Warehouse Stock Prediction Using Krill Herd Algorithm
Somula Ramasubbareddy1, Aditya Sai Srinivas T2, Govinda K3, Manivannan .S.S4, Swetha .E5
1Somula Ramasubbareddy, Department of Information Technology, VNRVJIET, JNTUH, Hyderabad (Telangana), India.
2Aditya Sai Srinivas T, School of Computer Science and Engineering, VIT University, Vellore (Tamil Nadu), India.
3Govinda K, School of Computer Science and Engineering, VIT University, Vellore (Tamil Nadu), India.
4Manivannan.S.S, School of Information Technology, VIT University, Vellore (Tamil Nadu), India.
5Swetha. E, Department of Computer Science and Engineering, VIT University, Vellore (Tamil Nadu), India.
Manuscript received on 27 March 2019 | Revised Manuscript received on 06 April 2019 | Manuscript Published on 27 April 2019 | PP: 702-705 | Volume-7 Issue-6S2 April 2019 | Retrieval Number: F11200476S219/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: E-commerce and online shopping have seen a surge in demand that surpasses offline shopping. To keep up with this demand and stay relevant in the market, companies need to provide best service in the minimum time. The conventional trend is to get the product to the nearest warehouse after it is ordered or stock the warehouses to their full capacity to cater to customer demands. Those methods are ineffective because if the product is called after it is ordered it will lead to a waiting time of a couple of days, also special transportation needs to be arranged to get a less number of parcels to the destination which raises costs and pollution. Stocking up of warehouses is also ineffective as you waste space that could have been used for other products but also the products that no one needs have to be sent back which doubles the transportation cost. What we propose is a method to estimate the demand and strategically get and order products on trend analysis to save time, money and the environment. The algorithm we try to reach that end is Krill Herd Algorithm and Particle Swarm Optimisation which are a part of the genetic algorithms.
Keywords: Genetic Algorithms, Krill Herd Algorithm, Particle Swarm, E-commerce, Online Shopping, Warehouse Optimisation, Optimisation, Inventory Management.
Scope of the Article: Algorithm Engineering