A multistage and multiple response optimization approach for serial manufacturing system

Bera, Sasadhar; Mukherjee, Indrajit

doi:10.1016/j.ejor.2015.07.018

Cited by 31 publications

(13 citation statements)

References 37 publications

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“…In many known desirability functions there exists a non-differentiable point that does not allow the use of the gradient-based optimization techniques. Some approaches were proposed to solve this breakpoint problem [14][15][16][17]. Castillo et al [16] proposed a modification of piecewise desirability function coupled with the generalized reduced gradient (GRG) method.…”

Section: The Desirability Optimization Methodology Combines Desirabilmentioning

confidence: 99%

A novel desirability function for multi-response optimization and its application in chemical engineering

Marinković

2020

CI&CEQ

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In many aspects of modern engineering (processes, products, and systems) there is a need to optimize multiple responses simultaneously, rather than optimizing one response at a time. The optimization of each individual response may generate as many different results as the responses are considered in the study. Then, it may be impossible to decide whether one solution is better than the other. On the other hand, some improvement in one response can significantly degrade at least one or more responses. There are a number of multiresponse optimization techniques available. Among the these multi-response optimization techniques, the desirability-based approaches take a prominent place because they are less sophisticated, easy to understand and implement, and more flexible with respect to other existing approaches, due to which they are very popular among researchers and practitioners. There are many different formulations of the desirability function. Unfortunately, most desirability functions known in the literature are piecewise, non-differentiable functions. In this paper, a novel desirability function is proposed, which is continuous and differentiable in its domain. This function is more suitable for applying some of the efficient gradient-based optimization methods. The efficiency and accuracy of the proposed method were analyzed on two chemical processes that were studied extensively in the literature.

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Section: The Desirability Optimization Methodology Combines Desirabilmentioning

confidence: 99%

A novel desirability function for multi-response optimization and its application in chemical engineering

Marinković

2020

CI&CEQ

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“…In multi-stage manufacturing processes [20,21], finding the values of controllable factors that make them the same as pre-determined target values is much more needed than maximizing or minimizing quality characteristics. In these processes, some (or all) of quality characteristics in the current stage process are considered as design parameters for the next stage process.…”

Section: Process Optimizationmentioning

confidence: 99%

A Confidence Interval-Based Process Optimization Method Using Second-Order Polynomial Regression Analysis

Kim

Lee

et al. 2020

Processes

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In the manufacturing processes, process optimization tasks, to optimize their product quality, can be performed through the following procedures. First, process models mimicking functional relationships between quality characteristics and controllable factors are constructed. Next, based on these models, objective functions formulating process optimization problems are defined. Finally, optimization algorithms are applied for finding solutions for these functions. It is important to note that different solutions can be found whenever these algorithms are independently executed if a unique solution does not exist; this may cause confusion for process operators and engineers. This paper proposes a confidence interval (CI)-based process optimization method using second-order polynomial regression analysis. This method evaluates the quality of the different solutions in terms of the lengths of their CIs; these CIs enclose the outputs of the regression models for these solutions. As the CIs become narrower, the uncertainty about the solutions decreases (i.e., they become statistically significant). In the proposed method, after sorting the different solutions in ascending order, according to the lengths, the first few solutions are selected and recommended for the users. To verify the performance, the method is applied to a process dataset, gathered from a ball mill, used to grind ceramic powders and mix these powders with solvents and some additives. Simulation results show that this method can provide good solutions from a statistical perspective; among the provided solutions, the users are able to flexibly choose and use proper solutions fulfilling key requirements for target processes.

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“…Mukherjee and Ray 27 applied a desirability function‐based MRSO approach to a two‐stage grinding manufacturing process where multiple responses exist. Bera and Mukherjee 28 also suggested the desirability function‐based MRSO approach to a multistage multiresponse optimization problem. Both Mukherjee and Ray 27 and Bera and Mukherjee 28 attempted to build empirical models for response variables and convert the empirical models to individual desirability functions.…”

Section: Literature Reviewmentioning

confidence: 99%

Multiresponse optimization of a multistage manufacturing process using a patient rule induction method

Lee

Yang

Kim

2020

Quality & Reliability Eng

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Most manufacturing industries produce products through a series of sequential stages, known as a multistage process. In a multistage process, each stage affects the stage that follows, and the process often has multiple response variables. In this paper, we suggest a new procedure for optimizing a multistage process with multiple response variables. Our method searches for an optimal setting of input variables directly from operational data according to a patient rule induction method (PRIM) to maximize a desirability function, to which multiple response variables are converted. The proposed method is explained by a step-by-step procedure using a steel manufacturing process as an example. The results of the steel manufacturing process optimization show that the proposed method finds the optimal settings of input variables and outperforms the other PRIM-based methods.

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A multistage and multiple response optimization approach for serial manufacturing system

Cited by 31 publications

References 37 publications

A novel desirability function for multi-response optimization and its application in chemical engineering

A novel desirability function for multi-response optimization and its application in chemical engineering

A Confidence Interval-Based Process Optimization Method Using Second-Order Polynomial Regression Analysis

Multiresponse optimization of a multistage manufacturing process using a patient rule induction method

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