An Enhanced Genetic Algorithm for Assembly Planning
M. Dev Anand1, S. Kumanan2, R. R. Girish3, T. Selvaraj4, P. Asokan5
1M. Dev Anand, Professor, Noorul Islam Centre for Higher Education, Thuckalay (Tamil Nadu), India.
2S. Kumanan, Professor, National Institute of Technology, Tiruchirappalli (Tamil Nadu), India.
3R. R. Girish, Assistant Professor, Rajalakshmi College of Engineering, (Tamil Nadu), India.
4T. Selvaraj, Professor, National Institute of Technology, Tiruchirappalli (Tamil Nadu), India.
5P. Asokan, Professor, National Institute of Technology, Tiruchirappalli (Tamil Nadu), India.
Manuscript received on 16 July 2019 | Revised Manuscript received on 01 August 2019 | Manuscript Published on 10 August 2019 | PP: 36-42 | Volume-8 Issue-2S3 July 2019 | Retrieval Number: B10070782S319/2019©BEIESP | DOI: 10.35940/ijrte.B1007.0782S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Assembly planning is very important for competitive manufacturing where assemble-to- order of products is in-practice. Assembly planning is a complex task and an optimal assembly plan is detrimental to meet customer demands. This work presents a genetic algorithm for assembly planning. This problem is more difficult than other assembling problems that have already been tackled with success using these approaches, such as the classic Traveling Salesperson Problem (TSP) or the Job Shop Scheduling Problem (JSSP). It not only involves the arranging of tasks, as in those problems, but also the selection of them from a set of alternative operations. Random search methods are being attempted for these types of combinatorial problems. Thus, many current research reports describe efforts to develop more efficient planning algorithms. Genetic algorithms show particular promise for assembly planning. As a result, several recent research reports present assembly planners based upon traditional genetic algorithms. Although prior genetic assembly planners find improved assembly plans with some success, they also tend to converge prematurely at local-optimal solutions. Thus, we present an assembly planner, based upon an enhanced genetic algorithm that demonstrates improved searching characteristics over an assembly planner based upon a traditional genetic algorithm. In particular, our planner finds optimal or near-optimal solutions more reliably and more quickly than an assembly planner that uses a traditional genetic algorithm.
Keywords: Genetic Algorithm, Assembly Planning, Liaisons Graph, Task Scheduling, Assembly Robots, Optimization Problem.
Scope of the Article: Algorithm Engineering