Operation Sequencing Optimization in CAPP Using Hybrid Teaching-Learning Based Optimization (HTLBO)
الموضوعات :Hassan Halleh 1 , Azam Sadati 2 , Nasser Hajisharifi 3
1 - Golpayegan University of Technology, Golpayegan, Iran
2 - Islamic Azad University, Khomein Branch, Khomein, Iran
3 - Department of mathematics, Collage of basic sciences, Islamic azad university, Khomein branch, Khomein, Iran
الکلمات المفتاحية: Computer-aided process planning (CAPP), Operation sequence, Hamilton path, Teaching–learning-based optimization,
ملخص المقالة :
Computer-aided process planning (CAPP) is an essential component in linking computer-aided design (CAD) and computer-aided manufacturing (CAM). Operation sequencing in CAPP is an essential activity. Each sequence of production operations which is produced in a process plan cannot be the best possible sequence every time in a changing production environment. As the complexity of the product increases, the number of feasible sequences increase exponentially, consequently the best sequence is to be chosen. This paper aims at presenting the application of a newly developed meta-heuristic called the hybrid teaching–learning-based optimization (HTLBO) as a global search technique for the quick identification of the optimal sequence of operations with consideration of various feasibility constraints. To do so, three case studies have been conducted to evaluate the performance of the proposed algorithm and a comparison between the proposed algorithm and the previous searches from the literature has been made. The results show that HTLBO performs well in operation sequencing problem.
Bhaskara Reddy, S.V., Shunmugam, M.S., & Narendran, T.T. (1999). Operation sequencing in CAPP using genetic algorithms. International Journal of Production Research, 37(5), 1063–1074.
Castro de Andrade, R. (2016). New formulations for the elementary shortest-path problem visiting a given set of nodes. European Journal of Operational Research, 254, 755–768.
Chen, X., Zhou,Y., Tang ,Z., and Luo,Q. (2017). A hybrid algorithm combining glowworm swarm optimization and complete 2-opt algorithm for spherical travelling salesman problems. Applied Soft Computing, 58, 104–114.
Doua, J., Li, J. & Su, C. (2018). A discrete particle swarm optimisation for operation sequencing in CAPP. International Journal of Production Research, 56(11), 3795- 3814.
Guo, Y.W., Mileham, A.R., Owen, G.W., & Li, W.D. (2006). Operation sequencing optimization using a particle swarm optimization approach. Proceeding of the Institution of Mechanical Engineering, Part B: Journal of Engineering Manufacture, 220(B12), 1945– 1958.
Halevi, G., & Weill, R. (1988). Development of flexible optimum process planning procedures. CIRP, 29(1), 313–317.
Hosseini S.M.H.(2019). Modelling and Solving the Job Shop Scheduling Problem Followed by an Assembly Stage Considering Maintenance Operations and Access Restrictions to Machines. Journal of Optimization in Industrial Engineering. 12 (1), 63- 78.
Hu, Q., Qiao, L., & Peng, G. (2017). An Ant Colony Approach to Operation Sequencing Optimization in Process Planning. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 231(3), 470–489.
Keshavarz-Kohjerdi, F., & Bagheri, A. (2017). A linear- time algorithm for finding Hamiltonian (s,t)-paths in even-sized rectangular grid graphs with a rectangular hole. Theoretical Computer Science, 690, 26-58.
Krishna, A.G., and Rao, K.M. (2006). Optimisation of operations sequence in CAPP using an ant colony algorithm. The International Journal of Advanced Manufacturing Technology, 29, 159–164.
Korde U.P., Bora B.C., Stelson K.A., & Riley D.R. (1992) Computer-Aided Process Planning for Turned Parts Using Fundamental and Heuristic Principles, Journal of Engineering for Industry, 114, 31–40.
Koulamas, C. (1993). Operation sequencing and machining economics. International Journal of Production Research, 31(4), 957–975.
Li, L., Fuh, J.Y.H., Zhang, Y.F., & Nee, A.Y.C. (2005). Application of genetic algorithm to computer-aided process planning in distributed manufacturing environments. Robotics and Computer-Integrated Manufacturing, 21, 568–578.
Li, W.D., Ong, S.K., & Nee, A.Y.C. (2004). Optimization of process plans using a constraint-based tabu search approach. International Journal of Production Research, 42(10), 1955–1985.
Lin, C.J., & Wang, H.P. (1993). Optimal operation planning and sequencing; minimization of tool changeovers. International Journal of Production Research, 31(2), 311–324.
Nallakumarasamy, G., Srinivasan, P.S.S., Venkatesh Raja, K., & Malayalamurthi, R. (2011). Optimization of operation sequencing in CAPP using simulated annealing technique (SAT). International Journal of Advanced Manufacturing Technology, 54, 721–728.
Rao, R.V., Savsani, V.J., and Vakharia, D.P. (2011). Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43, 303–315.
Salehi, M. & Tavakkoli-Moghaddam, R. (2009). Application of genetic algorithm to computer-aidedprocess planning in prelimirary and detailed planning. Engineering Applications of Artificial Intelligence, 22, 1179-1187.
Türkay, D.I., & Hüseyin, F. (1999). Optimisation of process planning functions by genetic algorithms. International Journal of Computer and Industrial Engineering, 36(2), 281–308.
Weill, R., Spur, G., & Eversheim, W. (1982). Survey of computer-aided process planning systems. CIRP Annals, 31(2), 539–551.