A hybrid particle swarm optimization and tabu search algorithm for order planning problems of steel factories based on the Make-To-Stock and Make-To-Order management architecture

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January 2011, 7(1): 31-51. doi: 10.3934/jimo.2011.7.31

A hybrid particle swarm optimization and tabu search algorithm for order planning problems of steel factories based on the Make-To-Stock and Make-To-Order management architecture

Tao Zhang ^1, , Yue-Jie Zhang ^2, , Qipeng P. Zheng ^3, and P. M. Pardalos ^4,

1.	School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433
2.	School of Computer Science, Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 200433
3.	Department of Industrial and Management Systems Engineering, PO Box 6070, West Virginia University, Morgantown, WV 26505, United States
4.	Center for Applied Optimization, Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611, United States

Received November 2009 Revised September 2010 Published January 2011

Abstract
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This paper presents the production planning management architecture for iron-steel manufacturing factories based on Make-To-Order (MTO) and Make-To-Stock (MTS) management ideas. Within this architecture, we discuss the procedures of order planning in details and construct a nonlinear integer programming model for the order planning problem. This model takes into account inventory matching and production planning simultaneously, and considers multiple objectives, such as the total cost of earliness/tardiness penalty, tardiness penalty in delivery time window, production, inventory matching and order cancelation penalty. In order to solve this nonlinear integer program, this paper designs a hybrid Particle Swarm Optimization (PSO) and Tabu Search (TS) algorithm, in which new heuristic rules to repair infeasible solutions are proposed, and then analyzes the parameter settings for PSO and the combined algorithm by simulations. This paper also compares the results of using PSO individually, TS individually, and the hybrid PSO/TS algorithm to solve the models with three different order quantities. Numerical results show that the hybrid PSO/TS algorithm provides better solutions while being computationally efficient.

Keywords: MTS (Make To Stock), PSO (particle swarm optimization), MTO (Make To Order), TS (Tabu Search), hybrid PSO/TS., order planning, inventory matching.

Mathematics Subject Classification: Primary: 90B30, 90C59; Secondary: 90B3.

Citation: Tao Zhang, Yue-Jie Zhang, Qipeng P. Zheng, P. M. Pardalos. A hybrid particle swarm optimization and tabu search algorithm for order planning problems of steel factories based on the Make-To-Stock and Make-To-Order management architecture. Journal of Industrial & Management Optimization, 2011, 7 (1) : 31-51. doi: 10.3934/jimo.2011.7.31

References:

