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In many practical situations, it is important to evaluate the relationships between the factors that compose an industrial process and their effects on one or more response variables that are of interest to an enterprise. The main contribution of this present study is to propose a new conceptual hybrid framework based on the DMAIC (Define, Measure, Analyze, Improve, and Control) methodological structure, to optimize complex experimental problems with multiple responses. This procedure combines Response Surface Methodology, with the Desirability (D), Modified Desirability (MD), Compromise Programming (CP) functions, with Generalized Reduced Gradient (GRG) and Evolutionary Algorithms (EA). We made real application to a glass lamination process case study to describe how to use the proposed framework. The procedure allowed several configurations to be tested involving the D, MD and CP functions, adopting the GRD and EV, to optimize the studied industrial process. The best configuration was defined by a practical confirmation experiment and validated by company engineers and experts. As examples of the advantages of adopting the proposed framework in the glass lamination problems, the best solutions resulted in a 49.86% increase in grinding wheel shelf life, corresponding to a 927kg reduction of steel-use per year, and a 41.7% reduction in dressing stone consumption, saving 17,200 stones per year. [ABSTRACT FROM AUTHOR]

This paper presents a new hybrid decision-making support method (New Hesitant Fuzzy AHP-QFD-PROMETHEE II Method), which jointly uses the Analytic Hierarchy Process (AHP), the Quality Function Deployment (QFD) and the Preference Ranking Method for Enrichment Evaluation (PROMETHEE II), as well as the Hesitant Fuzzy Linguistic Term Sets (HFLTS) to capture hesitation and aggregate divergent opinions from different experts. A real application of the new method to a packaging design selection problem for an automotive company is described, finding that AHP assisted in determining the importance of QFD's customer requirements (CRs) and PROMETHEE II was used to select the best packaging design. With this same problem, for the purpose of validating the proposed method, a comparative analysis was made with the use of the Hesitant Fuzzy AHP-QFD-TOPSIS method and also with the traditional AHP-QFD-PROMETHEE method, which makes it impossible to capture the hesitation of decision makers. The result showed similarity in the rankings of design alternatives found in the three methods application. The proposed method proved advantageous for solving problems that can generally be solved with the QFD House of Quality but have serious difficulties when decision makers have divergent opinions and hesitate in evaluating criteria and alternatives. [ABSTRACT FROM AUTHOR]

This work has been developed in a large steel industry in Brazil, which produces railway and industrial components, and whose aim was to reduce casting defects. Usually, in industrial processes, identifying the causes of defects and their control are relatively complex activities, due to the many variables involved. In this context, the production processes of seven products, involving 38 process variables (inputs and outputs), have been evaluated adopting a new and innovative procedure. Initially, using a Weighted Goal Programming ‐ Multiple Criteria Data Envelopment Analysis (WGP‐MCDEA) model, we identified the most relevant input and output variables, and the studied company validated the results. Next, using the multiple regression technique, empirical functions were constructed for two response variables chosen by the company – number of external cracks and number of internal cracks. Then, to model the real processes adequately, we introduced the occurrence of uncertainty on the coefficients of these functions, considering them as random variables, according to triangular probability functions. Finally, applying the optimizer Optquest, optimization via Monte Carlo simulation (OvMCS) was performed, and with the Ordinary Least Square technique, we obtained the best fit for the two response variables. Specialists from the company validated the proposed procedure. They found that the values of input and output variables obtained by OvMSC, as well as the values of the response variables, belonged to the database available in the ERP system of the company. These results showed that the procedure proposed herein provided feasible and useful solutions to improve the industrial processes under study. [ABSTRACT FROM AUTHOR]

