Optimal tolerance allotment using a genetic algorithm and truncated Monte Carlo simulation

Lee, Jinkoo; Johnson, Glen E.

doi:10.1016/0010-4485(93)90075-y

Cited by 83 publications

(31 citation statements)

References 11 publications

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“…There have been several e orts in this direction 15,19,56 . Also, heuristic techniques for optimization such as simulated annealing, genetic algorithms, Lagrangian relaxation, and Tabu search have been used by researchers 57 59 .…”

Section: Optimization Methodsmentioning

confidence: 99%

Design for tolerance of electro-mechanical assemblies: An integrated approach

Narahari

Rachuri

Lyons

et al. 1999

IEEE Trans. Robot. Automat.

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Section: Optimization Methodsmentioning

confidence: 99%

Design for tolerance of electro-mechanical assemblies: An integrated approach

Narahari

Rachuri

Lyons

et al. 1999

IEEE Trans. Robot. Automat.

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“…This solution is mainly based on mathematical functions (such as power, exponential or polynomial functions [4,5,14,18]) which only express the manufacturing cost considering the tolerance interval to produce (4). They occur mainly in the following form:…”

Section: Tolerance Allocationmentioning

confidence: 99%

Variation management by functional tolerance allocation and manufacturing process selection

Etienne

Dantan

Qureshi

et al. 2008

Int J Interact Des Manuf

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The imperfections of the manufacturing process lead to functional characteristics degradation, and therefore product quality. To ensure a certain level of product quality, the synthesis of tolerance (tolerance design) aims to determine the acceptable limits of the characteristics of parts, assemblies... The allocation or synthesis of functional tolerances is an important step in the design process which takes place generally during the detailed design and greatly impacts the design of the manufacturing process, manufacturing and product control. That is why it is important, when functional tolerances are quantified, to take into account their impacts on the manufacturing cost and product quality. These two concepts (manufacturing cost and product quality) are usually considered as conflicting goals. The proposed approach aims to allocate the functional tolerances that provide the best ratio between functional performances and manufacturing cost. It is based on the "Key Characteristics" approach, developed by Boeing coupled with an activities approach. This optimization is carried out by a genetic algorithm. The process selection is performed by a constraint satisfaction algorithm. Finally, the impacts of process choices are assessed with the Monte Carlo simulation which calculates the behavior and quality of the resulting product.

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“…The SA algorithm has the ability of escaping from local minima and has been applied widely (see Dougherty and Marryott 1991;Marryott 1996;Goldman and Mays 1999;Cunha and Sousa 2001). If the energy of generated new state is lower than that of the previous one, the change is accepted unconditionally and the system is updated; otherwise, the new state is accepted by Metropolis criterion with a probability function so as to ensure that the algorithm has the ability of escaping from local minimum energy state and reaching global minimum (Lee and Johnson 1993). The SA has been so far little applied to the optimal operation of reservoir systems.…”

Section: Introductionmentioning

confidence: 99%

An Improved Genetic Algorithm-Simulated Annealing Hybrid Algorithm for the Optimization of Multiple Reservoirs

Xia

2007

Water Resour Manage

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A hybrid evolutionary search algorithm is developed to optimize the classical single-criterion operation of multi-reservoir systems. The proposed improved genetic algorithm-simulated annealing (IGA-SA) which combines genetic algorithms (GAs) and the simulated annealing (SA) is a new global optimization algorithm. The algorithm is capable of overcoming the premature convergence of GAs and escaping from local optimal solutions. In addition, it is faster than a traditional unimproved GA-SA algorithm. A case study of optimization operation on generation electricity of a 3-reservoir system in series over 41-year (from May 1940 to April 1981) time periods in Wujiang River, one branch of Yangtze River in China, was performed. The objective is to maximize generation output from the system over each 12-month operating periods. Trade-off analyses on binary coding representation and real-value coding representation of GAs are performed. Sensitivity to some parameters of the GA, the SA and the IGA-SA is analyzed, respectively, and the appropriate values of parameters are suggested. The performance of the proposed algorithm is compared with that of the existing genetic algorithm, the simulated annealing and the dynamic programming (DP). Results demonstrate that the GA is better than the DP, the SA performs better than the GA and the IGA-SA is more efficient than SA. The IGA-SA produces higher quality solutions and costs less computation time compared with the traditional GA-SA. The results obtained from these applications have proved that the IGA-SA has the ability of addressing large and complex problems and is a new promising search algorithm for multi-reservoir optimization problems.

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Optimal tolerance allotment using a genetic algorithm and truncated Monte Carlo simulation

Cited by 83 publications

References 11 publications

Design for tolerance of electro-mechanical assemblies: An integrated approach

Design for tolerance of electro-mechanical assemblies: An integrated approach

Variation management by functional tolerance allocation and manufacturing process selection

An Improved Genetic Algorithm-Simulated Annealing Hybrid Algorithm for the Optimization of Multiple Reservoirs

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