Assembly Sequence Planning of Quayside Container Crane Based on Improved Immune Algorithm

Lu, Houjun; Zhen, Huiqiang; Huang, Youfang; Yan, Wenliang

doi:10.3923/jas.2013.4922.4928

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…5,6 These methods cannot work out the optimum solution for solely relying on experiences or perhaps have too high computational complexities for generating the global optimum solution. 7 Many recent studies tried to apply different intelligent algorithms for automated solving of the ASP problem. Cem 8 presented a neural network which has three layers with recurrent structure for analyzing assembly sequences of assembly systems.…”

Section: Introductionmentioning

confidence: 99%

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A new discrete double-population firefly algorithm for assembly sequence planning

Zhang

Yuan

Zhang

2016

Proceedings of the Institution of Mechanical Engineers, Part B:

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Assembly sequence planning is a critical step of assembly planning in product digital manufacturing. It is a combinational optimization problem with strong constraints. Many studies devoted to propose intelligent algorithms for efficiently finding a good assembly sequence to reduce the manufacturing time and cost. Considering the unfavorable effects of penalty function in the traditional algorithms, a new discrete firefly algorithm is proposed based on a double-population search mechanism for the assembly sequence planning problem. The mechanism can guarantee the population diversity and enhance the local and global search capabilities by using the parallel evolution of feasible and infeasible solutions. All parts composed of the assembly are assigned as the firefly positions, and the corresponding movement direction and distance of each firefly are defined using vector operations. Three common objectives, including assembly stability, assembly polymerization and change number of assembly direction, are taken into account in the fitness function. The proposed approach is successfully applied in a real-world assembly sequence planning case. The sizes of feasible and infeasible populations are adequately discussed and compared, of which the optimal size combination is used for initializing the firefly algorithm. The application results validate the feasibility and effectiveness of the discrete double-population firefly algorithm for solving assembly sequence planning problem.

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Section: Introductionmentioning

confidence: 99%

“…5,6 These methods cannot work out the optimum solution for solely relying on experiences or perhaps have too high computational complexities for generating the global optimum solution. 7…”

Section: Introductionmentioning

confidence: 99%

A new discrete double-population firefly algorithm for assembly sequence planning

Zhang

Yuan

Zhang

2016

Proceedings of the Institution of Mechanical Engineers, Part B:

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show abstract

“…Li et al (2013a) implemented discrete PSO for the train assembly. Besides those popular algorithms, researchers also implemented relatively new algorithms such as the imperialist algorithm (Zhou et al , 2013), the immune algorithm (Lu et al , 2013), shuffled frog leaping (Guo et al , 2015), breakout local search (Ghandi and Masehian, 2015), the firefly algorithm (Li et al , 2015a) and harmony search (Li et al , 2015b; Wang et al , 2013).…”

Section: Introductionmentioning

confidence: 99%

A hybrid Ant-Wolf Algorithm to optimize assembly sequence planning problem

Rashid

2017

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Purpose-This paper aims to optimize the assembly sequence planning (ASP) problem using a proposed hybrid algorithm based on Ant Colony Optimization (ACO) and Gray Wolf Optimizer (GWO). The proposed Hybrid Ant-Wolf Algorithm (HAWA) is designed to overcome premature convergence in ACO. Design/methodology/approach-The ASP problem is formulated by using task-based representation. The HAWA adopts a global pheromone-updating procedure using the leadership hierarchy concept from the GWO into the ACO to enhance the algorithm performance. In GWO, three leaders are assigned to guide the search direction, instead of a single leader in most of the metaheuristic algorithms. Three assembly case studies used to test the algorithm performance. Findings-The proposed HAWA performed better in comparison to the Genetic Algorithm, ACO and GWO because of the balance between exploration and exploitation. The best solution guides the search direction, while the neighboring solutions from leadership hierarchy concept avoid the algorithm trapped in a local optimum. Originality/value-The originality of this research is on the proposed HAWA. In addition to the standard pheromone-updating procedure, a global pheromone-updating procedure is introduced, which adopted leadership hierarchy concept from GWO.

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Optimizing container terminal operations: a systematic review of operations research applications

2023

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Assembly Sequence Planning of Quayside Container Crane Based on Improved Immune Algorithm

Cited by 3 publications

References 8 publications

A new discrete double-population firefly algorithm for assembly sequence planning

A new discrete double-population firefly algorithm for assembly sequence planning

A hybrid Ant-Wolf Algorithm to optimize assembly sequence planning problem

Optimizing container terminal operations: a systematic review of operations research applications

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