[1]	I. J. B. F. Adan and J. van der Wal, Combing make to order and make to stock,, OR Spectrum, 20 (1998), 73. doi: 10.1007/BF01539854. Google Scholar
[2]	A. Arreola-risa and G. A. DeCroix, Make-to-order versus make-to-stock in a production-inventory system with general production times,, IIE Transactions, 30 (1998), 705. doi: 10.1080/07408179808966516. Google Scholar
[3]	A. Balakrishnan and J. Guenes, Production planning with flexible product specifications: an application to special steel manufacturing,, Operations Research, 51 (2003), 94. doi: 10.1287/opre.51.1.94.12791. Google Scholar
[4]	M. Ben-Daya and M. Al-Fawzan, A tabu search approach for the flow shop scheduling problem,, European Journal of Operational Research, 109 (1998), 88. doi: 10.1016/S0377-2217(97)00136-7. Google Scholar
[5]	D. Corti, A. Pozzetti and M. Zorzini, A capacity-driven approach to establish reliable due dates in a MTO environment,, International Journal of Production Economics, 104 (2006), 536. doi: 10.1016/j.ijpe.2005.03.003. Google Scholar
[6]	P. Cowling, A flexible decision support system for steel hot rolling mill scheduling,, Computers & Industrial Engineering, 45 (2003), 307. doi: 10.1016/S0360-8352(03)00038-X. Google Scholar
[7]	B. Denton, D. Gupta and K. Jawahir, Managing increasing products variety at integrated steel mills,, Interfaces, 33 (2003), 41. Google Scholar
[8]	G. Dutta and R. Fourer, An optimization-based decision support system for strategic and operational planning in process industries,, Optimization and Engineering, 5 (2004), 295. doi: 10.1023/B:OPTE.0000038888.65465.4e. Google Scholar
[9]	V. Ganapathy, S. Marimuthu and S. G. Ponnambalam, Tabu search and simulated annealing algorithms for lot-streaming in two-machine flowshop,, IEEE International Conference on Systems, (2004), 4221. Google Scholar
[10]	J. Grabowski and J. Pempera, The permutation flow shop problem with blocking: A tabu search approach,, International Journal of Management Science, 35 (2007), 302. Google Scholar
[11]	Z. Gao and L. Tang, A multi-object model for purchasing of bulk raw materials of a large-scale integrated steel plant,, International Journal of Production Economics, 83 (2003), 325. doi: 10.1016/S0925-5273(02)00373-0. Google Scholar
[12]	K. Hu, W. Chen, D. Wang and B. Zheng, Research for joint optimization of inventory matching and production planning considering mass factor, (Chinese),, Theory Methodology applications, 13 (2004), 199. Google Scholar
[13]	J. R. Kalagnamam, M. W. Dewande, M. Trumbo and S. H. Lee, The surplus inventory matching problem in the process industry,, Operations Research, 48 (2000), 505. doi: 10.1287/opre.48.4.505.12425. Google Scholar
[14]	J. Kennedy and R. Eberhart, Particle swarm optimization,, IEEE Service Center, 4 (1995), 1942. Google Scholar
[15]	K. Kogan, E. Khmelnitsky and O. Maimon, Balancing facilities in aggregate production planning: Make-to-order and make-to-stock environments,, International Journal of Production Research, 36 (1998), 2585. doi: 10.1080/002075498192715. Google Scholar
[16]	Z. Liang, X. Gu and B. Jiao, A novel particle swarm optimization algorithm for permutation flowshop scheduling to minimize makespan,, Chaos, 35 (2008), 851. doi: 10.1016/j.chaos.2006.05.082. Google Scholar
[17]	S. Liu, J. Tang and J. Song, Order-planning model and algorithm for manufacturing steel sheets,, International Journal of Production Economics, 100 (2006), 30. doi: 10.1016/j.ijpe.2004.10.002. Google Scholar
[18]	S. Q. Liu, H. L. Ong and K. M. Ng, A fast tabu search algorithm for the group shop scheduling problem,, Advances in Engineering Software, 36 (2005), 533. Google Scholar
[19]	J. Meredith and U. Akinc, Characterizing and structuring a new make-to-forecast production strategy,, Journal of Operations Management, 25 (2007), 623. doi: 10.1016/j.jom.2006.04.006. Google Scholar
[20]	V. Nguyen, A multiclass hybrid production center in heavy traffic,, Operations Research, 46 (1998), 13. doi: 10.1287/opre.46.3.S13. Google Scholar
[21]	Q. K. Pan, M. F. Tasgetiren and Y. C. Liang, A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem,, Computers and Operations Research, 35 (2008), 2807. doi: 10.1016/j.cor.2006.12.030. Google Scholar
[22]	P. M. Pardalos and V. Korotkikh, "Optimization and Industry: New Frontiers,", Kluwer Academic Publishers, (2003). Google Scholar
[23]	P. M. Pardalos and M. G. C. Resende, "Handbook of Applied Optimization,", Oxford University Press, (2002). Google Scholar
[24]	H. Rezazadeh, M. Ghazanfari, M. Saidi-Mehrabad and S. J. Sadjadi, An extended discrete particle swarm optimization algorithm for the dynamic facility layout problem,, Journal of Zhejiang University Science A, 10 (2009), 520. doi: 10.1631/jzus.A0820284. Google Scholar
[25]	C. A. Soman, D. P. van Donk and G. Gaalman, Combined make-to-order and make-to-stock in a food production system,, International Journal of Production Economics, 90 (2004), 223. doi: 10.1016/S0925-5273(02)00376-6. Google Scholar
[26]	C. A. Soman, D. P. van Donk and G. Gaalman, Comparison of dynamic scheduling policies for hybrid make-to-order and make-to-stock production systems with stochastic demand,, International Journal of Production Economics, 104 (2004), 441. doi: 10.1016/j.ijpe.2004.08.002. Google Scholar
[27]	I. C. Trelea, The particle swarm optimization algorithm: convergence analysis and parameter selection,, Information Processing Letters, 85 (2003), 317. doi: 10.1016/S0020-0190(02)00447-7. Google Scholar
[28]	H. Tsubone, Y. Ishikawa and H. Yamamoto, Production planning system for a combination of make-to-stock and make-to-order products,, International Journal of Production Research, 40 (2002), 4835. doi: 10.1080/00207540210158834. Google Scholar
[29]	K. H. Youssef, C. van Delft and Y. Dallery, Efficient scheduling rules in a combined make-to-stock and make-to-order manufacturing system,, Annals of Operations Research, 126 (2004), 103. doi: 10.1023/B:ANOR.0000012277.97069.a6. Google Scholar
[30]	T. Zhang, M. Wang, L. Tang, J. Song and J. Yang, The method for the order planning of the steel plant based on the MTO management system, (Chinese),, Control and Decision, 15 (2000), 649. Google Scholar
[31]	T. Zhang, Y. Zhang and S. Liu, A mixed integer programming model and improved genetic algorithm for order planning of Iron-Steel Plants,, International Journal of Information and Management Science, 19 (2008), 413. Google Scholar
[32]	T. Zhang, W. A. Chaovalitwongse, Y. Zhang and P. M. Pardalos, The hot-rolling batch scheduling method based on the prize collecting vehicle routing problem,, Journal of Industrial and Management Optimization, 5 (2009), 749. doi: 10.3934/jimo.2009.5.749. Google Scholar

