Vol. 7 No. 1 (2024)

Open Access

Original Research Articles

Article ID: 644

Optimizing processes and products: The role of DOE

by Amir Ahmad Dar, Princy Yadav, Aswani N, Tashi Wangmo A

Insight - Statistics, Vol.7, No.1, 2024; 146 Views, 95 PDF Downloads

The Design of Experiment (DOE) methodology is a fundamental tool for systematic inquiry and optimization in both scientific and industrial applications. The DOE’s statistical framework is designed to enhance the efficiency and reliability of experimental investigations by systematically planning, conducting, and evaluating controlled experiments. To support well-informed decision-making, process optimization, and quality improvement, the main goal of DOE is to discover and quantify the effects of various variables on a response variable. Various methods such as full factorial, fractional factorial, Taguchi, and response surface methodologies provide powerful tools for optimising processes and enhancing quality. The study covers the application of DOE in several industries, including engineering, manufacturing, agriculture, and medicine. Defect minimization, process optimization, and quality improvement are all aided by DOE in manufacturing. By determining the best dosages and formulations, it helps in drug development in the pharmaceutical industry. In the field of agriculture, DOE facilitates the identification of optimal growing conditions and techniques. It helps in the engineering and assessing new systems and products. To achieve consistency and accuracy in data collection, the experiment must be carried out with strict adherence to the experimental strategy. Analysing data involves using statistical tools to evaluate the findings and make conclusions, such as ANOVA, regression analysis, and graphical approaches. By pointing out important variables and their interactions, these studies aid in process optimization and product quality enhancement.

Open Access

Original Research Articles

Article ID: 693

Scheme selection with probabilistic multiple objectives optimization

by Maosheng Zheng, Jie Yu

Insight - Statistics, Vol.7, No.1, 2024; 24 Views, 6 PDF Downloads

This article presents the scheme/alternative selections with probabilistic multiple objectives optimization (PMOO). In the PMOO assessment, all response objectives (attributes) are divided into beneficial or unbeneficial types according to their function and preference to equivalently contribute their partial preferable probabilities simultaneously, the total preferable probability of an alternative is the multiplication of its all partial preferable probabilities, which determines the optimal evaluation uniquely and comparatively. The application examples contain a personnel selection and a production quantity optimal control. The former is for an engineer position in a software development department from five alternative candidates withstanding seven optimal criteria (response objectives) comparatively, and the latter aims to get an optimal production quantity with a higher profit rate and lower final cost. In the personnel selection, the seven optimal response objectives (criteria) include relevant education, work experience in the field, relevant certificates, level of presentation and communication, ability of personnel management, capabilities of planning and organization, and expressiveness of foreign language. All these seven response objectives are attributed to the beneficial type of attribute to join the assessment. As to the production quantity control, the profit rate belongs to the beneficial type of objective, while the final cost is attributed to the unbeneficial type of attribute. The evaluated results reveal that the optimal alternate for the personnel selection is candidate No. 4, and the optimum production quantity is at x* = 54 items. The achievement of the present article indicates the validity of the corresponding approach and algorithm with rationality. The novelty of this work is to reflect the simultaneity of the response objectives (criteria) in the optimal system by using probabilistic multiple objectives optimization, and all response objectives either beneficial type or unbeneficial type are evaluated separately in an equivalent manner.