Machine Loading Optimization in Flexible Manufacturing System Using a Hybrid of Bio-inspired and Musical-Composition Approach

2011 ◽  
Author(s):  
Umi Kalsom Yusof ◽  
Rahmat Budiarto ◽  
Ibrahim Venkat ◽  
Safaai Deris
Keyword(s):  
Flexible Manufacturing ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Musical Composition ◽  
Machine Loading

Integrated Decision Model for Interrelated Sub-Problems of Part Design or Selection, Machine Loading and Machining Optimization

2009 ◽  
Author(s):  
M. I. Mgwatu ◽  
E. Z. Opiyo ◽  
M. A. M. Victor
Keyword(s):  
Flexible Manufacturing ◽  
Decision Model ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Shop Floor ◽  
Machine Loading ◽  
To Come ◽  
Computational Analyses ◽  
Machining Optimization ◽  
Full Factorial

In the work presented in this paper, we made an attempt to integrate the decisions for interrelated sub-problems of part design or selection, machine loading and machining optimization in a random flexible manufacturing system (FMS). The main purpose was to come up with an optimization model for achieving more generic and consistent decisions for the FMS and which can be practically implemented on the shop floor to help designers and other engineers in several ways, including, for instance, to optimize the designs of parts for specific FMS. In order to attain the generic decisions, an integer nonlinear programming (INLP) problem was formulated and solved to maximize the FMS throughput. Based on the results, the part design or selection, machine loading and machining optimization decisions can be simultaneously made. To get more insights of the results and also to check the validity of the model, a two-factor full factorial design was implemented for the sensitivity analysis, analysis of variance (ANOVA) and residual analysis. The computational analyses show that the tooling budget and available processing time were both statistically significant to throughput and confirmed that the model is valid with the data normally distributed.

scholarly journals Hybrid Genetic Algorithms for Part Type Selection and Machine Loading Problems with Alternative Production Plans in Flexible Manufacturing System

1970 ◽  
Vol 8 (1) ◽  
pp. 80-93 ◽  
Author(s):  
Wayan F. Mahmudy ◽  
Romeo M. Marian ◽  
Lee H. S. Luong
Keyword(s):  
Genetic Algorithms ◽  
Flexible Manufacturing ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Part Type ◽  
Type Selection ◽  
Machine Loading ◽  
Part Type Selection ◽  
Alternative Machines ◽  
Production Plans

This paper addresses two NP-hard and strongly related problems in production planning of flexible manufacturing system (FMS), part type selection problem and machine loading problem. Various flexibilities such as alternative machines, tools, and production plans are considered. Real coded genetic algorithms (RCGA) that uses an array of real numbers as chromosome representation is developed to handle these flexibilities. Hybridizing with variable neighbourhood search (VNS) is performed to improve the power of the RCGA exploring and exploiting the large search space of the problems. The effectiveness of this hybrid genetic algorithm (HGA) is tested using several test bed problems. The HGA improves the FMS effectiveness by considering two objectives, maximizing system throughput and minimizing system unbalance. The resulted objective values are compared to the optimum values produced by branch-and-bound method. The experiments show that the proposed RCGA could reach near optimum solution and the hybridization can improve the performance of the RCGA.

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