Pengembangan Algoritma Hybrid Metaheuristik Untuk Penentuan Rute Pengiriman Produk Perishable
DOI:
https://doi.org/10.22219/JTIUMM.Vol18.No2.191-206Keywords:
perishable products, delivering, route, hybrid metaheuristics, particle swarm optimizationAbstract
The decision to dispatch consumers demand has become a strategic and tactical consideration to be solved in an integrated manner. In this study, the problem of determining routing problem take case study of delivery of perishable product. The routes determination should take into account the unique characteristics of perishable products possess. Perishable products continuously decreases quality over their lifetime. The challenge for distributors is how to minimize the cost of delivering perishable products by taking into account the temperature so that it can serve a number of customers within the specified timeframe,
The problem of determining the route on delivery is included in the combinatorial optimization problem, thus causing this problem to be complex to be solved by the exact method. On the other hand, metaheuristic methods are increasingly being developed to be applied in the completion of combinatorial optimizations.
This research started from mathematical model of perishable product delivery which pay attention to perishability (quality, temperature, quality loss) and time windows. Based on this model, this research develops the route settlement algorithm of delivery of perishable product using metaheuristic, particle swarm optimization. The algorithm development is required because route determination included in discrete issues. In addition, the development of algorithms to improve performance by combining (hybrid) algorithms, nearest neighbor and particle swarm optimization. Experiments were performed on 2 sets of Solomon data. From the experimental results with the metaheuristic hybrid algorithm is able to provide better performance than pure metaheuristik. Although the solution gap produced by these two algorithms is not very significant, but when viewed from the computation time and the number of iterations required to find the best solution, this metaheuristic hybrid algorithm can save an average time of 17 times from pure metaheuristic algorithm.
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References
J.-P. Rodrigue, C. Comtois, and B. Slack, The geography of transport systems: Routledge, 2009.
X. Li, P. Tian, and S. C. Leung, "Vehicle routing problems with time windows and stochastic travel and service times: Models and algorithm," International Journal of Production Economics, vol. 125, pp. 137-145, 2010.
P. Toth and D. Vigo, "The vehicle routing problem, ser. SIAM monographs on discrete mathematics and applications," Society for Industrial and Applied Mathematics, 2002.
M. A. Figliozzi, "An iterative route construction and improvement algorithm for the vehicle routing problem with soft time windows," Transportation Research Part C: Emerging Technologies, vol. 18, pp. 668-679, 2010. [1] J.-P. Rodrigue, C. Comtois, and B. Slack, The geography of transport systems: Routledge, 2009.
X. Li, P. Tian, and S. C. Leung, "Vehicle routing problems with time windows and stochastic travel and service times: Models and algorithm," International Journal of Production Economics, vol. 125, pp. 137-145, 2010.
P. Toth and D. Vigo, "The vehicle routing problem, ser. SIAM monographs on discrete mathematics and applications," Society for Industrial and Applied Mathematics, 2002.
M. A. Figliozzi, "An iterative route construction and improvement algorithm for the vehicle routing problem with soft time windows," Transportation Research Part C: Emerging Technologies, vol. 18, pp. 668-679, 2010.
J. Müller, "Approximative solutions to the bicriterion vehicle routing problem with time windows," European Journal of Operational Research, vol. 202, pp. 223-231, 2010.
H. I. Calvete, C. Galé, M.-J. Oliveros, and B. Sánchez-Valverde, "A goal programming approach to vehicle routing problems with soft time windows," European Journal of Operational Research, vol. 177, pp. 1720-1733, 2007.
G. N. Yücenur and N. Ç. Demirel, "A new geometric shape-based genetic clustering algorithm for the multi-depot vehicle routing problem," Expert Systems with Applications, vol. 38, pp. 11859-11865, 2011.
X.-S. Yang, Engineering optimization: an introduction with metaheuristic applications: John Wiley & Sons, 2010.
J. Jie, J. Zeng, C. Han, and Q. Wang, "Knowledge-based cooperative particle swarm optimization," Applied mathematics and Computation, vol. 205, pp. 861-873, 2008.
T. J. Ai and V. Kachitvichyanukul, "A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery," Computers & Operations Research, vol. 36, pp. 1693-1702, 2009.
Trihardani, "Vehicle Routing Problem for Optimizing Multi Temperature Joint Distribution On Distribution of Perishable Products," International Conference on Operations and Supply Chain Management (OSCM), pp. 331-343, 2016.
R. A. Broekmeulen and K. H. van Donselaar, "A heuristic to manage perishable inventory with batch ordering, positive lead-times, and time-varying demand," Computers & Operations Research, vol. 36, pp. 3013-3018, 2009.
H.-S. Hwang, "A food distribution model for famine relief," Computers & Industrial Engineering, vol. 37, pp. 335-338, 1999.
C. Tarantilis and C. Kiranoudis, "Distribution of fresh meat," Journal of Food Engineering, vol. 51, pp. 85-91, 2002.
C. Tarantilis and C. Kiranoudis, "A meta-heuristic algorithm for the efficient distribution of perishable foods," Journal of food Engineering, vol. 50, pp. 1-9, 2001.
J. Faulin, "Applying MIXALG procedure in a routing problem to optimize food product delivery," Omega, vol. 31, pp. 387-395, 2003.
J. Belenguer, E. Benavent, and M. Martínez, "RutaRep: a computer package to design dispatching routes in the meat industry," Journal of food engineering, vol. 70, pp. 435-445, 2005.
C.-I. Hsu, S.-F. Hung, and H.-C. Li, "Vehicle routing problem with time-windows for perishable food delivery," Journal of food engineering, vol. 80, pp. 465-475, 2007.
A. Osvald and L. Z. Stirn, "A vehicle routing algorithm for the distribution of fresh vegetables and similar perishable food," Journal of food engineering, vol. 85, pp. 285-295, 2008.
H.-K. Chen, C.-F. Hsueh, and M.-S. Chang, "Production scheduling and vehicle routing with time windows for perishable food products," Computers & operations research, vol. 36, pp. 2311-2319, 2009.
A. Rong, R. Akkerman, and M. Grunow, "An optimization approach for managing fresh food quality throughout the supply chain," International Journal of Production Economics, vol. 131, pp. 421-429, 2011.
J.-Y. Yan, Q. Ling, and D.-m. Sun, "A differential evolution with simulated annealing updating method," in Machine Learning and Cybernetics, 2006 International Conference on, 2006, pp. 2103-2106.
S. Das, A. Konar, and U. K. Chakraborty, "Annealed differential evolution," in Evolutionary Computation, 2007. CEC 2007. IEEE Congress on, 2007, pp. 1926-1933.
B. Santosa and P. Willy, "Metoda Metaheuristik Konsep dan Implementasi," Guna Widya, Surabaya, 2011.