The response surface methodology (RSM), which uses a quadratic empirical function as an approximation to the original function and allows the identification of relationships between independent variables xi and dependent variables ys associated with multiple responses, stands out. The main contribution of the present study is to propose an innovative procedure for the optimization of experimental problems with multiple responses, which considers the insertion of uncertainties in the coefficients of the obtained empirical functions in order to adequately represent real situations. This new procedure, which combines RSM with the finite element (FE) method and the Monte Carlo simulation optimization (OvMCS), was applied to a real stamping process of a Brazilian multinational automotive company. For RSM with multiple responses, were compared the results obtained using the agglutination methods: compromise programming, desirability function (DF), and the modified desirability function (MDF). The functions were optimized by applying the generalized reduced gradient (GRG) algorithm, which is a classic procedure widely adopted in this type of experimental problem, without the uncertainty in the coefficients of independent factors. The advantages offered by this innovative procedure are presented and discussed, as well as the statistical validation of its results. It can be highlighted, for example, that the proposed procedure reduces, and sometimes eliminates, the need for additional confirmation experiments, as well as a better adjustment of factor values and response variable values when comparing to the results of RSM with classic multiple responses. The new proposed procedure added relevant and useful information to the managers responsible for the studied stamping process. Moreover, the proposed procedure facilitates the improvement of the process, with lower associated costs. [ABSTRACT FROM AUTHOR]

• A Goal Programming model to optimize a waste paper recycling network was elaborated. • The model considers objectives related to sustainability, multiple products and periods. • Vehicle routing, different-capacity vehicles and multiple trips are highlights. • The possibility of storing material at the collection points is considered. • The formulation provides relevant solutions to assist decision-makers. Organization and planning of reverse logistics networks make sustainable processes more efficient. Thus, an important sector for connecting the collection and waste paper recycling echelons is the intermediate center. In this study, a Mixed Integer Linear Programming model, which is multi-objective, multi-product, multi-level and multi-period, was developed to optimize the waste paper logistics processes of intermediate centers. The formulation includes the following echelons: collection, considering vehicle routing with different capacities; inventory of non-baling materials; baling sorted waste and inventory of bales; selling bales; disposal of non-recyclable waste. The aims of the model include: maximizing the collected waste; minimizing distances; maximizing both the production and sale of bales, and minimizing costs. According to research carried out in the literature, the developed model is a new proposal and to implement it, the Weighted Goal Programming and Revised Multi-Choice Goal Programming approaches were used to deal with multiple objectives and incorporate uncertainty into the quantity of waste available for collection. To analyze the proposed model, computational tests were executed with instances based on real data from a Brazilian company in the sector. For all performed tests, General Algebraic Modeling System 23.6.5 modeling language and CPLEX 12.2.0.2 solver were used for modeling and optimization. The results show that this study presents formulation and technological approaches that represent real situations and provide competitive solutions to the problem. [ABSTRACT FROM AUTHOR]

In the last years, the use of metamodel-based simulation optimization techniques to solve industrial problems stood out as a promising research field, mainly due to the advance of machine learning techniques. The number of metamodeling studies has grown considerably in recent years, but many academics and practitioners still have doubts about which metamodels to choose for their projects. In this way, some studies have compared the effectiveness of metamodeling algorithms. However, they have just analyzed the performance of one or more metrics separately; i.e., they did not analyze the overall efficiency of these metamodels. Basing the metamodels' choice only on one or more metrics empirically might generate biases, causing distortions in decision-making. Therefore, we propose using the multi-criteria data envelopment analysis (MCDEA) model to systematically compare some of the main machine learning algorithms (support vector machine, artificial neural network, gradient-boosted trees, random forest, and Gaussian process). To evaluate the proposed approach, we developed discrete-event simulation models of three real case studies to obtain their input and output data. Moreover, we used machine learning algorithms to train and optimize the metamodels and, finally, new-MCDEA was adopted to compare the metamodels' efficiency considering the associated error, fitting, training and prediction times, and response, among other metrics. Different from traditional comparison approaches, where different algorithms could be chosen depending on the decision-maker bias, the proposed work allowed a good balance between all metrics, and for all cases, the metamodels based on gradient-boosted trees were considered the most efficient. [ABSTRACT FROM AUTHOR]