Abstract
Assembly sequence planning is one of the well-known combinatorial optimization problems in manufacturing. An assembly is often represented as an assembly relation graph or precedence graph. The traditional methods are used to generate a large number of feasible assembly sequences and then find the optimal sequence through evaluation. A lot of computation resources are needed. To reduce the complexity of assembly sequence planning, the assembly is converted into a weighted assembly precedence graph considering multiple assembly constraints, i.e., the qualitative and quantitative constraints. The vertices in the weighted precedence graph are the parts or components. The qualitative constraints including the topological and geometrical assembly constraints guarantee to derive the feasible assembly sequences. Some process constraints are also taken as the qualitative constraints. They are represented as the directed edges in the weighted assembly precedence graph. The other assembly constraints, such as the stable support, connector strength, changes of assembly directions, and tools and so forth, are quantified as indices to compute the cost of assembly relations with the fuzzy analytical hierarchy process. The costs are taken as the heuristic information to find the optimal or near-optimal assembly sequences. With the weighted assembly precedence graph, the search space of the optimal assembly sequence will be reduced. We design a minimum spanning tree-based algorithm to detect the optimal assembly sequence based on the weighted assembly precedence graph. The optimal assembly sequences are found in O(n 3) computation time, where n is the number of the discrete parts.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Boothroyd G, Dewhurst P, Knight A (2002) Product design for manufacture and assembly. Marcel Dekker, Inc, New York
Nof SY, Wilhelm WE, Warneke HI (1997) Industrial assembly. Chapman & Hall, London
Gottschlich S, Ramos C, Lyons D (1994) Assembly and task planning: a taxonomy. IEEE Robot Autom Mag 1(3):4–12
de Mello Homem LS, Lee S (1991) Computer-aided mechanical assembly planning. Kluwer Academic Publishers, London
Romeo MM, Lee HS, Luong KA (2006) A genetic algorithm for the optimisation of assembly sequences. Comput Ind Eng 50(4):503–527
Bourjault A, Lhote A (1986) Modeling an assembly process. IEEE Int Conf Autom Manuf Ind 20(2):183–198
Homem De Mello L, Sanderson A (1990) And/or graph representation of assembly plans. IEEE Trans Robot Autom 6(2):188–199
Dini G, Santochi M (1992) Automated sequencing and subassembly detection in assembly planning. Ann CIRP 41(1):1–4
Dutta D, Woo TC (1995) Algorithm for multiple disassembly and parallel assemblies. ASME J Eng Ind 117(2):102–109
Lambert AJD, Gupta SM (2005) Disassembly modeling for assembly, maintenance, reuse, and recycling. CRC Press, Florida
Makris S, Pintzos G, Rentzos L, Chryssolouris G (2013) Assembly support using AR technology based on automatic sequence generation. CIRP Ann Manuf Technol 62(1):9–12
Wang Y, Liu JH (2010) Chaotic particle swarm optimization for assembly sequence planning. Robot Comput Integr Manuf 26(2):212–222
Zhu XW, Hu SJ, Koren Y, Huang NJ (2012) A complexity model for sequence planning in mixed-model assembly lines. J Manuf Syst 31(2):121–130
Su Q (2009) A hierarchical approach on assembly sequence planning and optimal sequences analyzing. Robot Comput Integr Manuf 25(1):224–234
Hu SJ, Ko J, Weyand L, ElMaraghy HA et al (2011) Assembly system design and operations for product variety[J]. CIRP Ann Manuf Technol 60(2):715–733
Zha XF (2000) An object-oriented knowledge based Petri net approach to intelligent integration of design and assembly planning. Artif Intell Eng 14(1):83–112
De Fazio TL, Whitney DE (1987) Simplified generation of all mechanical assembly sequences. IEEE J Robot Autom 3(6):640–658
Baldwin DF, Abell TE, De Fazio TL, Whitney DE (1991) An integrated computer aid for generating and evaluating assembly sequences for mechanical products. IEEE Trans Robot Autom 7(1):78–94
Zhou W, Zheng JR, Wang JF (2011) Nested partitions method for assembly sequences merging. Expert Syst Appl 38(8):9918–9923
Homem de Mello LS, Sanderson A (1991) A correct and complete algorithm for the generation of mechanical assembly sequences. IEEE Trans Robot Autom 7(2):228–240
Su Q (2007) Computer aided geometric feasible assembly sequence planning and optimizing. Int J Adv Manuf Technol 33(1/2):48–57
Hsu YY, Tai PH, Wang MW, Chen WC (2011) A knowledge-based engineering system for assembly sequence planning. Int J Adv Manuf Technol 55(5–8):763–782
Lambert AJD (2005) Generating disassembly sequences using exact and heuristic methods applied to disassembly precedence graphs. The 6th IEEE International Symposium on Assembly and Task Planning: From Nano to Macro Assembly and Manufacturing, NY, pp 47–52
