Wang JF, Liu JH, Zhong YF (2005) A novel

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Wang JF, Liu JH, ZhongYF (2005) A novel ant colony algorithm for assembly sequence planning.

Int. J.Adv. Manuf. Technol., 25(11– 12), 1137–1143.Lu C, Huang HZ, FuhJYH, Wong YS (2008) A multi-objective disassembly planning approach with antcolony optimization algorithm. Proc.

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Inst. Mech. Eng. B. J. Eng. Manuf.,222(11), 1465–1474.

Tiwari MK, Prakash AK, MilehamAR (2005) Determination of an optimal assembly sequence using the psychoclonalalgorithm. Proc. Inst. Mech. Eng. B. J. Eng.

Manuf., 219(1), 137–149. Lu C, Liu YC (2012) Adisassembly sequence planning approach with an advanced immune algorithm. Proc.

IME. C. J. Mech. Eng. Sci.

, 226(11), 2739–2749. Cao PB, Xiao RB (2007) Assemblyplanning using a novel immune approach. Int. J. Adv.

Manuf. Technol., 31(7–8), 770–782.Wang Y, Liu JH (2010)Chaotic particle swarm optimization for assembly sequence planning. RobotComput. Integr. Manuf.

, 26(2), 212–222. Lv H, Lu C (2010) An assemblysequence planning approach with a discrete particle swarm optimizationalgorithm. Int. J. Adv. Manuf. Technol., 50, 761–770.

Lu C, Wong YS, Fuh JYH(2006) An enhanced assembly planning approach using a multi-objective genetic algorithm.Proc. Inst. Mech. Eng. B J Eng Manuf, 220(2), 255–272. Smith, G.

C. and Smith,S. S. F. (2002) An enhanced genetic algorithm for automated assembly planning.Robotics Computer Integ.

Mfg, 18, 355–364. Guan, Q., Liu, J.

H.,and Zhong, Y. F.

(2002) A concurrent hierarchical evolution approach toassembly process planning. Int. J. Prod.

Res., 40(14), 3357–3374. Lit, P.

D., Latinne,P., Rekiek, B., and Delchambre, A. (2001) Assembly planning with an orderinggenetic algorithm. Int.

J. Prod. Res., 39(16), 3623–3640. Chen, S.

F. and Liu, Y.J. (2001) An adaptive genetic assembly sequence planner. Int.

J. Computer  Integ. Mfg, 14(5), 489–500. Lazzerini, B. andMarcelloni, F.

 (2000) A geneticalgorithm for generating optimal assembly plans. Artif. Intell. Engng, 14,319–329.

Hong, D. S. and Cho, H.S. (1999) A genetic-algorithm based approach to the generation of roboticassembly sequence.

Control Engng Practice, 7, 151–159. Dini, G., Failli, F.,Lazzerini, B., and Marcelloni, F. (1999) Generation of optimized assemblysequence using genetic algorithms.

Annals CIRP, 48(1), 17–20. ReferencesThe strong urge ofmanufacturing companies to become more flexible in their products and theimportance of assembly sequence planning place a high importance on characterizingproduct flexibility in an assembly system. With little knowledge on this researchaspect, it is expected that it will contribute immensely to both theory andpractice by identifying the need for assembly sequence planning for flexibleproduct. This research will focus on assembly systems with semi-automatic assemblylines.Through assemblysequence planning, a feasible and optimal assembly sequence can be obtainedthrough which the parts can be assembled into the product successfully andefficiently with less assembly time or assembly cost.

It has been seen thatthere are several works on assembly sequence planning with some limitations.Hence, this research study is proposing to utilize the generic algorithm basedapproach for assembly sequence planning for flexible product in which changestime of the assembly tools, assembly directions and assembly types will be usedin the fitness function to evaluate the assembly cost. Moreover, the influenceof tolerance and clearance on the product will be considered and non-dominatedsolutions will be found.

More constraints will be considered to improve thestability in the assembly process which will help widen the capacity ofassembly sequence planning. Case studies will be given so as to verify theproposed assembly sequence planning approach for flexible products. For the research works onassembly sequence planning, Lu et al. (2006) and Guan et al. (2002) proposedthe assembly planning approaches with genetic algorithm (GA), where theassembly sequences are regarded as chromosomes, and the solutions are evolvedthrough crossover and mutation operation.

Lv and Lu (2010) and Wang and Liu(2010) proposed the particle swarm optimization approach to assembly sequenceplanning, and this approach is easier to implement with the fewer computationprocedures and fewer parameters. Cao and Xiao (2007) proposed an immuneoptimization approach to generate the optimal or near-optimal assembly sequenceby the immune operations, such as immune selection, inoculation, and immunemetabolism. Lu and Liu (2012) proposed a disassembly sequence planning approachwith an advanced immune algorithm, by which the optimal or near-optimal assemblysequence can be derived by converting the generated disassembly sequences. Basedon the artificial immune system, Tiwari et al. (2005) proposed a psychoclonalalgorithm by applying need hierarchy theory and the theory of clonal selectionto address the assembly sequence planning problem with good evolution performance.

