MULTI-OBJECTIVE ROBUST PRODUCTION PLANNING CONSIDERING WORKFORCE EFFICIENCY WITH A METAHEURISTIC SOLUTION APPROACH

Timely delivery of products to customers is one of the main factors of customer satisfaction and a key to the survival of a manufacturing system. Therefore, decreasing wasted time in manufacturing processes significantly affects production delivery time, which can be achieved through the maximization of workforce efficiency. This issue becomes more complicated when the parameters of the production system are under uncertainty. This paper presents a bi-objective scenario-based robust production planning model considering maximizing workforce efficiency and minimizing costs where the backorder, demand, and costs are uncertain. Also, backorder, raw materials purchasing, inventory control, and manufacturing time capacity are considered. A case study in a faucet manufacturing plant is considered to solve the model. Furthermore, the ε-constraint method, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), the Strength Pareto Evolutionary Algorithm 2 (SPEA2), and the Pareto Envelope-based Selection Algorithm II (PESA-II) are employed to solve the model. Also, the Taguchi method is used to tune the parameters of these algorithms. To compare these algorithms, five indicators are defined. The results show that the SPEA2 is the most time-consuming algorithm and the NSGA-II is the fastest, while their objective function values are nearly the same

<b>New hybrid multivariate analysis approach to optimize multiple response surfaces considering correlations in both inputs and outputs

Quality control in industrial and service systems requires the correct setting of input factors by which the outputs result at minimum cost with desirable characteristics. There are often more than one input and output in such systems. Response surface methodology in its multiple variable forms is one of the most applied methods to estimate and improve the quality characteristics of products with respect to control factors. When there is some degree of correlation among the variables, the existing method might lead into misleading improvement results. Current paper presents a new approach which takes the benefits of principal component analysis and multivariate regression to cope with the mentioned difficulties. Global criterion method of multiobjective optimization has been also used to reach a compromise solution which improves all response variables simultaneously. At the end, the proposed approach is described analytically by a numerical example

Marketing analytics : a practical guide to real marketing science / Mike Grigsby.

business bookfair2016xv, 232 pages :"Mike Grigsby provides business analysts and marketers with the marketing science understanding and techniques they need to solve real-world marketing challenges, such as pulling a targeted list, segmenting data, testing campaign effectiveness, and forecasting demand.Assuming no prior knowledge, Marketing Analytics introduces concepts relating to statistics, marketing strategy, and consumer behavior and then works through a series of problems by providing various data modeling options as solutions. By using this format of presenting a problem and multiple ways to solve it, this book both makes marketing science accessible to beginners and aids the more experienced practitioner in understanding the more complex aspects of data analytics to refine their skills and compete more effectively in the workplace"-- Provided by publisher."Marketing Analytics arms business analysts and marketers with the marketing science understanding and techniques they need to solve real-world marketing problems, from pulling a targeted list and segmenting data to testing campaign effectiveness and forecasting demand. Assuming no prior knowledge, this book outlines everything practitioners need to 'do' marketing science and demonstrate value to their organization. It introduces concepts relating to statistics, marketing strategy and consumer behaviour and then works through a series of marketing problems in a straightforward, jargon-free way. It demonstrates solutions for various data modelling scenarios and includes full workings and critical analyses to reinforce the key concepts. By starting with the marketing problem and then sharing a series of data modelling options on how to solve it, Marketing Analytics both makes marketing science accessible for beginners and aids the more seasoned practitioner in getting to grips with the trickier technical aspects of data analytics to refine their marketing skills and toolkit and compete more effectively in the marketplace. About the series: The Marketing Science series makes difficult topics accessible to marketing students and practitioners by grounding them in business reality. Each book is written by an expert in the field and includes case studies and illustrations so marketers can gain confidence in applying the tools and techniques and commission external research"-- Provided by publisher