Open AccessAn Enhanced Genetic Algorithm for Assembly Planning
Author(s) -
M. Dev Anand,
S. Kumanan,
R. Girish,
T. Selvaraj,
P. Asokan
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b1007.0782s319
Subject(s) - planner , genetic algorithm , computer science , plan (archaeology) , scheduling (production processes) , selection (genetic algorithm) , task (project management) , set (abstract data type) , mathematical optimization , algorithm , artificial intelligence , engineering , machine learning , mathematics , systems engineering , archaeology , history , programming language
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.