Implementation of Ant Colony Optimization and Genetic Algorithm to Plant Layout

Ankita P. Patil, K. V. Chandratre

Abstract


Plant layout is related with designing the layout and allocation of machines and activities of the system. For effective and smooth working of the system it is important to design the layout properly. The aim is to allocate ‘n’ facilities to ‘n’ locations thus reducing the sum of flow times distance. It is shown in many research that 20-30% of overall manufacturing cost is occupied by material handling. The reduction in material handling can   be achieved by optimizing the plant layout with effectively implementing the optimization techniques. This paper eventually deals with implementing Ant Colony Optimization and Genetic algorithm to industrial plant layout with an  aim  to minimize the sum of flow times distance using MATLAB as an tool to design the program. The problem is framed mathematically using Quadratic Assignment Problem and the algorithms are validated by implementing them on the benchmark problem available in the QAPLIB.


Full Text:

PDF

References


Boaz Golany and Meir J. Rosenblatt, 1989. A heuristic algorithm for the quadratic assignment formulation to the plant layout problem. Vol. 27, No. 2, 293-308.

C.M.L Castell, R Lakshmanan, J. M. Skilling, R. Banares-Alcantara., 1998. Optimization of process plant layout using genetic algo- rithms,Computers Chem. Engng Vol.22, Suppl., pp. S993-S996.

S.K. Chaharsooghi, 2006. An effective ant colony optimization algorithm (ACO) for multi-objective resource allocation problem (MORAP), Ap- plied Mathematics and Computation 200, Pp. 167-177.

Y. Hani , L. Amodeo, F. Yalaoui, H. Chen.,2007. Ant Colony Opti- mization for solving an industrial layout problem, European Journal of Operation,633-642.

Surendra Kumar, C.S.P. Rao, 2009. application of ant colony, genetic algorithm and data mining-based techniques for scheduling, Robotics and Computer-Integrated Manufacturing 25, Pp. 901-908.

Yeo Keun Kim, Won Seop Song, Jun Hyuk Kim, 2007. A mathematical model and a genetic algorithm for two-sided assembly line balancing, Compute and Operations Research 36, Pp. 853-865.

Yuichi Nagata, David Soler, 2012. A new genetic algorithm for the asym- metric travelling salesman problem, Expert Systems with Applications 39, Pp. 8947-8953.

Zhang Wei-guo, LuTian-yu., 2012. The research of genetic ant colony algorithms and its application, Procedia Engineering 37, Pp. 101-106.

Malek Mouhoub, Zhijie Wang, 2008. Improving the ant colony opti- mization algorithm for the Quadratic Assignment Problem, Evolutionary Computation.

Prasanna Balaprakash, Mauro Biratari, Thomas Stuzle, Marco Dorigo,2006.Incremental local search in ant colony optimization: Why it fails for the quadratic assignment problem, Evolutionary Computation.

Nihan Cetin Demirel, M. Duran Toksari, 2006. Optimization of quadratic assignment problem using an ant colony optimization, Applied Mathemat- ics and Computation 183, Pp.427-435.

Xin Li,Lael Parrott, 2016. An improved genetic algorithm for spatial optimization of multi-objective and multi-site land use allocation, Com- puters, Environment and Urban Systems, Pp. 184-194.

G.Aiello, M.Enea, G.Galante, 2005. A multi-objective approach to facility layout problem by genetic search algorithm and Electre method, Robotic and Computer Integrated Manufacturing, Pp.447-455.

Sahu, S.K., and Pandey, M., 2014. Hybrid Ant System Algorithm for Solving Quadratic Assignment Problems, International Journal of Computer Science and Technologies, Vol.(5) 4, P.p. 5950-5956.

Rosenblatt, M.J., and Golany, B., 1992. A distance assignment approach to the facility layout problem, European Journal of Operational Research, P.p. 253-270.

Tiwari, P.K., and Vidyarthi, D.P., 2015. Improved Auto Control Ant Colony Optimization using Lazy Ant Approch for Grid Scheduling Problem, Applied Soft Computing.

Kulkarni, C.N., Talib, M.I., and Jahagirdar, R.S., 2013. Simulation Methodology for Facility Layout Problems, The International Journal of Engineering, Volume. 2 Issue. 2, P.p. 24-30.

Ficko, M., and Palcic, I., 2013. Designing a layout using modified trian- gle method, and Genetic Algorithms, International Journal of Simulation Model, Vol. 4, P.p 237-251.


Refbacks

  • There are currently no refbacks.


 

Copyright © IJETT, International Journal on Emerging Trends in Technology