Besides the above approaches, Lu et al. (2008) and Wang et al. (2005) proposedthe ant colony optimization approach to disassembly planning or assemblyplanning. The disassembly sequence or assembly sequence can be built step by stepwith the mechanism of the ant colony optimization, and the optimal ornear-optimal sequences can be easily found.

The work of Dini et al.(1999) proposed a method using genetic algorithms to generate and evaluate theassembly sequence, and adopted a fitness function considering simultaneouslythe geometric constraints and some assembly process, including the minimizationof gripper changes and object orientations, and the possibility of groupingsimilar assembly operations. Hong and Cho (1999) proposed a GA-based approachto generate the assembly sequence for robotic assembly, and the fitnessfunction is constructed based on the assembly costs that are reflected by thedegree of motion instability, and assembly direction changes are assigned withdifferent weights.

Lazzerini and Marcelloni (2000) used GA to generate andassess the assembly plans. The fitness function is constructed throughassigning different weights to three criteria: number of orientation changes,number of the gripper replacements, and grouping of similar assembly operations;and the different assembly planning results are derived through adjusting theweights in the fitness function in the experiments. Chen and Liu (2001)proposed an adaptive genetic algorithm (AGA) to find global optimal ornear-global-optimal assembly sequences.

In this algorithm, the genetic-operatorprobabilities are varied according to certain rules, and calculated by asimulation function. The calculated genetic operator probability settings arethen used to optimize dynamically the AGA search for an optimal assemblysequence. Lit et al.

(2001) proposed an original ordering GA to plan theassembly sequence. In this approach, a multi-objective cost function wasproposed, including five technical criteria: the number of reorientations, thestability of subsets, the parallelism between operations, and the latest orearliest components to be put in the plan. The algorithm is based on amulti-criterion decision-aided method whereby the decision maker assigns andadjusts the respective weights until good solutions can be found. Guan et al. (2002)proposed the concept of gene-group to consider the assembly process planning.One gene-group includes the component to be assembled, tool used to handle thecomponent, assembly direction, and type of the assembly operation to expressthe information of the assembly process. The change times of the assemblytools, assembly directions, and assembly types are used in the fitness functionto evaluate the assembly costs.

Smith and Smith (2002) proposed an enhancedgenetic algorithm based on the traditional genetic algorithm. This approachdoes not choose the next generation assembly sequence based on the fitness.Instead it periodically repopulates with high-fitness assembly plans to findoptimal or near-optimal assembly plans more reliably and quicker than thetraditional approaches. Some success has been achieved in the abovementionedGA-based assembly planning works. In these works, generally the geometricprecedence constraints are used during assembly planning to ensure the generationof a feasible assembly sequence. However, the influence of tolerance andclearance on product assemblability in different assembly sequences was notconsidered. In addition, to deal with a multi-objective optimization problem,these works generally used constant weights to build the fitness function bysome form of evolutionary trial. The search direction was fixed, and sometimesthe optimal or near-optimal solution, and other non-dominated solutions, couldnot be found.

According to the above limitations, more research effort isneeded in this area to enhance the function of assembly planning. The toleranceand clearance influence on product assemblability should be considered, andmore non-dominated solutions should be found.Assembly sequenceplanning is one of the best-known productions scheduling problems and proved tobe a strongly NP-hard problem.

It has a focus of determining the order ofprocessing jobs in the assembly line, to save the assembly cost or shorten theassembly time. Recently, some artificial intelligence-based technologies havebeen utilized in the assembly sequence planning. Knowledge-based approach andGeneric algorithm-based approach are the two areas in which artificialintelligence based approach can be divided. Although the mechanism ofknowledge-based approach can find feasible assembly sequence, however; whenassembly has many parts and components, and many alternative assembly sequencesexist, it is difficult to find optimal assembly sequence without an optimal searchalgorithm. Generic algorithm-based approach for assembly planning has receivedgreater research interest because both the optimal and near optimal solutioncan be found with high computing efficiency being achieved. Hence, the genericalgorithm is a promising approach to be utilized for this study.

Assembly planning is animportant step during product development. Flexible product development is theability to make changes to the product being developed or how it is beingdeveloped without being too disruptive. It is worthy to state that assembly planningmain objective is to find a feasible assembly sequence with the minimumassembly cost and assembly time.

  Toimprove profit margin, effective assembly planning is important so as tosignificantly reduce the product development cost.

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