Scheduling optimization of flexible manufacturing system environment using Differential evolution and Bacterial Foraging Optimization Algorithms

Sreedhar Kumar A V S*, Veeranna**, B. Durgaprasad***
* Professor, Department of Mechanical Engineering, Balaji institute of Engineering and Management Studies, Nellore, Andhra Pradesh, India.
** Professor & Dean, Department of Mechanical Engineering,Brundavan Institute of Technology & Science, Kurnool, Andhra Pradesh, India.
*** Professor, Department of Mechanical Engineering, JNTUA college of Engineering, Anantapur, Andhra Pradesh, India.
Periodicity:February - April'2014
DOI : https://doi.org/10.26634/jic.2.2.2952

Abstract

In the 21 century, manufacturing companies face increasingly frequent and unpredictable market changes driven by global competition, including the rapid introduction of new products and constantly varying product demand. To remain competitive, companies must design manufacturing systems that not only produce high-quality products at low costs, but also which allow for rapid response to market changes and consumer needs. Complex Discrete Event Simulation Systems like Flexible Manufacturing systems necessitate the complete utilization of available resources to optimize their productivity. One important objective of scheduling in FMS systems is to increase resource utilization and reduce idle time. In this paper, scheduling is modeled as a Multi Objective Optimization Problem, with primary objective for picking a schedule which has very less Combined Objective Function (COF) value. Most of the optimization functions proposed in the literature have penalties incorporated in them when the scheduled job is not completed in the specified time. The authors have incorporated a reward for each job if the job is completed ahead of time. Such an approach has led to the increase in efficiency of the system.

Complex machining operations configured in the existing setup of FMS having 6 Machines producing 3 different parts through 3 alternative routes is considered for this work. An automated tool in the form of Graphical User Interface (GUI) is designed to automate the optimization of scheduling process by searching for solution in the search spaces using Bacterial Foraging Optimization Algorithm (BFOA), Genetic algorithm (GA) and Differential Evolution (DE) approaches and choose the best for that scenario. The inclusion of reward along with the penalty value as one of the parameters in the Combined Objective Function has yielded expected results in increasing the efficiency of the scheduling process in way of reduced machine idle time and reduced penalties.

Keywords

Flexible Manufacturing System (FMS), Scheduling, Graphical User Interface ,Genetic Algorithm, Differential evolution, Bacterial Foraging Optimization Algorithm(BFOA)

How to Cite this Article?

Kumar, A.V.S.S., Veeranna, V., and Durgaprasad, B. (2014). Scheduling optimization of flexible manufacturing system environment using Differential evolution and Bacterial Foraging Optimization Algorithms. i-manager’s Journal on Instrumentation and Control Engineering, 2(2), 1-10. https://doi.org/10.26634/jic.2.2.2952

References

[1]. Chan, F.T.S., Bhagwat, R., Wadhwa, S., (2007). “Flexibility performance: Taguchi's method study of physical system and operating control parameters of FMS”. Rob. Comput. Integr. Manuf. Vol.23, pp.25–37.
[2]. T. Karthikeyan, H.A. Noorul and M. Dinesh, (2003), “Scheduling decisions in FMS using a heuristic approach”, Int J Adv Manuf Technol, pp.374-379.
[3]. Veeranna.V, (2004). “Some Studies on optimum design of flexible manufacturing system layout”, P.hd. thesis, JNTU, Hyderabad.
[4]. Giffler B, Thomson GL (1960). “Algorithms for solving production scheduling problems”. Int J Oper Res 8:487–503
[5]Gupta, D., Buzacott, JA., (1989). “A framework for understanding flexibility of manufacturing systems”. J. Manuf. Syst. 8 (2), 89–97.
[6]. Sridhar J, Rajendran C (1994). “A genetic algorithm for family and job scheduling in a flow line based manufacturing cell”. In: Proceedings of the 16th International Conference on computers and IE, location, pp 337–340
[7]. Santarek and Buseif, (1998). Modelling and design of flexible manufacturing systems using SADT and Petri nets tools. J. Mater. Process. Technol., 76 (1998), pp. 212–218.
[8]. 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
[9]. Choudhury .B.B, Biswal .B.B, (2007). ”Task assignment and scheduking in a constrained manufacturing system using GA”, International Journal Agile System & Management.
[10]. B.B.Choudhury, D.Mishra and B.B.Biswal, (2009). Appropriate Evolutionary Algorithm for Scheduling in FMS , IEEE Xplore.
[11]. Gnanavelbabu, A; Jerald, J; Noorul Haq, A & Asokan, P. (2009). ”Multi Objective Scheduling of Jobs, AGVs and AS/RS in FMS using Artificial immune system”. Advances in Production Engineering & Management, pp.139-150, ISSN 1854-6250
[12]. S. Q. Liu and H. L. Ong, (2004). “Metaheuristics for the Mixed Shop Scheduling Problem,” Asia- Pacific Journal of Operational Research, Vol. 21, No. 4, pp. 97-115.
[13]. 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
[14]. 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
[15]. Sambarta Dasgupta, Swagatam Das, Ajith Abraham, Arijit Biswas., (2009). “Adaptive Computational Chemotaxis in Bacterial Foraging Optimization: An Analysis,” IEEE Transactions on Evolutionary Computation, Vol. 13, No. 4,
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Online 200 35 35 200 15
Pdf 35 35 200 20
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.