# Special Issue: Smart Service Technology for Industrial Applications
## 3 Performance Evaluation and Applications in the Smart Manufacturing and Service Technology  
In this subfield, six papers were published. Highlights are individually addressed below:  
1. Applying ANN and TM to Build a Prediction Model for the Site Selection of a Convenience Store: This paper used the techniques of Artificial Neural Network (ANN) and Back Propagation Neural Networks (BPN) to build a systematic and reliable prediction model for selecting locations of the convenience store chains, in order to improve the existing decision-making method for choosing locations, which is currently done by experienced managers. This model adopted the Taguchi method (TM) to find the optimal parameters of the BPN. The study results demonstrated that the prediction accuracy and decision-making quality of this model are higher than those of the existing manager-oriented decision-making method [10].
2. Multi-Relational Graph Convolution Network for Service Recommendation in Mashup Development: This paper solved the problems of data scarcity and cold start faced in service recommendation, and proposed a novel framework of Multi-Relational Graph Convolutional Networks (MRGCN) used for service recommendation. This study revealed that the method proposed by this paper, under certain conditions, can outperform the state-of-the-art method in the aspect of service recommendation [11].
3. Parallel-Structure Deep Learning for Prediction of Remaining Time of Process Instances: This paper derived a new deep learning method consisting of a convolutional neural network (CNN) and a multilayer perceptron (MLP) with embedded layers in the context of the growing popularity of deep learning and the need of exploiting heterogeneous data.  
This method performed experiments with real-world datasets, and the result showed that the proposed method can achieve more accurate predictions [12].  
Simulation and Optimization for a Closed-Loop Vessel Dispatching Problem in the Middle East Considering Various Uncertainties:  
Due to uncertainties such as weather and unexpected incidents, as well as the lack of effective management techniques, the operation of the closed-loop shipping system often lags behind. Therefore, this paper developed a detailed and realistic simulation model to evaluate the economic and environmental performance of the closed-loop vessel dispatching system, and also took into account various uncertainties of weather and port operations. In addition, this paper incorporated the optimization model into the simulation model and proposed a new vessel dispatching policy which first applied to large vessels, in order to achieve cost savings and reduce greenhouse gas emissions [13].  
Fault Location and Restoration of Microgrids via Particle Swarm Optimization:  
This paper first introduced the fault location algorithm and the recovery algorithm, respectively. The fault location algorithm was based on the network connection matrix to form a new system topology. After the fault section was assigned, the particle swarm algorithm was used to realize the multi-objective function, and the optimal recovery was carried out under its constraints. Finally, a series of simulations and analyses were performed for the sample system. The result showed that the proposed optimal algorithm can effectively solve the problem of fault location and recovery in the micro-grids [14].  
Analysis of the Effectiveness of Promotion Strategies of Social Platforms for the Elderly with Different Levels of Digital Literacy:  
This paper aims to examine strategies for social platform promotion. Based on empirical results of the innovative diffusion research, an agent-based model was created to analyze the effectiveness of social platform promotion strategies for the elderly with different levels of digital literacy. The result demonstrated that the proportion of passive information receivers (PIR) has a direct negative impact on market penetration and a moderating effect on the effectiveness of various promotional strategies [15].