INTELLIGENT ROUTING APPROACH FOR THE DISTRIBUTIONS REGARDING TO THE SUPPLY CHAIN MANAGEMENT OF AGRICULTURAL PRODUCTS AND FOODS

INTELLIGENT ROUTING APPROACH FOR THE DISTRIBUTIONS REGARDING TO THE SUPPLY CHAIN MANAGEMENT OF AGRICULTURAL PRODUCTS AND FOODS

Purpose- In this study, to effectively manage the supply chain intended for the storage, transportation and distribution of agricultural products and foods, an intelligent routing approach is proposed for accomplishing this distribution at low cost. Methodology- As an activity in logistics, the demands related to the exportation of various foods/goods in agricultural production are considered. It is assumed that a vehicle fleet, each vehicle with a certain load capacity, starts at a depot with the loads, visits a set of points using the shortest paths while accomplishing the related distributions and returns back to the depot. It is aimed to find the route set with the minimum cost so that all customers are to be visited. Findings- The main contribution of this study in which the Capacitated Vehicle Routing Problem has been addressed is to find the route set to service all the customers in a region while considering the points to be serviced globally; or for each sub-region, the separated tours to service the related customer group while considering them as subsets.Conclusion- Using the hybrid meta-heuristic algorithm including Genetic Algorithms and Local Search developed for the solution, low costly route sets with separated/global tours may be generated within very short periods of time. 

