Abstract
Vehicle routing problem is an interesting combinatoric research problem of NP-complete class for investigation. Many researchers in the past have targeted this interesting combinatorial problem with a number of methodologies. The classic methods like brute-force approach, dynamic programming, and integer linear programming methods were used in earlier attempts to find the most optimized route for a vehicle. However, these methods met their computational limitation for a large number of coverage points. Owing to the exhaustive evaluation for the number of routes, the genetic algorithm-based heuristic approach was proposed to find accurate approximate solutions. The method involves solving a traveling salesman problem (TSP) using the genetic algorithm approach for a large number of route combinations which were very high. This research document proposes a solution to this by using the multi-core architecture, where it has been shown that implementing GA as heuristic approach for a large solution space is not sufficient. A contrast has been shown between the serial and parallel implementation of the solution using OpenMP multi-processing architecture which shows a considerable speedup for the execution time of the algorithm to search the best path. For a varied degree of graph structures, this implementation has highly reduced execution time.
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References
R. Saxena, M. Jain, D.P. Sharma, S. Jaidka, A review on VANET routing protocols and proposing a parallelized genetic algorithm based heuristic modification to mobicast routing for real time message passing. J. Intell. Fuzzy Syst. 36(3), 2387–2398 (2019)
Y. Lin, W. Li, F. Qiu, H. Xu, Research on optimization of vehicle routing problem for ride-sharing taxi. Procedia Soc. Behav. Sci. 43, 494–502 (2012)
H. Nazif, L.S. Lee, Optimized crossover genetic algorithm for capacitated vehicle routing problem. Appl. Math. Model. 36(5), 2110–2117 (2012)
A.K.M. Masum, M. Shah Jalal, F. Faruque, I.H. Sarker, Solving the vehicle routing problem using genetic algorithm. Int. J. Adv. Comput. Sci. Appl. 2(7), 126–131 (2011)
P. Chand, J.R. Mohanty, A multi-objective vehicle routing problem using dominant rank method. Int. J. Comput. Appl. 29–34 (2013)
R.G. Kang, C.Y. Jung, The improved initialization method of genetic algorithm for solving the optimization problem, in International Conference on Neural Information Processing (Springer, Berlin, Heidelberg, 2006), pp. 789–796
P.L.N.U. Cooray, T.D. Rupasinghe, Machine learning-based parameter tuned genetic algorithm for energy minimizing vehicle routing problem. J. Ind. Eng. (2017)
H. Nazif, L.S. Lee, Optimized crossover genetic algorithm for vehicle routing problem with time windows. Am. J. Appl. Sci. 7(1), 95 (2010)
E. Alba, B. Dorronsoro, Solving the vehicle routing problem by using cellular genetic algorithms, in European Conference on Evolutionary Computation in Combinatorial Optimization (Springer, Berlin, Heidelberg, 2004), pp. 11–20
M. Jain, R. Saxena, V. Agarwal, A. Srivastava, An OpenMP-based algorithmic optimization for congestion control of network traffic, in Information and Decision Sciences (Springer, Singapore, 2018), pp. 49–58
R. Saxena, M. Jain, D. Singh, A. Kushwah, An enhanced parallel version of RSA public key crypto based algorithm using OpenMP, in Proceedings of the 10th International Conference on Security of Information and Networks (ACM, 2017), pp. 37–42
R. Saxena, M. Jain, D.P. Sharma, GPU-based parallelization of topological sorting, in Proceedings of First International Conference on Smart System, Innovations and Computing (Springer, Singapore, 2018), pp. 411–421
M. Jain, R. Saxena, Parallelization of video summarization over multi-core processors. Int. J. Pure Appl. Math. 118(9), 571–584 (2018). ISSN: 1311-8080
R. Saxena, M. Jain, A. Kumar, V. Jain, T. Sadana, S. Jaidka, An improved genetic algorithm based solution to vehicle routing problem over OpenMP with load consideration, in Advances in Communication, Devices and Networking (Springer, Singapore, 2019), pp. 285–296
M. Basthikodi, W. Ahmed, Parallel algorithm performance analysis using OpenMP for multicore machines. Int. J. Adv. Comput. Technol. (IJACT) 4(5), 28–32 (2015)
R. Saxena, M. Jain, S. Bhadri, S. Khemka, Parallelizing GA based heuristic approach for TSP over CUDA and OPENMP, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (IEEE, 2017), pp. 1934–1940
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Saxena, R., Jain, M., Malhotra, K., Vasa, K.D. (2020). An Optimized OpenMP-Based Genetic Algorithm Solution to Vehicle Routing Problem. In: Elçi, A., Sa, P., Modi, C., Olague, G., Sahoo, M., Bakshi, S. (eds) Smart Computing Paradigms: New Progresses and Challenges. Advances in Intelligent Systems and Computing, vol 767. Springer, Singapore. https://doi.org/10.1007/978-981-13-9680-9_20
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DOI: https://doi.org/10.1007/978-981-13-9680-9_20
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