Feature Based Costing (FBC) of Welded Assemblies: A Genetic Approach

2002 ◽  
Author(s):  
Gautam Subbarao ◽  
Michael L. Philpott ◽  
R. Sebastian Schrader ◽  
Dale E. Holmes
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Manufacturing Cost ◽  
Genetic Approach ◽  
Cad System ◽  
Computational Speed ◽  
Feature Based ◽  
Feature Information ◽  
The Cost

A genetic algorithm based welding planner capable of using parametric features to determine the manufacturing cost of welded assemblies has been developed. Parametric weld feature information obtained from a CAD system is translated to a hybrid traveling salesman problem. A genetic algorithm is used to search for a sequence of welds that minimizes the cost of the welded assembly. Emphasis is placed on developing a mechanistic, feature based DFM tool, with the aim of rapidly providing welded assembly cost feedback to the designer in a CAD environment, while maintaining a balance between accuracy and computational speed.

scholarly journals Evolutionary Divide and Conquer (I): A Novel Genetic Approach to the TSP

1993 ◽  
Vol 1 (4) ◽  
pp. 313-333 ◽  
Author(s):  
Christine L. Valenzuela ◽  
Antonia J. Jones
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Divide And Conquer ◽  
Search Problem ◽  
Genetic Approach ◽  
Solution Quality ◽  
Alternative Approach ◽  
The Traveling Salesman Problem

Experiments with genetic algorithms using permutation operators applied to the traveling salesman problem (TSP) tend to suggest that these algorithms fail in two respects when applied to very large problems: they scale rather poorly as the number of cities n increases, and the solution quality degrades rapidly. We propose an alternative approach for genetic algorithms applied to hard combinatoric search which we call Evolutionary Divide and Conquer (EDAC). This method has potential for any search problem in which knowledge of good solutions for subproblems can be exploited to improve the solution of the problem itself. The idea is to use the genetic algorithm to explore the space of problem subdivisions rather than the space of solutions themselves. We give some preliminary results of this method applied to the geometric TSP.

scholarly journals PENYELESAIAN MULTI TRAVELING SALESMAN PROBLEM DENGAN ALGORITMA GENETIKA

2017 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
NI KADEK MAYULIANA ◽  
EKA N. KENCANA ◽  
LUH PUTU IDA HARINI
Keyword(s):  
Genetic Algorithm ◽  
Traveling Salesman Problem ◽  
Heuristic Algorithm ◽  
Minimum Distance ◽  
Processing Time ◽  
Traveling Salesman ◽  
Running Time ◽  
Shortest Route ◽  
Time Required

Genetic algorithm is a part of heuristic algorithm which can be applied to solve various computational problems. This work is directed to study the performance of the genetic algorithm (GA) to solve Multi Traveling Salesmen Problem (multi-TSP). GA is simulated to determine the shortest route for 5 to 10 salesmen who travelled 10 to 30 cities. The performance of this algorithm is studied based on the minimum distance and the processing time required for 10 repetitions for each of cities-salesmen combination. The result showed that the minimum distance and the processing time of the GA increase consistently whenever the number of cities to visit increase. In addition, different number of sales who visited certain number of cities proved significantly affect the running time of GA, but did not prove significantly affect the minimum distance.

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