Manufacturing Technology Today

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Multi-Product Inventory Optimization in a Multi-Echelon Supply Chain Using Artificial Bee Colony Optimization


Affiliations
1 Department of Mechanical Engineering, Kallam Haranadha Reddy Institute of Technology, Chowdavaram, Guntur, Andhra Pradesh, India
2 Mechanical Engineering Department, R.V.R & J.C. College of Engineering, Chowdavaram, Guntur, Andhra Pradesh, India
     

   Subscribe/Renew Journal


Inventory management is very important area in the supply chain management. Excess stocks may lead to incurring holding costs while shortage of stocks lead to shortage costs. The problem becomes more complicated when several factories produce multiple products in multiple time periods and supplies to several distribution centers who in turn supply to various agents and customers. With the advances in information technology and computing methods the inventory management problem in a multi echelon supply chain can be solved reasonably well. This paper presents an approach for the multi product inventory optimization in a multi echelon supply chain using Artificial Bee Colony Optimization method.

Keywords

Multi Product Inventory, Supply Chain, Artificial Bee Colony Optimization.
User
Subscription Login to verify subscription
Notifications
Font Size

  • Abdelmaguid, Tamer F; Dessousky, Maged M: A genetic algorithm approach to the integrated inventory distribution problem, 'International journal of production research', vol. 44, no. 21, 2006, 4445-4464.

  • Pardoe, D; Stone, P: An autonomous agent for supply chain management, in: Handbooks in information systems series: Business computing, Adomavicius, G and A Gupta (Eds), Elsevier Amsterdam, 2007. http://www.cs.utexas.edu /~pstone/Papers/bib2html/b2hd-TacTex-Book07.html.

  • Calderia, JL; Azevedo, RC et al: Supply-chain management using ACO and Beam-aco algorithms, 'Proceedings of the IEEE international fuzzy systems conference', July 23-26, London, 2007, 1-6.

  • Chih-Yao-Lo: Advance of dynamic productioninventory strategy for multiple policies using genetic algorithm, 'Information Technology Journal', vol. 7, 2008, 647-653.

  • Wang, K; Wang, W: Applying genetic algorithms to optimize the cost of multiple sourcing supply chain systems – An industry case study, Studies on computational intelligence, vol. 92, 2008, 355-372.

  • Karaboga, D; Akay, B: A comparative study of artificial bee colony algorithm, 'Applied mathematics and computation', vol. 214, no. 1, 2009, 108-132.

  • Radhakrishnan, P et al: Predictive analytics using genetic algorithm for efficient supply chain inventory optimization, 'International journal of computer science and network security', vol. 10, no. 3, 2010, 182-187.

  • Jeyanthi, N; Radhakrishnan, P: Optimizing multi product inventory using genetic algorithm for efficient supply chain management involving lead time, 'International journal of computer science and network security', vol. 10, no. 5, 2010, 231-239.

  • Narmadha, S et al: Multi-product inventory optimization using uniform crossover genetic algorithm”, International journal of computer science and information security, vol. 7, no. 1, 2010, 170-179.

  • Narmadha, S et al: Efficient inventory optimization of multi product, multiple suppliers with lead time using PSO, 'International Journal of Computer Science and Information Security', vol. 7, no. 1, 2010, 180-189.

  • Priya, P; Iyyakutti, K: Web based multi product inventory optimization using genetic algorithm, 'International journal of computer applications', vol. 25, no. 8, 2011, 23-28.

  • Tarun Kumar et al, Genetic algorithm based multi product and multi agent inventory optimization in supply chain management, 'International journal of modeling and optimization', vol. 2, no. 6, 2012, 653-657.

  • Yuce, Baris et al: Honey Bees Inspired Optimization Method: The Bees Algorithm”, insects, vol. 4, 2013, 646-662.

  • Akay, Bahriye et al: Solving Integer Programming Problems by Using Artificial Bee Colony Algorithm”, AI*IA 2009, LNAI 5883, 2009, 355-364


Abstract Views: 306

PDF Views: 2




  • Multi-Product Inventory Optimization in a Multi-Echelon Supply Chain Using Artificial Bee Colony Optimization

Abstract Views: 306  |  PDF Views: 2

Authors

T. V. S. R. K. Prasad
Department of Mechanical Engineering, Kallam Haranadha Reddy Institute of Technology, Chowdavaram, Guntur, Andhra Pradesh, India
Kolla Srinivas
Mechanical Engineering Department, R.V.R & J.C. College of Engineering, Chowdavaram, Guntur, Andhra Pradesh, India
C. Srinivas
Mechanical Engineering Department, R.V.R & J.C. College of Engineering, Chowdavaram, Guntur, Andhra Pradesh, India

Abstract


Inventory management is very important area in the supply chain management. Excess stocks may lead to incurring holding costs while shortage of stocks lead to shortage costs. The problem becomes more complicated when several factories produce multiple products in multiple time periods and supplies to several distribution centers who in turn supply to various agents and customers. With the advances in information technology and computing methods the inventory management problem in a multi echelon supply chain can be solved reasonably well. This paper presents an approach for the multi product inventory optimization in a multi echelon supply chain using Artificial Bee Colony Optimization method.

Keywords


Multi Product Inventory, Supply Chain, Artificial Bee Colony Optimization.

References