Abstract
This paper addresses a new mathematical model for cellular manufacturing problem integrated with group scheduling in an uncertain space. This model optimizes cell formation and scheduling decisions, concurrently. It is assumed that processing time of parts on machines is stochastic and described by discrete scenarios enhances application of real assumptions in analytical process. This model aims to minimize total expected cost consisting maximum tardiness cost among all parts, cost of subcontracting for exceptional elements and the cost of resource underutilization. Scheduling problem in a cellular manufacturing environment is treated as group scheduling problem, which assumes that all parts in a part family are processed in the same cell and no inter-cellular transfer is needed. Finally, the nonlinear model will be transformed to a linear form in order to solve it for optimality. To solve such a stochastic model, an efficient hybrid method based on new combination of genetic algorithm (GA), simulated annealing (SA) algorithm, and an optimization rule will be proposed where SA and optimization rule are subordinate parts of GA under a self-learning rule criterion. Also, performance and robustness of the algorithm will be verified through some test problems against branch and bound and a heuristic procedure.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Heragu S (1997) Facilities design. PWS publishing company, Boston, p 316
Papaioannou G, Wilson JM (2008) Fuzzy extensions to integer programming of cell formation problem in machine scheduling. Ann Oper Res 166:1–19
Hentschel C, Seliger G, Zussman E (1995) Grouping of used products for cellular recycling systems. CIRP Ann Manuf Technol 44(1):11–14
Shanker R, Vrat P (1999) Some design issues in C.M. using the fuzzy programming approach. Int J Prod Res 37(11):2545–2563
Szwarc D, Rajamani D, Bector CR (1997) Cell formation considering fuzzy demand and machine capacity. Int J Adv Manuf Technol 13(2):134–147
Ravichandran KS, Chandra Sekhara Rao K (2001) A new approach to fuzzy part family formation in CMS. Int J Adv Manuf Technol 18(8):591–597
Song S, Hitomi K (1991) Determining the planning horizon and group part family for flexible cellular manufacturing. Nippon Kikai Gakkai Ronbunshu, C Hen/Trans Jpn Soc Mech Eng C 57(542):3364–3371
Hurley SF, Clay Whybark D (1999) Inventory and capacity trade-off in a manufacturing cell. Int J Prod Econ 59(1):203–212
Tavakkoli-Moghaddam R, Javadian N, Javadi B, Safaei N (2007) Design of a facility layout problem in CMS with stochastic demand. Appl Math Comput 184(2):721–728
Balakrishnan J, Cheng CH (2005) Dynamic C.M. under multi-period planning horizon. J Manuf Technol Manage 16(5):516–530
Balakrishnan J, Cheng CH (2007) Multi-period planning and uncertainty issues in C.M.: a review and future directions. Eur J Oper Res 177(1):281–309
Yang J, Deane RH (1993) Setup time reduction and competitive advantage in a closed manufacturing cell. Eur J Oper Res 69(3):413–423
Kuroda M, Tomita T (2005) Robust design of a cellular—line production system with unreliable facilities. Comput Ind Eng 48(3):537–551
Hosseini MM (2000) An inspection model with minimal and major maintenance for a system with deterioration and poison failures. IEEE Trans Reliab 49(1):88–98
Gupta SM, Kavusturucu A (1998) Modeling of finite buffer cellular manufacturing systems with unreliable machines. Int J Ind Eng Theory Appl Pract 5(4):265–277
Simeu-Abazi Z, Sassine C (1999) Maintenance integration in manufacturing systems using stochastic Petri nets. Int J Prod Res 37(17):3927–3940
Asgharpour MJ, Javadian N (2004) Solving a stochastic cellular manufacturing model using genetic algorithm. Int J Eng Trans A: Basics 17(2):145–156