show all references

References:

[1]	I. J. B. F. Adan and J. van der Wal, Combing make to order and make to stock,, OR Spectrum, 20 (1998), 73. doi: 10.1007/BF01539854. Google Scholar
[2]	A. Arreola-risa and G. A. DeCroix, Make-to-order versus make-to-stock in a production-inventory system with general production times,, IIE Transactions, 30 (1998), 705. doi: 10.1080/07408179808966516. Google Scholar
[3]	A. Balakrishnan and J. Guenes, Production planning with flexible product specifications: an application to special steel manufacturing,, Operations Research, 51 (2003), 94. doi: 10.1287/opre.51.1.94.12791. Google Scholar
[4]	M. Ben-Daya and M. Al-Fawzan, A tabu search approach for the flow shop scheduling problem,, European Journal of Operational Research, 109 (1998), 88. doi: 10.1016/S0377-2217(97)00136-7. Google Scholar
[5]	D. Corti, A. Pozzetti and M. Zorzini, A capacity-driven approach to establish reliable due dates in a MTO environment,, International Journal of Production Economics, 104 (2006), 536. doi: 10.1016/j.ijpe.2005.03.003. Google Scholar
[6]	P. Cowling, A flexible decision support system for steel hot rolling mill scheduling,, Computers & Industrial Engineering, 45 (2003), 307. doi: 10.1016/S0360-8352(03)00038-X. Google Scholar
[7]	B. Denton, D. Gupta and K. Jawahir, Managing increasing products variety at integrated steel mills,, Interfaces, 33 (2003), 41. Google Scholar
[8]	G. Dutta and R. Fourer, An optimization-based decision support system for strategic and operational planning in process industries,, Optimization and Engineering, 5 (2004), 295. doi: 10.1023/B:OPTE.0000038888.65465.4e. Google Scholar
[9]	V. Ganapathy, S. Marimuthu and S. G. Ponnambalam, Tabu search and simulated annealing algorithms for lot-streaming in two-machine flowshop,, IEEE International Conference on Systems, (2004), 4221. Google Scholar
[10]	J. Grabowski and J. Pempera, The permutation flow shop problem with blocking: A tabu search approach,, International Journal of Management Science, 35 (2007), 302. Google Scholar
[11]	Z. Gao and L. Tang, A multi-object model for purchasing of bulk raw materials of a large-scale integrated steel plant,, International Journal of Production Economics, 83 (2003), 325. doi: 10.1016/S0925-5273(02)00373-0. Google Scholar
[12]	K. Hu, W. Chen, D. Wang and B. Zheng, Research for joint optimization of inventory matching and production planning considering mass factor, (Chinese),, Theory Methodology applications, 13 (2004), 199. Google Scholar
[13]	J. R. Kalagnamam, M. W. Dewande, M. Trumbo and S. H. Lee, The surplus inventory matching problem in the process industry,, Operations Research, 48 (2000), 505. doi: 10.1287/opre.48.4.505.12425. Google Scholar
[14]	J. Kennedy and R. Eberhart, Particle swarm optimization,, IEEE Service Center, 4 (1995), 1942. Google Scholar
[15]	K. Kogan, E. Khmelnitsky and O. Maimon, Balancing facilities in aggregate production planning: Make-to-order and make-to-stock environments,, International Journal of Production Research, 36 (1998), 2585. doi: 10.1080/002075498192715. Google Scholar
[16]	Z. Liang, X. Gu and B. Jiao, A novel particle swarm optimization algorithm for permutation flowshop scheduling to minimize makespan,, Chaos, 35 (2008), 851. doi: 10.1016/j.chaos.2006.05.082. Google Scholar
