Chaotic particle swarm optimization for assembly sequence planning

Wang, Y.; Liu, J. H.

doi:10.1016/j.rcim.2009.05.003

Cited by 152 publications

(84 citation statements)

References 15 publications

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“…There have been a significant number of research works about optimisation of the assembly scheduling for traditional production systems. This class of optimisation problems has been solved by various approaches, such as heuristic (Andrés et al 2008;Kim et al 1996;Al-Anzi and Allahverdi 2007;Allahverdi and Al-Anzi 2009;Sung and Kim 2008;Koulamas and Kyparisis 2001), particle swarm optimisation (Dong et al 2012;Wang and Liu 2010;Hamta et al 2013;Allahverdi and Al-Anzi 2006), mixed integer programming (Ozturk et al 2010;Lin and Liao 2012;Terekhov et al 2012;Sawik 2004), genetic algorithm (Wong et al 2009;Marian et al 2003Marian et al , 2006Yolmeh and Kianfar 2012;Celano et al 1999;Dini et al 1999), Taguchi method (Chen et al 2010), dynamic programming (Jiang et al 1997;Zhang et al 2005;Yee and Ventura 1999), neural networks (Chen et al 2008;Hong and Cho 1995), multi-agent evolutionary algorithm (Zeng et al 2011), simulated annealing (Milner et al 1994), etc. In general, all of the works done so far deal with two main optimisation issues: assembly sequence and assembly resource location.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimisation of assembly scheduling in VCIM systems using genetic algorithm

2017

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Assembly plays an important role in any production system as it constitutes a significant portion of the lead time and cost of a product. Virtual computer-integrated manufacturing (VCIM) system is a modern production system being conceptually developed to extend the application of traditional computer-integrated manufacturing (CIM) system to global level. Assembly scheduling in VCIM systems is quite different from one in traditional production systems because of the difference in the working principles of the two systems. In this article, the assembly scheduling problem in VCIM systems is modeled and then an integrated approach based on genetic algorithm (GA) is proposed to search for a global optimised solution to the problem. Because of dynamic nature of the scheduling problem, a novel GA with unique chromosome representation and modified genetic operations is developed herein. Robustness of the proposed approach is verified by a numerical example.

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Section: Literature Reviewmentioning

confidence: 99%

Optimisation of assembly scheduling in VCIM systems using genetic algorithm

2017

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“…However, when using TTs for generating assembly sequences, only the precedence relations need to be clarified before creating assembly sequences with minimal rotation of parts arranged successively. Compared with the method using the precedence relations of the parts only for generating assembly sequences (Jun et al, 2005;Martinez et al, 2009;Wen et al, 2008Wen et al, , 2010Kai et al, 2008;Marian et al, 2006;Young et al, 2009;Wang et al, 2010;Qiang, 2009;Hongbo et al, 2009;Biswal et al, 2009;Zhou et al, 2008;Shanshan et al, 2008;Hwai et al, 2007;Ostrovsky et al, 2006;Liverani et al, 2006), the proposed method intentionally shows the assembly between two parts with a tournament style method, and the assembly sequences shown are visually accessible.…”

Section: Procedures Of Selectively Gen-erating Assembly Sequences Usinmentioning

confidence: 99%

“…There are many studies on assembly sequence planning (Jun et al, 2005;Martinez et al, 2009;Wen et al, 2008Wen et al, , 2010Kai et al, 2008;Marian et al, 2006;Young et al, 2009;Wang et al, 2010;Qiang, 2009;Hongbo et al, 2009;Biswal et al, 2009;Zhou et al, 2008;Shanshan et al, 2008;Hwai et al, 2007;Ostrovsky et al, 2006;Liverani et al, 2006). In this research area, it is common to generate assembly sequences using a BOM method, a genetic algorithm method or a graph theory-based method.…”

Section: Introductionmentioning

confidence: 99%

A Proposal for Generating Good Assembly Sequences by Tournament Tree

Tsuboi¹,

Matsumoto

Shinoda

et al. 2011

Industrial Engineering and Management Systems

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Abstract. In seeking further efficiency in production preparation, it is common to examine assembly sequences using digital manufacturing. The assembly sequences affect the product evaluation, so it is necessary to test several assembly sequences before actual production. However, because selection and testing of assembly sequences depends on the operator's personal experience and intuition, only a small number of assembly sequences are actually tested. Nevertheless, there is a systematic method for generating assembly sequences using a contact-related figure. However, the larger the number of parts, the larger the number of assembly sequences geometric becomes. The purpose of this study is to establish a systematic method of generating efficient assembly sequences regardless of the number of parts. To generate such assembly sequences selectively, a "Tournament Tree," which shows the structure of an assembly sequence, is formulated. Applying the method to assembly sequences of a water valve, good assembly sequences with the same structure as the Tournament Tree are identified. The structure of such a Tournament Tree tends to have fewer steps than the others. As a test, the structure is then applied for a drum cartridge with 38 parts. In all the assembly sequences generated from the contact-related figures, the best assembly sequence is generated by using the Tournament Tree.

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“…Microlevel concerns are typically respected as pieces of advice in form of special constraints, giving up this way the completeness and sometimes even the satisfiability of the planning problem. All this requires custom-tailored (re)solution methods [5][12] [13].…”

Section: Introductionmentioning

confidence: 99%

Decomposition approach to optimal feature-based assembly planning

2017

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The paper proposes a generic approach to assembly planning where individual tasks, with detailed technological content specified by features, must be combined into an optimal assembly plan subject to technological and geometric constraints. To cope with the complexity and variety of the constraints that refer to the overall assembly process, Benders decomposition is applied. The macro-level master problem looks for the optimal sequencing and resource assignment of the tasks, while sub-problem modules ensure plan feasibility on the micro-level from aspects of technology, fixturing, tooling, and collision. Constraints are also dynamically generated for the master problem. The approach is demonstrated in automotive assembly.

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Chaotic particle swarm optimization for assembly sequence planning

Cited by 152 publications

References 15 publications

Optimisation of assembly scheduling in VCIM systems using genetic algorithm

Optimisation of assembly scheduling in VCIM systems using genetic algorithm

A Proposal for Generating Good Assembly Sequences by Tournament Tree

Decomposition approach to optimal feature-based assembly planning

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