Authors:
Anurag Tiwari,Vivek Kumar Singh,Praveen Kumar Shukla,Manuj Darbari,DOI NO:
https://doi.org/10.26782/jmcms.2020.03.00025Keywords:
MOO,QFD,Mobile Handsets,Abstract
The Blending problem is one of the oldest and best known optimization problems. It is generally formulated as a linear program and has been applied in many fields. However, the mixing problem encountered in the industry requires much more than direct linear programming formulation. Indeed, the classic blending model would almost always be impossible due to the problem of blending in the industry. Indeed, it is often not possible to combine the characteristics of the mixtures as desired, which leads decision makers to seek solutions as close as possible to specific solutions. In this article, we develop and solve a versatile optimization model for the problem of blending, in which we minimize the total cost of the raw materials to be used, as well as violations of the desired characteristic scores of the final blends. We also present a parametric model which is used as a reference point to compare the multi-objective optimization model.Refference:
I. D Yagyasen, M Darbari, PK Shukla, VK Singh (2013), “Diversity and convergence issues in evolutionary multiobjective optimization: application to agriculture science”, IERI Procedia.
II. D Yagyasen, M Darbari (2014),”Application of semantic web and petri calculus in changing business scenario”Modern Trends and Techniques in Computer Science.
III. R Asthana, NJ Ahuja, M Darbari (2011),”Model proving of urban traffic controls using neuro Petri nets and fuzzy logic”International Review on Computers and Software (IRECOS.
IV. S Bansal, M Darbari(2012),”Application of Multi Objective Optimization in Prioritizing and Machine Scheduling: a Mobile Scheduler Toolkit”International Journal of Applied Information Systems 3 (2), 24-28.
V. SS Ahmad, M Darbari, H Purohit (2015),”Handling web dynamics of internet marketing supply chain using evolutionary algorithms and semantic breakdown strategy”International Business Information Management ConferenceNetherlands.
VI. SS Ahmad, H Purohit, F Alshaikhly, M Darbari (2013),”Information granules for medical infonomics”International Journal of Information and Operations Management Education.
VII. SaviturPrakash and ManujDarbari, “‘Quality & Popularity’ Prediction Modeling of TV Programme through Fuzzy QFD Approach,” Journal of Advances in Information Technology, Vol. 3, No. 2, pp. 77-90, May, 2012.doi:10.4304/jait.3.2.77-90
VIII. Sofia Angeletou, Matthew Rowe, and HarithAlani: Modelling and Analysis of User Behaviour in Online Communities, 10th International Semantic Web Conference Bonn, Germany, October 23-27, 2011, Lecture Notes in Computer Science, Springer-Verlag Berlin Heidelberg.
IX. Yang. S.Y, Hyun Ko, Seung, Wok. H, Hee. Y. Y, (2007), “Priority-Based Message Scheduling for the Multi-agent System in Ubiquitous Environment”, IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology – Workshops, pp. 395-398.
X. Yujian. Fu, Kan. W, Junwei. Y (2006), “A Multi-Agent System for Manufacturing Material Resource Planning”, Sixth International Conference on Intelligent Systems Design and Applications (ISDA’06) Volume 2, pp. 1118-1123.
XI. Zhanjie. W, Yanbo. L (2006), “A Multi-Agent Agile Scheduling System for Job-Shop Problem”, Sixth International Conference on Intelligent Systems Design and Applications (ISDA’06) Volume 2, pp. 679-683.
View Download