Yi‐Shou Wang, Hong‐Fei Teng and Yan‐Jun Shi
The purpose of this paper is to tackle a satellite module layout design problem (SMLDP). As a complex engineering layout and combinatorial optimization problem, SMLDP cannot be…
Abstract
Purpose
The purpose of this paper is to tackle a satellite module layout design problem (SMLDP). As a complex engineering layout and combinatorial optimization problem, SMLDP cannot be solved effectively by traditional exact methods. Although evolutionary algorithms (EAs) have shown some promise of tackling SMLDP in previous work, the solution quality and computational efficiency still pose a challenge. This paper aims to address these two issues.
Design/methodology/approach
Scatter search (SS) and a cooperative co‐evolutionary architecture are integrated to form a new approach called a cooperative co‐evolutionary scatter search (CCSS). The cooperative co‐evolutionary architecture is characterized by the decomposition and cooperation for dealing with complex engineering problems. SS is a flexible meta‐heuristic method that can effectively solve the combinatorial optimization problems. Designing the elements of SS is context‐dependent. Considering the characteristics of SMLDP, our work focuses on two folds: the diversification method, and the reference set update method. The diversification method is built on the method of coordinate transformation and the controlled randomness. The reference set is updated by the static method on the basis of two dissimilarities. Two test problems for circles packing illustrated the capacity of SS. However, when solving SMLDP, SS shows some limitations in the computational time and quality. This study adopts divide‐conquer‐coordination strategy to decompose SMLDP into several layout sub‐problems. Then CCSS is applied to cooperatively solve these sub‐problems. The experimental results illustrate the capability of the proposed approach in tackling the complex problem with less computational effort.
Findings
Applying CCSS to SMLDP can obtain satisfying solutions in terms of quality and computational efficiency. This contrasts with the limiting experimental results of SMLDP with some approaches (including modified SS).
Originality/value
A new CCSS is proposed to provide an effective and efficient way of solving SMLDP. Some elements of SS are improved to address the layout problem. SMLDP is decomposed into several sub‐problems that can be solved cooperatively by CCSS after its characteristics are taken into consideration.
Details
Keywords
Ziqing Peng and Yan Wan
In this age of extremely well-developed social media, it is necessary to detect any change in the corporate image of an enterprise immediately so as to take quick action to avoid…
Abstract
Purpose
In this age of extremely well-developed social media, it is necessary to detect any change in the corporate image of an enterprise immediately so as to take quick action to avoid the wide spread of a negative image. However, existing survey-based corporate image evaluation methods are costly, slow and static, and the results may quickly become outdated. User comments, news reports and we-media articles on the internet offer varied channels for enterprises to obtain public evaluations and feedback. The purpose of this study is to effectively use online information to timely and accurately measure enterprises’ corporate images.
Design/methodology/approach
A new corporate image evaluation method was built by first using a literature review to establish a corporate image evaluation index system. Next, an automatic text analysis of online public information was performed through a topic classification and sentiment analysis algorithm based on the dictionary. The accuracy of the topic classification and sentiment analysis algorithm is then calculated. Finally, three internet enterprises were chosen as cases, and their corporate image was evaluated.
Findings
The results show that the author’s corporate image evaluation method is effective.
Originality/value
First, in this study, a new corporate image evaluation index system is constructed. Second, a new corporate image evaluation method based on text mining is proposed that can support data-driven decision-making for managers with real-time corporate image evaluation results. Finally, this study improves the understanding of corporate image by generating business intelligence through online information. The findings provide researchers with specific and detailed suggestions that focus on the corporate image management of emerging internet enterprises.