Assembly Sequence Planning Based on Max-min Ant Colony System

2012 ◽  
Vol 48 (23) ◽  
pp. 152 ◽  
Author(s):  
Jiapeng YU
Keyword(s):  
Assembly Sequence Planning ◽  
Ant Colony ◽  
Ant Colony System ◽  
Assembly Sequence ◽  
Sequence Planning

Assembly Sequence Planning Based on Max-Min Ant Colony System

2013 ◽  
Vol 284-287 ◽  
pp. 2220-2227 ◽  
Author(s):  
Jia Peng Yu ◽  
Cheng En Wang
Keyword(s):  
Performance Appraisal ◽  
Assembly Sequence Planning ◽  
Ant Colony ◽  
Planning System ◽  
Assembly Planning ◽  
Group Method ◽  
Ant Colony System ◽  
Assembly Sequence ◽  
Ant System ◽  
Sequence Planning

An assembly sequence planning (ASP) method that combined the advantages of ant colony system (ACS) and Max-Min ant system (MMAS) is proposed. To identify the best sequence easily, five optimization criterions are automatically quantified, including reorientation, parallelism, continuity, stability and auxiliary stroke, and then integrated into the multi-objective heuristic and fitness functions of ant colony optimization (ACO). To improve the search capability for the global-best sequence, several measures are presented from aspects of determining number of ant, max-min pheromone limits, performance appraisal for initial components allocation and the group method for same components. The ASP algorithm based on Max-Min ant colony system (MMACS) is proposed. An assembly planning system AutoAssem is developed based on Siemens NX platform, and the actual effectiveness of each optimization measure is testified through case study of a valve.

An Ant Colony Algorithm for Integrating Assembly Sequence Planning and Assembly Line Balancing

2013 ◽  
Vol 397-400 ◽  
pp. 2570-2573 ◽  
Author(s):  
Zhuo Yang ◽  
Cong Lu ◽  
Hong Wang Zhao
Keyword(s):  
Ant Colony Algorithm ◽  
Assembly Line ◽  
Assembly Line Balancing ◽  
Assembly Sequence Planning ◽  
Ant Colony ◽  
Line Balancing ◽  
Assembly Planning ◽  
Planning Problem ◽  
Assembly Sequence ◽  
Sequence Planning

Assembly sequence planning (ASP) and assembly line balancing (ALB) problems are two essential problems in the assembly optimization. This paper proposes an ant colony algorithm for integrating assembly sequence planning and assembly line balancing, to deal with the two problems on parallel, and resolve the possible conflict between two optimization goals. The assembly sequence planning problem and the assembly line balancing problem are discussed, the process of the proposed ant colony algorithm is investigated. The results can provide a set of solutions for decision department in assembly planning.

Assembly Sequence Planning Including Tool Check Based on Improved Ant-Colony Algorithm

2013 ◽  
Vol 670 ◽  
pp. 3-9 ◽  
Author(s):  
R. Li ◽  
H. Chen ◽  
L. Xie
Keyword(s):  
Ant Colony Algorithm ◽  
Influence Factors ◽  
Assembly Sequence Planning ◽  
Ant Colony ◽  
Assembly Sequence ◽  
Sequence Planning ◽  
Precedence Relations ◽  
Assembly Sequences ◽  
Electromechanical Products ◽  
Improved Ant Colony Algorithm

According to the characteristics and demands of assembly sequence planning (ASP) of complicated electromechanical products, the improved Ant-Colony algorithm is presented to solve the problem. In order to plan assembly sequence, Precedence Graph, which is created based on assembly model, is created to record precedence relations between components. Several mechanisms are designed to make the precedence relations correct and compendious. To examine the feasibility of the sequences for tool’s operation, a quick checking method is proposed based on Potential Interference Information, which is created by simulating the tool’s movement and checking the interference information in entire assembly. Then, the Ant Colony Algorithm is improved to generate optimal assembly sequences with the guidance of precedence relations. Two important influence factors have been taken into account in the method. (1) A Penalty-Evaporation mechanism is proposed for the algorithm to punish unfeasible solutions. (2) Via analyzing the effects of the parameters in the state transfer function, the better solution is obtained while change the fixed parameters to transforming with time. Finally, an example illustrates the effectiveness of the strategy.

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