Manufacturing optimization through intelligent techniques

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On the other hand, process planning optimization for reconfigurable manufacturing is not amenable to classical modeling approaches due to the presence of complex system dynamics.


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Therefore, this study explores how to model reconfigurable manufacturing activities in an optimization perspective and how to develop and select appropriate non-conventional optimization techniques for reconfigurable process planning. In this study, a new approach to modeling Manufacturing Process Planning Optimization MPPO was developed by extending the concept of manufacturing optimization through a decoupled optimization method. The uniqueness of this approach lies in embedding an integrated scheduling function into a partially integrated process planning function in order to exploit the strategic potentials of flexibility and reconfigurability in manufacturing systems.

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Alternative MPPO models were constructed and variances associated with their utilization analyzed. Five 5 Alternative Algorithm Design Techniques AADTs were developed and investigated for suitability in providing process planning solutions suitable for reconfigurable manufacturing.

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In particular, the relative performances of the novel variant of simulated annealing in comparison to: a i a simulated annealing search, and ii a genetic search in the Boltzmann Machine Architecture, and b i a modified genetic algorithm and ii a genetic algorithm with a customized threshold operator that implements an innovative extension of the diversity control mechanism to gene and genome levels; were pursued in this thesis.

Results show that all five 5 AADTs are capable of stable and asymptotic convergence to near optimal solutions in real time. Figure 2 shows the behavior of both techniques with different order sets. It can be observed that the improvement is significant in all the analyzed sets.

Advanced Engineering Optimization Through Intelligent Techniques : R. Venkata Rao :

However the improvement depends on the topology and distribution of flaws and also the customer order selection. As the customer orders are more heterogeneous, less cullet will be generated. Thus, our tool is able to select form a given set of customer orders which four will be managed together to minimize cullet. The tool developed in this TTP has reduced the cullet of the company and increased the competitiveness of the company in the market.

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Skip to main content. These problems can be summarized in: The customer order selection problem.

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This problem aims to select, from the batch of all customer orders, the more appropriate ones to be inserted into the system. The selected orders will be served and they will be the input of the glass cutting algorithm.


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    Abstract The main objective of the work reported in this thesis has been to study and develop methodologies that can improve the communication gap between design and manufacturing systems. The emphasis has been on searching for the possible means of modeling and optimizing processes in an integrated design and manufacturing system environment using the combined capabilities hybrids of computational intelligence tools particularly that of artificial neural networks and genetic algorithms. Within the last two decades, a trend of interest towards use of computers has been observed in almost all business activities.

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    Manufacturing Optimization through Intelligent Techniques (2006)

    This has forced the industrial business to undergo dynamic profound changes with automation through information and communication technology being on the forefront of business success. Business in manufacturing engineering is no exceptional to this trend. Several functions in the manufacturing field such as design, process planning and manufacturing have enjoyed the recent advances in information and communication technology.

    However, the earlier isolated automation in each function have created a significant hindrance to smooth flow of information particularly because there has been a very high system incompatibility among the computerized systems. One of the most difficult problems in modern manufacturing is the instability of production systems to mimic the basis human capabilities such as adjusting appropriately to the ever-changing environment. From past studies, it has been possible to witness that advances in theory and application methodology of artificial intelligence techniques can overcome many of the obstacles existing in manufacturing discipline.

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