Multi Objective Scheduling of Jobs in Flexible Manufacturing System Using Metaheuristic Approaches with Inclusion of Simulation Modeling

Main Article Content

A.V. S. SREEDHAR KUMAR
V. VEERANNA
B. DURGAPRASAD

Abstract

Manufacturing industries play a vital role in defining the growth and development of a nation. More often than not the health of manufacturing industries is a direct reflection on the economic health and prosperity of a nation. The viability and profitability of any industry depends on how easily and quickly the industry is adapting itself to changes in demand. To adapt quickly to the changes an industry must be lean, flexible and should be capable of utilizing the existing resources to optimum. Optimum utilization of resources can only be possible if there is a proper scheduling system in place. The main objective of this paper is to study the scheduling problems in FMS environment with primary emphasis on metaheuristic approaches and also describes the development of a simulation model for production planning personnel to carry out optimization of scheduling in FMS environment. To illustrate the study in detail, a flexible manufacturing system consisting of 6 Machines producing 3 different parts through 3 different setup of machines and in each setup, 3 alternative routes are considered for this work. The optimization of scheduling process by using Bacterial Foraging optimization algorithm (BFOA, Genetic algorithm (GA) and Differential Evolution (DE) and choose the best for the scenario. In order to impress upon the industry management to take proper decision about the significance of the proposed changes, the best setup and schedule as suggested by metaheuristic approach has been modeled by using the Promodel software and simulated for various runs.

Article Details

How to Cite
[1]
A.V. S. SREEDHAR KUMAR, V. VEERANNA, and B. DURGAPRASAD, “Multi Objective Scheduling of Jobs in Flexible Manufacturing System Using Metaheuristic Approaches with Inclusion of Simulation Modeling”, Int. J. Comput. Eng. Res. Trends, vol. 2, no. 4, pp. 264–269, Apr. 2015.
Section
Research Articles

References

Chuda Basnet, Joe H. Mize,(1995) “Scheduling and Control of Flexible Manufacturing System: A Critical Review”. Journal of Intelligent Manufacturing, Special issue on Production Planning and Scheduling, pp 1-41.

T. Karthikeyan,(2003) "Modelling and Analysis of Scheduling in Computer Integrated Manufacturing", Ph.D Dissertation, Bharathidasan University, Tiruchirappalli,

Veeranna. V, Dattatreyasarma. B, Chakraverti. G(2006), Optimization of FMS Layout by Heuristic Procedure with Scheduling as a Constraint”Industrial Engg.Journal, 25-28

B.B.Choudhury, D.Mishra and B.B.Biswal, (2009). Appropriate Evolutionary Algorithm for Scheduling in FMS, IEEE Xplore [5] Jerald.J, Asokan.P, Prabaharan.G, Saravanan.R (2005). “Scheduling optimisation of flexible manufacturing systems using particle swarm optimisation algorithm”Int J Adv Manuf Technol, 25: 964–97

Choudhury .B.B, Biswal .B.B, (2007). ”Task assignment and scheduling in a constrained manufacturing system using GA” International Journal Agile System & Management.

Guohui Zhang, Liang Gao , Yang Shi (2011)An effective genetic algorithm for the flexible job-shop scheduling problem Expert Systems with Applications- 3563–3573

Lee C.Y, Piramuthu S, Tsai Y.K,(1997) “Job shop scheduling with a Genetic algorithm and machine learning” International Journal of production research, vol 4 pp 48-56.

Dervi_s Karabo_GA, Sel_cuk ¨Okdem, (2004). ”A Simple and Global Optimization Algorithm for Engineering Problems: Differential Evolution Algorithm” Turk J Elec Engin Vol.12, No.1

Tai-Chen Chen, Pei-Wei Tsai, Shu-Chuan Chu*, and Jeng-Shyang Pan (2007). “A Novel Optimization Approach: Bacterial-GA Foraging “ICICIC '07, IEEE Computer Society, Washington, DC, USA © ISBN:0-7695-2882-1

Suresh Kumar N & Sridharan R,(2009)” Simulation modeling and analysis of part and tool flow control decisions in a flexible manufacturing system” Robotics and ComputerIntegrated Manufacturing 25, Elsevier, pp 829–838