good lattice structure. Math. Comput. 68(225), 249–260 (1999)
L’Ecuyer, P., Blouin, F., Couture, R.: A search for good multiple recursive random number generators. ACM Trans. Model. Comput. Simul. (TOMACS) 3(2), 87–98 (1993)
Matsumoto, M., Nishimura, T.: Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans. Model. Comput. Simul. (TOMACS) 8(1), 3–30 (1998)
O’Neill, M.E.: PCG: a family of simple fast space-efficient statistically good algorithms for random number generation. Tech. Rep. HMC-CS-2014-0905, Harvey Mudd College, Claremont, CA (2014)
Steele, G.L., Vigna, S.: Computationally easy, spectrally good multipliers for congruential pseudorandom number generators. Softw. Pract. Exper. 52(2), 443–458 (2022)
Takes, F., Kosters, W.A.: Applying monte Carlo techniques to the capacitated vehicle routing problem. In: Proceedings of 22th Benelux Conference on Artificial Intelligence (BNAIC 2010) (2010)
Thomson, W.: A modified congruence method of generating pseudo-random numbers. Comput. J. 1(2), 83 (1958)
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Gülşen, M.E., Yayla, O. (2023). Random Sequences in Vehicle Routing Problem. In: Georgiev, I., Datcheva, M., Georgiev, K., Nikolov, G. (eds) Numerical Methods and Applications. NMA 2022. Lecture Notes in Computer Science, vol 13858. Springer, Cham. https://doi.org/10.1007/978-3-031-32412-3_14
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DOI: https://doi.org/10.1007/978-3-031-32412-3_14
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