Optimization of Assembly Sequence Planning Using Soft Computing Approaches: A Review

Abdullah, Muhammad Amirul; Rashid, Mohd Fadzil Faisae Ab; Ghazalli, Zakri

doi:10.1007/s11831-018-9250-y

Cited by 42 publications

(17 citation statements)

References 59 publications

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“…And the positioning relations, support relations, assembly tool, installation direction, etc. are considered by the multi-objective heuristic function (Abdullah et al, 2019). 5.…”

Section: Assembly Knowledge Representationmentioning

confidence: 99%

Automatically generating assembly sequences with an ontology-based approach

Zhong

Jiang

Qin

et al. 2019

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Purpose The purpose of this paper is to present and develop an ontology-based approach for automatic generation of assembly sequences. Design/methodology/approach In this approach, an assembly sequence planning ontology is constructed to represent the structure and interrelationship of product geometry information and assembly process information. In the constructed ontology, certain reasoning rules are defined to describe the knowledge and experience. Based on the ontology with reasoning rules, the algorithm for automatically generating assembly sequences is designed and implemented. Findings The effectiveness of this approach is verified via applying it to generate the assembly sequences of a gear reducer. Originality/value The main contribution of the paper is presenting and developing an ontology-based approach for automatically generating assembly sequences. This approach can provide a feasible solution for the issue that mathematics-based assembly sequence generation approaches have great difficulty in explicitly representing assembly experience and knowledge.

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“…And the positioning relations, support relations, assembly tool, installation direction, etc. are considered by the multi-objective heuristic function (Abdullah et al, 2019). 5.…”

Section: Assembly Knowledge Representationmentioning

confidence: 99%

Automatically generating assembly sequences with an ontology-based approach

Zhong

Jiang

Qin

et al. 2019

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“…Assembly sequence planning (ASP) plays a major role in the assembly process, because it closely related to the feasibility, cost and time of the assembly process. It is very difficult to obtain an optimal sequence since assembly sequence planning is a NP-hard problem (Abdullah et al, 2019). Because the number of the parts in an assembly product increases, the number of feasible assembly sequences exponentially increases because of the combinatorial explosion of the ASP problem.…”

Section: Introductionmentioning

confidence: 99%

Optimizing assembly sequence planning using precedence graph-based assembly subsets prediction method

Zhang

Liu

Qiu

et al. 2019

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Purpose Assembly sequence planning (ASP) plays a vital role in assembly process because it directly influences the feasibility, cost and time of the assembly process. The purpose of this study is to solve ASP problem more efficiently than current algorithms. Design/methodology/approach A novel assembly subsets prediction method based on precedence graph is proposed to solve the ASP problem. The proposed method adopts the idea of local to whole and integrates a simplified firework algorithm. First, assembly subsets are generated as initial fireworks. Then, each firework explodes to several sparks with higher-level assembly subsets and new fireworks are selected for next generation according to selection strategy. Finally, iterating the algorithm until complete and feasible solutions are generated. Findings The proposed method performs better in comparison with state-of-the-art algorithms because of the balance of exploration (fireworks) and exploitation (sparks). The size of initial fireworks population determines the diversity of the solution, so assembly subsets prediction method based on precedence graph (ASPM-PG) can explore the solution space. The size of sparks controls the exploitation ability of ASPM-PG; with more sparks, the direction of a specific firework can be adequately exploited. Practical implications The proposed method is with simple structure and high efficiency. It is anticipated that using the proposed method can effectively improve the efficiency of ASP and reduce computing cost for industrial applications. Originality/value The proposed method finds the optimal sequence in the construction process of assembly sequence rather than adjusting order of a complete assembly sequence in traditional methods. Moreover, a simplified firework algorithm with new operators is introduced. Two basic size parameters are also analyzed to explain the proposed method.

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“…The effectiveness of an assembly plan is dependent on how the assembly problem is modeled. There are three ASP problem representation categories: connector-based, task-based and part-based (Abdullah et al, 2018). These categories are defined according to the smallest element used to represent the assembly problem.…”

Section: Introductionmentioning

confidence: 99%

“…Besides problem representation, researchers also consider different objectives to optimize ASP. According to a survey, the most frequently used optimization objectives are to minimize direction change and tool change (Abdullah et al, 2018). However, the assembly sequence with high direction changes makes the assembly process inefficient.…”

Section: Introductionmentioning

confidence: 99%

Energy efficient modeling and optimization for assembly sequence planning using moth flame optimization

Abdullah

Rashid

Ponnambalam

et al. 2019

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Purpose Environmental problems in manufacturing industries are a global issue owing to severe lack fossil resources. In assembly sequence planning (ASP), the research effort mainly aims to improve profit and human-related factors, but it still lacks in the consideration of the environmental issue. This paper aims to present an energy-efficient model for the ASP problem. Design/methodology/approach The proposed model considered energy utilization during the assembly process, particularly idle energy utilization. The problem was then optimized using moth flame optimization (MFO) and compared with well-established algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). A computational test was conducted using five assembly problems ranging from 12 to 40 components. Findings The results of the computational experiments indicated that the proposed model was capable of generating an energy-efficient assembly sequence. At the same time, the results also showed that MFO consistently performed better in terms of the best and mean fitness, with acceptable computational time. Originality/value This paper proposed a new energy-efficient ASP model that can be a guideline to design assembly station. Furthermore, this is the first attempt to implement MFO for the ASP problem.

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Optimization of Assembly Sequence Planning Using Soft Computing Approaches: A Review

Cited by 42 publications

References 59 publications

Automatically generating assembly sequences with an ontology-based approach

Automatically generating assembly sequences with an ontology-based approach

Optimizing assembly sequence planning using precedence graph-based assembly subsets prediction method

Energy efficient modeling and optimization for assembly sequence planning using moth flame optimization

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