María Jesús Álvarez

Practical applications of design of experiments in the field of engineering: a bibliographical review

The design of experiments (DoE) methodology is a technique that has been applied for many years in industry to improve quality. In this study, a summary of 77 cases of practical DoE application in the field of engineering is presented. All of the cases were published in important scientific journals between 2001 and 2005. The type of design that is applied, the size of the experiment, the number of factors that influence the response variable, and the sector of application of the design are analyzed. In addition, the increasing use of these designs over time is demonstrated. Copyright

Is design of experiments really used? A survey of Basque industries

A new survey is presented concerning the knowledge and use of the design of experiments technique (DoE) within industry in the Basque Country, a region recognised throughout Europe for its quality management. The survey was carried out within manufacturing companies, yielding a response rate of 18%. Results show that 94% of companies undertake experimentation; most of them use one-factor-at-a-time strategies, and only 20% of those follow a pre-established statistical methodology. Survey results show that research and development and manufacturing make up 85% of DoE use. Furthermore, results show that lack of knowledge about general statistics is commonplace and only 31% of companies claim to be knowledgeable about DoE. In addition, although Taguchi methods are well known among companies, only 7% apply this method. Despite every effort by specialists in quality and statistics, DoE has yet to be applied as widely as it could and should be.

How is experimentation carried out by companies? A survey of three European regions

A survey was carried out to characterize experimentation in three different European regions: the Baden-Wurttemberg region, The Basque Country and the rest of Spain. Results of the survey show that even though experimentation is a frequent activity, almost 95% of companies conduct experiments; the strategies used to carry them out are primitive. The one-factor-at-a-time strategy is used by 75% of companies far more than the 23%, which apply design of experiments (DoE). Results show that this may be due to the current lack of knowledge of DoE in these regions, where only 33% are familiar with the technique. Finally, the rate of applications of DoE among Six Sigma users is 40%, twice that of non-users, which stands at 19%. Copyright

Why is not design of experiments widely used by engineers in Europe

An extensive literature review was carried out to detect why design of experiments (DoE) is not widely used among engineers in Europe. Once 16 main barriers were identified, a survey was carried out to obtain first-hand information about the significance of each. We obtained 101 responses from academics, consultants and practitioners interested in DoE. A statistical analysis of the survey is introduced, including: (a) a ranking of the barriers, (b) grouping of barriers using factorial analysis, (c) differences between characteristics of respondents. This exploratory analysis showed that the main barriers that hinder the widespread use of DoE are low managerial commitment and engineers’ general weakness in statistics. Once the barriers were classified, the most important resultant group was that related to business barriers.

Planning Experiments, the First Real Task in Reaching a Goal

ABSTRACT In industrial problems, it is necessary to give importance to the whole experimental process, especially the planning stage, where there are different factors that can strongly affect the results of a study. In this article, we present a real case of DoE application, focusing on the preliminary stages of experimentation. Some of the aspects we explain in detail are how we chose the best response to analyze, how we determined what factors to evaluate, and what our data sample collection criteria were. In addition, the article contains our analysis of the experiments and details of our results.

A new design criterion for robust parameter experiments

Varying noise factors in an experiment allows us to find robust conditions against such variation. The levels of the noise factors need to be selected carefully. Levels that are too wide may be infeasible or too costly, and if too narrow they may not provide a good model for prediction and control purposes. We propose a noise-factor separation (NFS) criterion for designs used in robust parameter design, in which a design is preferred to another if it provides the same expected mean square error for the noise part of the fitted model but for a smaller range of the noise factors in uncoded units. We evaluate several experimental designs that can be used to fit a response model that is quadratic in the controllable factors and also contains noise main effects and noise × control interactions. It is shown how the new criterion is related to variance dispersion graphs for the slope and may be used in conjunction with these graphs to assess a given experimental design. The new criterion is incorporated into an optimization formulation used to find new three-level designs that also includes traditional design criteria, such a D-efficiency. A Genetic algorithm was developed to solve such formulation. It is shown how the new designs are competitive in terms of design size, noise-factor separation, and variance dispersion for the mean and slope with respect to composite mixed resolution designs.

A Multiobjective Optimization Algorithm to Solve the Part Feeding Problem in Mixed-Model Assembly Lines

Different aspects of assembly line optimization have been extensively studied. Part feeding at assembly lines, however, is quite an undeveloped area of research. This study focuses on the optimization of part feeding at mixed-model assembly lines with respect to the Just-In-Time principle motivated by a real situation encountered at one of the major automobile assembly plants in Spain. The study presents a mixed integer linear programming model and a novel simulated annealing algorithm-based heuristic to pave the way for the minimization of the number of tours as well as inventory level. In order to evaluate the performance of the algorithm proposed and validate the mathematical model, a set of generated test problems and two real-life instances are solved. The solutions found by both the mathematical model and proposed algorithm are compared in terms of minimizing the number of tours and inventory levels, as well as a performance measure called workload variation. The results show that although the exact mathematical model had computational difficulty solving the problems, the proposed algorithm provides good solutions in a short computational time.

A new heuristic-based bi-objective simulated annealing method for U-shaped assembly line balancing

The U-shaped assembly line has received a considerable attention and has been widely used in industry in recent years due to the pressures of the just-in-time (JIT) manufacturing philosophy. However, balancing the U-shaped line is more difficult than balancing the traditional straight line. This study aims at balancing the U-shaped assembly line by introducing a novel heuristic which is based on the simulated annealing (SA) algorithm. The objectives to be optimised in this study are the number of workstations and balance efficiency. The performance of the proposed algorithm is examined by solving a set of standard test problems and a real case study. The results attained by the proposed algorithm were compared against the best known solution in the literature and it was found that the proposed algorithm is able to find good solutions in a reasonably short computational time. [Received 29 December 2012; Revised 28 March 2014; Revised 25 August 2014; Revised 22 May 2015; Accepted 26 May 2015]

A modified particle swarm optimisation algorithm to solve the part feeding problem at assembly lines

The Assembly Line Part Feeding Problem (ALPFP) is a complex combinatorial optimisation problem concerned with the delivery of the required parts to the assembly workstations in the right quantities at the right time. Solving the ALPFP includes simultaneously solving two sub-problems, namely tour scheduling and tow-train loading. In this article, we first define the problem and formulate it as a multi-objective mixed-integer linear programming model. Then, we carry out a complexity analysis, proving the ALPFP to be NP-complete. A modified particle swarm optimisation (MPSO) algorithm incorporating mutation as part of the position updating scheme is subsequently proposed. The MPSO is capable of finding very good solutions with small time requirements. Computational results are reported, demonstrating the efficiency and effectiveness of the proposed MPSO.