JILSA  Vol.6 No.2 , May 2014
Simulation Model Using Meta Heuristic Algorithms for Achieving Optimal Arrangement of Storage Bins in a Sawmill Yard

Bin planning (arrangements) is a key factor in the timber industry. Improper planning of the storage bins may lead to inefficient transportation of resources, which threaten the overall efficiency and thereby limit the profit margins of sawmills. To address this challenge, a simulation model has been developed. However, as numerous alternatives are available for arranging bins, simulating all possibilities will take an enormous amount of time and it is computationally infeasible. A discrete-event simulation model incorporating meta-heuristic algorithms has therefore been investigated in this study. Preliminary investigations indicate that the results achieved by GA based simulation model are promising and better than the other meta-heuristic algorithm. Further, a sensitivity analysis has been done on the GA based optimal arrangement which contributes to gaining insights and knowledge about the real system that ultimately leads to improved and enhanced efficiency in sawmill yards. It is expected that the results achieved in the work will support timber industries in making optimal decisions with respect to arrangement of storage bins in a sawmill yard.

Cite this paper: Rahman, A. , Yella, S. and Dougherty, M. (2014) Simulation Model Using Meta Heuristic Algorithms for Achieving Optimal Arrangement of Storage Bins in a Sawmill Yard. Journal of Intelligent Learning Systems and Applications, 6, 125-139. doi: 10.4236/jilsa.2014.62010.

[1]   Silva, C.A., Sousa, J.M. and Runkler, T.A. (2008) Rescheduling and Optimization of Logistics Process Using GA and ACO. Journal of Engineering Application of Artificial Intelligence, 21, 343-352.

[2]   Liu, S.S. and Papageorgiou, L.G. (2013) Multi-Objective Optimization of Production, Distribution and Capacity Planning of Global Supply Chain in the Process Industry. Journal of Omega, 41, 369-382.

[3]   Asif, R., Vald, S., Pascal, R. and Siril, Y. (2012) Multi-Agent Simulation of Sawmill Yard Operations. IASTED Proceedings of Simulation and Modelling, Napoli.

[4]   Asif, R., Siril, Y. and Mark, D. (2011) Image Processing Technique to Count the Number of Timber Logs in a Truck. IASTED Proceedings of Signal and Image Processing, Dallas.

[5]   Chen, E.J., Lee, Y.M. and Selikson, P.L. (2002) A Simulation Study on Logistic Activities in a Chemical Plant. Journal of Simulation Modelling Practice and Theory, 10, 235-245.

[6]   Azadivar, F. and Wang, J. (2000) Facility Layout Optimization Using Simulation and Genetic Algorithm. International Journal of Production Research, 38, 4369-4383.

[7]   Shin, Y., Cho, H. and Kang, K. (2011) Simulation Model Incorporating Genetic Algorithm for Optimal Temporary Hoist Planning in High-Rise Building Construction. Journal of Automation in Construction, 20, 550-558.

[8]   Shin-ike, K. and Iima, H. (2011) A Method for Determining Classroom Seating Arrangements by Using Genetic Algorithm. SICE Annual Conference, Tokyo.

[9]   Yeh, J.Y. and Lin, W.S. (2006) Using Simulation Techniques and Genetic Algorithm to Improve the Quality Care of a Hospital Emergency Department. Journal of Expert System with Applications, 32, 1073-1083.

[10]   Goldspink, C. (2002) Methodological Implications of Complex Systems Approach to Sociality: Simulation as a Foundation for Knowledge. Journal of Artificial Societies and Social Simulation, 5, 1-19.

[11]   Albino, V., Carbonara, N. and Gainnoccaro, I. (2007) Supply Chain Cooperation in Industrial Districts: A Simulation Analysis. European Journal of Operation Research, 117, 261-280.

[12]   Javadi, A.A., Farmani, R. and Tan, T.P. (2005) A Hybrid Intelligent Genetic Algorithm. Journal of Advance Engineering Informatics, 19, 255-262.

[13]   Ceylan, H. and Ozturk, H.K. (2004) Estimating Energy Demand of Turkey Based on Economic Indicators Using Genetic Algorithm Approach. Journal of Energy Conversion and Management, 45, 2525-2537.

[14]   D’souza, C., Omkar, S.N. and Senthilnatj, J. (2012) Pickup and Delivery Problem Using Meta-Heuristic Techniques. Journal of Expert System with Applications, 39, 328-334.

[15]   Daniels, A.S. and Parson, M.G. (2007) Development of Hybrid Agent Genetic Algorithm Approach to General Arrangements. Proceedings of Computer Applications and Information Technology in the Maritime Industries.

[16]   Michalewicz, Z. and Fogel, D.B. (2003) How to Solve It: Modern Heuristics. Springer, Berlin.

[17]   Xin, Z., Hongnian, Y. and Anthony, A. (2008) An Overview of Simulation in Supply Chains. Advanced Design and Manufacture to Gain a Competitive Edge, Book Chapter 3. 407-416.

[18]   Reeves, C. (1990) Modern Heuristic Techniques for Combinatorial Problems. John Wiley & Sons, Chichester, England.

[19]   Vaidyanathan, J. and Anthony, R. (2003) A Simulated Annealing Methodology to Distribution Network Design and Management. European Journal of Operation Research, 144, 629-645.

[20]   Hamid, R.S. and Keivan, G. (2009) A Simulated Annealing Approach for Multi-Periodic Rail-Car Fleet Sizing Problem. Journal of Computers & Operation Research, 36, 1789-1799.

[21]   Allaoui, H. and Artiba, A. (2004) Integrating Simulation and Optimization to Schedule a Hybrid Flow Shop with Maintenance Constraints. Computers & Industrial Engineering, 47, 431-450.

[22]   Suman, B. (2004) Study of Simulated Annealing Based Algorithms for Multiobjective Optimization of a Constrained Problem. Computers and Chemical Engineering, 28, 1849-1871.

[23]   Mahmoud, A., Ali, D., Ameen, A., Raid, A. and Nishat, F.M. (2013) Simulated Annealing for Multi Objective Stochastic Optimization. International Journal of Science and Applied Information Technology, 2, 18-21.

[24]   Kelton, W.D., Sadowski and Swets, N. (2009) Simulation with Arena. 5th Edition, McGraw Hill, New York.

[25]   Laerd Statistics (2013) One way ANOVA Analysis.

[26]   Osborne, J. (2010) Improving Your Data Transformations: Applying the Box-Cox Transformation. Practical Assessment, Research & Evaluation, 15, 2010.