Part-selection and machine-loading problems in a flexible manufacturing system environment: a heuristic approach based on reallocation paradigm

2008 ◽  
Vol 32 (2) ◽  
pp. 142 ◽  
Author(s):  
M.K. Tiwari ◽  
J. Saha ◽  
S.K. Mukhopadhyay
Keyword(s):  
Flexible Manufacturing ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Heuristic Approach ◽  
System Environment ◽  
Machine Loading ◽  
Part Selection

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.

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