Optimization Algorithm in Supply Chain Management
Shraddha Ramdas Bandekar1, Vijayalakshmi C2
1Shraddha Ramdas Bandekar*,Mathematics Division, School of Advanced Sciences, Vellore Institute of Technology. Chennai.
2Vijayalakshmi C*, Mathematics Division, School of Advanced Sciences, Vellore Institute of Technology. Chennai.
Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 5072-5079 | Volume-8 Issue-12, October 2019. | Retrieval Number: L27241081219/2019©BEIESP | DOI: 10.35940/ijitee.L2724.1081219
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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: Optimization in the field of Operations Research has applications in various industries, be it medicine, business, analytics or education. Likewise Supply Chain Management (SCM) is required in every industry and with the need comes various challenges to get the optimized and best quality solution. There are stochastic, analytical models working on attaining optimization in various sub events involved in SCM. Supply Chain is a network at global level used for delivering of products and services from unprocessed materials to consumers through well-structured and planned flow of information, physical distribution and money. The process of managing this supply chain is Supply Chain Management. A major work on the previous research done using various mathematical models, be it mixed integer linear, nonlinear programming or evolutionary have been depicted in this paper. The aim is to get the best result and comparative approach is focused. This article provides a detailed study on various techniques, algorithms and mathematical models in optimization of SCM and in particular it focuses on Genetic Algorithm (GA) in SCM.
Keywords: Supply Chain Management, Supply Chain Management Processes , Optimization Model, Genetic Algorithm
Scope of the Article: Algorithm Engineering