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The purpose of this paper is to improve the forecasting efficiency of a grey model.

The exponentially weighted moving average (EWMA) algorithm is proposed to modify background values for a new grey model optimization.

The experimental results reveal that the proposed models (EGM, REGM) outperform traditional grey models.

A genetic algorithm (GA) optimizer is used to select the optimal weights for the background values of the EGM(1,1) and REGM(1,1) forecast models. The results of the current study are very encouraging, as the empirical results show that the REGM(1,1) and EGM(1,1) models reduce the MAPE rates over the traditional GM(1,1) and RGM(1,1) models.

Much research has recently been conducted into the use of models for the economic design of multiple control charts (EDCC). Control chart models generally assume that most process variables are constant and only a limited number of the major variables are varied to reach a local optimum. In the economic design of multiple control charts (EDMCC), multiple control charts are used to analyse many manufacturing process variables simultaneously, in order to produce an optimal design for process control. However, the large number of variables often makes it difficult to solve this optimisation problem manually. This research explores the proposition that EDMCC can be optimised by using a novel genetic algorithm which dynamically adjusts the genetic algorithm’s (GA) operator and parameter settings during operation to ensure optimum effectiveness. This method involves refining the chromosome structure and using orthogonal arrays with fuzzy reasoning to reduce the search space.

This study seeks to employ the capability‐based view to investigate the direct effect of an organization's development of technological and design capabilities on technology commercialization. It aims to use two indicators to test the claim of ambidexterity, i.e. that synchronizing the development of technological capabilities and design capabilities can enhance the performance of technology commercialization.

The research subjects consisted of R&D teams in Taiwan developing new high‐tech information and communications products; a total of 109 valid questionnaires were recovered. A hierarchical multiple regression model was used to perform hypothesis testing.

The findings of this study indicate that both technological and design capabilities have a positive effect on technology commercialization results, and the contribution of design capabilities is greater than that of technological capabilities. The interaction of technological capabilities and design capabilities has a positive influence on the results of technology commercialization. A relative imbalance between technological and design capabilities has a negative effect on technology commercialization.

This study specifies that there are tensions between technological and design capabilities. However, an ambidextrous strategy involving the concurrent development of technological and design capabilities is suggested. Also the ambidexterity hypothesis is verified. This study consequently suggests that enterprises should simultaneously develop their technological and design capabilities, and seek to balance the allocation of management attention and resources between these two types of capabilities, if they wish to obtain optimal technology commercialization results.