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  • Berger, J. & Barkaoui, M. 2003, “A hybrid genetic algorithm for the capacitated vehicle routing problem”, Genetic and Evolutionary Computation Conference (GECCO’03): Part I, pp. 646-656, Lecture Notes in Computer Science, 2723.
  • Boonsam, P., Suthikarnnarunai, N. & Chitphaiboon, W. 2011, “Assignment problem and vehicle routing problem for an improvement of cash distribution”, World Congress on Engineering and Computer Science (WCECS), vol. II, October 19-21, San Francisco, USA.
  • Bortfeldt, A. 2012, “A hybrid algorithm for the capacitated vehicle routing problem with three-dimensional loading constraints”, Computers & Operations Research, vol. 39, no. 9, pp. 2248-2257.
  • Cacchiani, V., Hemmelmayr, V.C. & Tricoire, F. 2014, “A set-covering based heuristic algorithm for the periodic vehicle routing problem”, Discrete Applied Mathematics, vol. 163, no. 1, pp. 53-64.
  • Chand, P. & Mohanty, J.R. 2013, “Solving vehicle routing problem with proposed non-dominated sorting genetic algorithm and comparison with classical evolutionary algorithms”, International Journal of Computer Applications (IJCA), vol. 69, no. 26, pp. 34-41.
  • Chand, P., Mishra, B.S.P. & Dehuri, S. 2010, “A multi objective genetic algorithm for solving vehicle routing problem”, International Journal of Information Technology and Knowledge Management, vol. 2, no. 2, pp. 503-506.
  • Dantzig, G.B. & Ramser, J.H. 1959, “The truck dispatching problem”, Management Science, vol. 6, no. 1, pp. 80-91.
  • Du, L. & He, R. 2012, “Combining nearest neighbor search with tabu search for large-scale vehicle routing problem”, Physics Procedia, vol. 25, pp. 1536-1546, International Conference on Solid State Devices and Materials Science, April 1-2, Macao.
  • Fuellerer, G., Doerner, K.F., Hartl, R.F. & Iori, M. 2010, “Metaheuristics for vehicle routing problems with three-dimensional loading constraints”, European Journal of Operational Research, vol. 201, no. 3, pp. 751-759.
  • Fukasawa, R., Lysgaard, J., de Aragão, M.P., Reis, M., Uchoa, E. & Werneck, R.F. 2006, “Robust branch-and-cut-and-price for the capacitated vehicle routing problem”, Mathematical Programming, vol. 106, no. 3, pp. 491-511.
  • Hà, M.H., Bostel, N., Langevin, A. & Rousseau, L.M. 2014, “An exact algorithm and a metaheuristic for the generalized vehicle routing problem with flexible fleet size”, Computers & Operations Research, vol. 43, pp. 9-19.
  • Ho, W., Ho, G.T.S., Ji, P. & Lau, H.C.W. 2008, “A hybrid genetic algorithm for the multi-depot vehicle routing problem”, Engineering Applications of Artificial Intelligence, vol. 21, no. 4, pp. 548-557.
  • Lei, H., Laporte, G. & Guo, B. 2011, “The capacitated vehicle routing problem with stochastic demands and time windows”, Computers & Operations Research, vol. 38, no. 12, pp. 1775-1783.
  • Leung, S.C.H., Zhang, Z., Zhang, D., Hua, X. & Lim, M.K. 2013, “A meta-heuristic algorithm for heterogeneous fleet vehicle routing problems with two-dimensional loading constraints”, European Journal of Operational Research, vol. 225, no. 2, pp. 199-210.
  • Lin, S.W., Lee, Z.J., Ying, K.C. & Lee, C.Y. 2009, “Applying hybrid meta-heuristics for capacitated vehicle routing problem”, Expert Systems with Applications, vol. 36, no. 2, pp. 1505-1512.
  • Luo, J. & Chen, M.R. 2014, “Improved shuffled frog leaping algorithm and its multi-phase model for multi-depot vehicle routing problem”, Expert Systems with Applications, vol. 41, no. 5, pp. 2535-2545.
  • Lysgaard, J., Letchford, A.N. & Eglese, R.W. 2004, “A new branch-and-cut algorithm for the capacitated vehicle routing problem”, Mathematical Programming, vol. 100, no. 2, pp. 423-445.
  • Marinakis, Y., Iordanidou, G.R. & Marinaki, M. 2013, “Particle swarm optimization for the vehicle routing problem with stochastic demands”, Applied Soft Computing, vol. 13, no. 4, pp. 1693-1704.
  • Moscato, P. & Cotta, C. 2003, “A gentle introduction to memetic algorithms”, pp. 105-144 Handbook of Metaheuristics, Glover, F., Kochenberger, G.A. (Eds.), Springer US, 57, Boston MA, 560p.
  • Pop, P.C., Matei, O. & Sitar, C.P. 2013, “An improved hybrid algorithm for solving the generalized vehicle routing problem”, Neurocomputing, vol. 109, pp. 76-83, “New Trends on Soft Computing Models in Industrial and Environmental Applications” — A selection of extended and updated papers from the SOCO 2011 International Conference.
  • Ruan, Q., Zhang, Z., Miao, L. & Shen, H. 2013, “A hybrid approach for the vehicle routing problem with three-dimensional loading constraints”, Computers & Operations Research, vol. 40, no. 6, pp. 1579-1589.
  • Stanojević, M., Stanojević, B. & Vujošević, M. 2013, “Enhanced savings calculation and its applications for solving capacitated vehicle routing problem”, Applied Mathematics and Computation, vol. 219, no. 20, pp. 10302-10312.
  • Subramanian, A., Uchoa, E. & Ochi, L.S. 2013, “A hybrid algorithm for a class of vehicle routing problems”, Computers & Operations Research, vol. 40, no. 10, pp. 2519-2531.
  • Yurtkuran, A. & Emel, E. 2010, “A new hybrid electromagnetism-like algorithm for capacitated vehicle routing problems”, Expert Systems with Applications, vol. 37, no. 4, pp. 3427-3433.
  • Tlili, T., Faiz, S. & Krichen, S. 2014, “A hybrid metaheuristic for the distance-constrained capacitated vehicle routing problem”, Procedia - Social and Behavioral Sciences, vol. 109, pp. 779-783, 2nd World Conference on Business, Economics and Management.
  • Wang, C.H. & Lu, J.Z. 2009, “A hybrid genetic algorithm that optimizes capacitated vehicle routing problems”, Expert Systems with Applications, vol. 36, no. 2, pp. 2921-2936.
  • Yücenur, G.N. & Demirel, N.Ç. 2011, “A hybrid algorithm with genetic algorithm and ant colony optimization for solving multi-depot vehicle routing problems”, Journal of Engineering and Natural Sciences, vol. Sigma 29, no. 3, pp. 340-350.