[17]	S. Liu, J. Tang and J. Song, Order-planning model and algorithm for manufacturing steel sheets,, International Journal of Production Economics, 100 (2006), 30. doi: 10.1016/j.ijpe.2004.10.002. Google Scholar
[18]	S. Q. Liu, H. L. Ong and K. M. Ng, A fast tabu search algorithm for the group shop scheduling problem,, Advances in Engineering Software, 36 (2005), 533. Google Scholar
[19]	J. Meredith and U. Akinc, Characterizing and structuring a new make-to-forecast production strategy,, Journal of Operations Management, 25 (2007), 623. doi: 10.1016/j.jom.2006.04.006. Google Scholar
[20]	V. Nguyen, A multiclass hybrid production center in heavy traffic,, Operations Research, 46 (1998), 13. doi: 10.1287/opre.46.3.S13. Google Scholar
[21]	Q. K. Pan, M. F. Tasgetiren and Y. C. Liang, A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem,, Computers and Operations Research, 35 (2008), 2807. doi: 10.1016/j.cor.2006.12.030. Google Scholar
[22]	P. M. Pardalos and V. Korotkikh, "Optimization and Industry: New Frontiers,", Kluwer Academic Publishers, (2003). Google Scholar
[23]	P. M. Pardalos and M. G. C. Resende, "Handbook of Applied Optimization,", Oxford University Press, (2002). Google Scholar
[24]	H. Rezazadeh, M. Ghazanfari, M. Saidi-Mehrabad and S. J. Sadjadi, An extended discrete particle swarm optimization algorithm for the dynamic facility layout problem,, Journal of Zhejiang University Science A, 10 (2009), 520. doi: 10.1631/jzus.A0820284. Google Scholar
[25]	C. A. Soman, D. P. van Donk and G. Gaalman, Combined make-to-order and make-to-stock in a food production system,, International Journal of Production Economics, 90 (2004), 223. doi: 10.1016/S0925-5273(02)00376-6. Google Scholar
[26]	C. A. Soman, D. P. van Donk and G. Gaalman, Comparison of dynamic scheduling policies for hybrid make-to-order and make-to-stock production systems with stochastic demand,, International Journal of Production Economics, 104 (2004), 441. doi: 10.1016/j.ijpe.2004.08.002. Google Scholar
[27]	I. C. Trelea, The particle swarm optimization algorithm: convergence analysis and parameter selection,, Information Processing Letters, 85 (2003), 317. doi: 10.1016/S0020-0190(02)00447-7. Google Scholar
[28]	H. Tsubone, Y. Ishikawa and H. Yamamoto, Production planning system for a combination of make-to-stock and make-to-order products,, International Journal of Production Research, 40 (2002), 4835. doi: 10.1080/00207540210158834. Google Scholar
[29]	K. H. Youssef, C. van Delft and Y. Dallery, Efficient scheduling rules in a combined make-to-stock and make-to-order manufacturing system,, Annals of Operations Research, 126 (2004), 103. doi: 10.1023/B:ANOR.0000012277.97069.a6. Google Scholar
[30]	T. Zhang, M. Wang, L. Tang, J. Song and J. Yang, The method for the order planning of the steel plant based on the MTO management system, (Chinese),, Control and Decision, 15 (2000), 649. Google Scholar
[31]	T. Zhang, Y. Zhang and S. Liu, A mixed integer programming model and improved genetic algorithm for order planning of Iron-Steel Plants,, International Journal of Information and Management Science, 19 (2008), 413. Google Scholar
[32]	T. Zhang, W. A. Chaovalitwongse, Y. Zhang and P. M. Pardalos, The hot-rolling batch scheduling method based on the prize collecting vehicle routing problem,, Journal of Industrial and Management Optimization, 5 (2009), 749. doi: 10.3934/jimo.2009.5.749. Google Scholar

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