Intelligent decision making for service and manufacturing industries
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EDITORIAL
Intelligent decision making for service and manufacturing industries Junwei Wang1 · Su Xiu Xu2 · Gangyan Xu3
© Springer Science+Business Media, LLC, part of Springer Nature 2019
This special issue is based on the 5th Institute of Industrial Engineers Asian Conference (IIEAsia2016) and the 7th Forum for Council of Industrial Engineering and Logistics Management Department Heads (CIEDH2016). The two conferences were organized by Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong. Most papers of this issue are selected from the best papers of the conferences through a regular review process. Today’s manufacturing and service systems are merging; we have facing more systems that have both service subsystems and manufacturing subsystems (Wang et al. 2014). These systems are working in a more dynamic and uncertain environment. Besides traditional properties of systems, such as efficiency and cost, other properties, such as synchronization (Lin et al. 2018), robustness (Zhou et al. 2017) and resilience (Zhou et al. 2019), have also become concerns of designers and users of systems. Advanced methodologies, such as big data, Internet of things, and cloud computing, have been adopted to improve systems and make decisions for every component/process of systems (Kusiak 2014; Kuo and Kusiak 2018). Decision making is to find an optimal decision from different candidate decisions and thus its essence is optimization. The objective of this special issue is to investigate different decision-making problems * Junwei Wang [email protected] Su Xiu Xu [email protected] Gangyan Xu [email protected] 1
Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Pok Fu Lam, Hong Kong
2
Institute of Physical Internet, School of Intelligent Systems Science and Engineering, Jinan University (Zhuhai Campus), Zhuhai, China
3
School of Architecture, Harbin Institute of Technology, Shenzhen, China
in the manufacturing and service industries by addressing various challenging issues, such as dynamic customers’ demand, real-time data, and changing production conditions. This special issue includes twelve excellent papers, each of which gives contributions to one or several challenging issues in service and manufacturing systems. These papers can be classified into three categories, i.e., product systems, manufacturing systems and logistics systems; each category includes four papers. The first category studies involve novel decision making methods to design various products, including physical products and service products. The paper “Application of combined Kano model and interactive genetic algorithm for product customization” by Dou, Zhang and Nan, considers the customer satisfaction level in the product design problem; in particular, it proposes a novel intelligent optimization algorithm by integrating the interactive genetic algorithm and a customer satisfaction evaluation model. This work is a good attempt on the service-oriented manufacturing ind
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