Qu SP, Jiang ZH, Tao NR (2013) An integrated method for block assembly sequence planning in shipbuilding. Int J Adv Manuf Technol 69(5–8):1123–1135
Gao L, Qian WR, Li XY, Wang JF (2009) Application of memetic algorithm in assembly sequence planning. Int J Adv Manuf Technol 49(9–12):1175–1184
Li MY, Wu B, Hu YM, Jin C, Shi TL (2013) A hybrid assembly sequence planning approach based on discrete particle swarm optimization and evolutionary direction operation. Int J Adv Manuf Technol 68(1–4):617–630
Niu XN, Ding H, Xiong Y (2003) A hierarchical approach to generating precedence graph for assembly planning. Int J Mach Tools Manuf 43(14):1473–1486
Yu JP, Wang CG (2013) A max–min ant colony system for assembly sequence planning. Int J Adv Manuf Technol 67(9–12):2819–2835
Li SQ, Liu Y, Wang JF, Zeng HM (2014) An intelligent interactive approach for assembly process planning based on hierarchical classif ication of parts. Int J Adv Manuf Technol 70(9–12):1903–1914
Ramos C, Rocha J, Vale Z (1998) On the complexity of precedence graphs for assembly and task planning. Comput Ind 36(1–2):101–111
Zhou XM, Du PG (2008) A model-based approach to assembly sequence planning. Int J Adv Manuf Technol 39(9–10):983–994
Gu TL, Xu ZB, Yang ZF (2008) Symbolic OBDD representations for mechanical assembly sequences. Comput Aided Des 40(4):411–421
Banerjee A, Banerjee P (2000) A behavioral scene graph for rule enforcement in interactive virtual assembly sequence planning. Comput Ind 42(2–3):147–157
Bai YW, Chen ZN, Bin HZ, Hun J (2005) An effective integration approach toward assembly sequence planning and evaluation. Int J Adv Manuf Technol 27(1/2):96–105
Choi YK, Lee DM, Cho YB (2012) An approach to multi-criteria assembly sequence planning using genetic algorithms. Int J Adv Manuf Technol 42(1):180–188
Tseng YJ, Chen JY, Huang FY (2010) A multi-plant assembly sequence planning model with integrated assembly sequence planning and plant assignment using GA. Int J Adv Manuf Technol 48(1–4):333–345
Zhou W, Zheng JR, Yan JJ, Wang JF (2011) A novel hybrid algorithm for assembly sequence planning combining bacterial chemotax is with genetic algorithm. Int J Adv Manuf Technol 52(5–8):715–724
Ibrahim I, Ibrahim Z, Ahmad H, Jusof M, Yusof Z, Nawawi SWI, Mubin M (2015) An assembly sequence planning approach with a rule-based multi-state gravitational search algorithm. Int J Adv Manuf Technol. doi:10.1007/s00170-015-6857-0
Fathi M, Ghobakhloo M (2014) A technical comment on “a review on assembly sequence planning and assembly line balancing optimisation using soft computing approaches”. Int J Adv Manuf Technol 71(9–12):2033–2042
Behdad S, Berg L, Vance J, Thurston D (2014) Immersive computing technology to investigate tradeoffs under uncertainty in disassembly sequence planning. J Mech Des 136(7):1–9
Scholl A, Becker C (2006) State-of-the-art exact and heuristic solution procedures for simple assembly line balancing. Eur J Oper Res 168(3):666–693
Wang Y (2014) The hybrid genetic algorithm with two local optimization strategies for traveling salesman problem. Comput Ind Eng 70(4):124–133
West DB (2006) Introduction to graph theory. China Machine Press, Beijing
Morato C, Kaipa KN, Gupta SK (2013) Improving assembly precedence constraint generation by utilizing motion planning and part interaction clusters. Comput Aided Des 45(11):1349–1364
van Holland W, Bronsvoort WF (2000) Assembly features in modeling and planning. Robot Comput Integr Manuf 16(4):277–294
Wang Y, Liu JH, Li LS (2009) Assembly sequences merging based on assembly unit partitioning. Int J Adv Manuf Technol 45(7–8):808–820
Michela B, Elisabetta B, Leila DF, George N (1996) Generating assembly and machining sequences from the face-to-face composition model. Comput Aided Des 28(2):101–112
Lee S, Yi C (1995) Assemblability evaluation based on tolerance propagation[C]. Proc IEEE Int Conf Robot Autom, Leuven, Belgium 21–27:1593–1598
Lee S (1994) Subassembly identification and evaluation for assembly planning. IEEE Trans Syst Man Cybernetics 24(3):493–503
Laarhoven P, Pedrycz W (1983) A fuzzy extension of Saaty’s priority theory. Fuzzy Sets Syst 11(3):229–241
Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New York
Wang JF, Liu JH, Zhong XF (2005) A novel ant colony algorithm for assembly sequence planning. Int J Mach Tools Manuf 25(11–12):1137–1143
Gao L, Qian WR, Li XY, Wang JF (2010) Application of memetic algorithm in assembly sequence planning. Int J Mach Tools Manuf 49(9–12):1175–1184
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wang, Y., Tian, D. A weighted assembly precedence graph for assembly sequence planning. Int J Adv Manuf Technol 83, 99–115 (2016). https://doi.org/10.1007/s00170-015-7565-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-015-7565-5