The purpose of this study is to develop an effective design of Cellular Manufacturing System (CMS) in dynamic production requirement with multiple objectives. Simulated annealing (SA) method is proposed optimization. The method is discussed and the model is implemented successfully in a manufacturing company with two period planning horizons. The algorithm developed in this study reduces intercell movement cost and intracell movement cost. An algorithm of CMS design minimizes total CMS cost and works in real life situation.

Shivprakash Bhagwatrao Barve

School of Mechanical Engineering

Engineering and Technology

Khodke Prakash M

MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

INTERNATIONAL JOURNAL OF CURRENT ENGINEERING AND SCIENTIFIC RESEARCH (IJCESR)

Computers & Industrial Engineering

Cell formation in a cellular manufacturing system using simulation integrated hybrid genetic algorithm

Applied Mathematical Modelling

A robust optimization approach for an integrated dynamic cellular manufacturing system and production planning with unreliable machines

International Journal of Production Research

Application of simulated annealing to a linear model forthe formulation of machine cells ingroup technology

European Journal of Operational Research

Simulated annealing procedures for forming machine cells in group technology

Identification of machine-cells is one of the most important problems in the design of cellular manufacturing systems (CMSs). It involves decomposing a manufacturing system into machine-cells by grouping machines and parts. Several algorithms with varying degrees of success have been proposed and utilised to solve this problem. Among the modern tools, neural network models have the potential to solve the machine-cell formation problem. Choosing the competitive learning model, adaptive resonance theory (ART) model and self-organizing feature map (SOFM) model from neural network theory for this purpose, we demonstrate their suitability for solving the machine-cell formation problem. Applications on trial problems show the viability for solving the machine-cell formations problem and stand testimony to the practical utility of neural network models in designing CMSs. © 1994 Taylor & Francis Group, LLC.

Machine-cell formation through neural network models

DESIGN OF CELLULAR MANUFACTURING SYSTEM: A CASE STUDY

Journal	INTERNATIONAL JOURNAL OF CURRENT ENGINEERING AND SCIENTIFIC RESEARCH (IJCESR)
Publisher	Technical Research Organisation India
Open Access	0