Sun Y-L, Yih Y (1996) An intelligent controller for manufacturing cell. Int J Prod Res 34(8):2353–2373
Andres C, Lozano S, Adenso-Diaz B (2007) Disassembly sequence planning in a disassembly cell. Robot Comput Integr Manuf 23(6):690–695
Taylor JF, Ham I (1981) The use of a micro computer for grouping scheduling. In: Proceedings of the 9th North American Manufacturing Research Conference (NAMRC), Society of Manufacturing Engineers, pp 483–491
Logendran R, Nudtasomboon N (1991) Minimizing the makespan of a group scheduling problem: a new heuristic. Int J Prod Econ 22:217–230
Solimanpur M, Vrat P, Shankar R (2004) A heuristic to minimize makespan of cell scheduling problem. Int J Prod Econ 88:231–241
Aneja YP, Kamoun H (1999) Scheduling of parts and robot activities in a two machine robotic cell. Comput Oper Res 26:297–312
Lockwood WT, Mahmoodi F, Ruben RA, Mosier CT (2000) Scheduling unbalanced cellular manufacturing systems with lot splitting. Int J Prod Res 38(4):951–965
Tsai CC, Chu CH, Barta T (1997) Analysis and modeling of a manufacturing cell formation problem with fuzzy integer programming. IIE Trans 29(7):533–547
Vakharia AJ, Kaku BK (1993) Redesigning a cellular manufacturing system to handle long-term demand changes: a methodology and investigation. Decis Sci 24(5):909–917
Mahdavi I, Javadi B, Fallah-Alipour K, Slomp J (2007) Designing a new mathematical model for cellular manufacturing system based on cell utilization. Appl Math Comput 190:662–670
Wu XD, Chu CH, Wang YF, Yan WL (2006) Concurrent design of cellular manufacturing systems: a genetic algorithm approach. Int J Prod Res 44(6):1217–1241
Venkataramanaiah S (2007) Scheduling in cellular manufacturing systems: an heuristic approach. Int J Prod Res 99999(1):1–21
Sridhar J, Rajendran C (1993) Scheduling in a cellular manufacturing system: a simulated annealing approach. Int J Prod Res 31(12):2927–2945
Mahmoodi F, Martin GE (1997) A new shop-based and predictive scheduling heuristic for cellular manufacturing. Int J Prod Res 35(2):313–326
Saad SM (2003) The reconfiguration issues in manufacturing systems. J Mater Process Technol 138:277–283
Panchalavarapu PR, Chankong V (2005) Design of cellular manufacturing systems with assembly considerations. Comput Ind Eng 48(3):449–469
Ghezavati VR, Jabal-Ameli MS, Makui A (2009) A new heuristic method for distribution networks considering service level constraint and coverage radius. Expert Syst Appl 36(3):5620–5629 Part 1
Mobasheri F, Orren LH, Sioshansi FP (1989) Scenario planning at southern California Edison. Interfaces 19(5):31–44
Shanker R, Vrat P (1998) Post design modeling for CMS with cost uncertainty. Int J Prod Econ 55(1):97–109
Snyder LV (2006) Facility location under uncertainty: a review. IIE Trans 38:537–554
Glover F, Woolsey L (1974) Converting the 0–1 polynomial programming problem to a 0–1 linear program. Oper Res 22:180–182
Chang C-T, Chang C-C (2000) A linearization method for mixed 0–1 polynomial programs. Comput Oper Res 27:1005–1016
Safaei N, Saidi-Mehrabad M, Jabal-Ameli MS (2008) A hybrid simulated annealing for solving an extended model of dynamic cellular manufacturing system. Eur J Oper Res 185:563–592
Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680
Jeon G, Leep HR (2006) Forming part families by using genetic algorithm and designing machine cells under demand changes. Comput Oper Res 33:263–283
Heragu S (1997) Facilities design. PWS publishing company, Boston, p 303
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ghezavati, V., Saidi-Mehrabad, M. Designing integrated cellular manufacturing systems with scheduling considering stochastic processing time. Int J Adv Manuf Technol 48, 701–717 (2010). https://doi.org/10.1007/s00170-009-2322-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-009-2322-2