The rise of e-commerce and globalization has changed consumption patterns. Different industries have different logistical needs. In meeting needs with different schedules logistics play a key role. Delivering a seamless service becomes a source of competitive advantage for the logistics industry. Global positioning system-based fleet management system technology provides synergy to transport companies and achieves many management goals such as monitoring and tracking commodity distribution, energy saving, safety, and quality. A case company, which is a subsidiary of a very famous food and retail conglomerate and operates the largest shipping line in Taiwan, has suffered from the nonsmooth introduction of GPS-based fleet management systems in recent years. Therefore, this study aims to identify key factors for introducing related systems to the case company. By using DEMATEL and ANP, we can find not only key factors but also causes and effects among key factors. The results showed that support from executives was the most important criterion but it has the worst performance among key factors. It is found that adequate annual budget planning, enhancement of user intention, and collaboration with consultants with high specialty could be helpful to enhance the faith of top executives for successfully introducing the systems to the case company.

As a crucial part of producer services, the logistics industry is highly dependent on the manufacturing industry. In general, the interactive development of the logistics and manufacturing industries is essential. Due to the existence of a certain degree of interdependence between any two factors, interaction between the two industries has produced a basis for measurement; identifying the key factors affecting the interaction between the manufacturing and logistics industries is a kind of decision problem in the field of multiple criteria decision making (MCDM). A hybrid MCDM method, DEMATEL-based ANP (DANP) is appropriate to solve this problem. However, DANP uses a direct influence matrix, which involves pairwise comparisons that may be more or less influenced by the respondents. Therefore, we propose a decision model, Grey DANP, which can automatically generate the direct influence matrix. Statistical data for the logistics and manufacturing industries in the China Statistical Yearbook (2006–2015) were used to identify the key factors for interaction between these two industries. The results showed that the key logistics criteria for interaction development are the total number of employees in the transport business, the volume of goods, and the total length of routes. The key manufacturing criteria for interaction development are the gross domestic product and the value added. Therefore, stakeholders should increase the number of employees in the transport industry and freight volumes. Also, the investment in infrastructure should be increased.

The forecast of carbon dioxide (CO₂) emissions has played a significant role in drawing up energy development policies for individual countries. Since data about CO₂ emissions are often limited and do not conform to the usual statistical assumptions, this study attempts to develop a novel multivariate grey prediction model (MGPM) for CO₂ emissions. Compared with other MGPMs, the proposed model has several distinctive features. First, both feature selection and residual modification are considered to improve prediction accuracy. For the former, grey relational analysis is used to filter out the irrelevant features that have weaker relevance with CO₂ emissions. For the latter, predicted values obtained from the proposed MGPM are further adjusted by establishing a neural-network-based residual model. Prediction accuracies of the proposed MGPM were verified using real CO₂ emission cases. Experimental results demonstrated that the proposed MGPM performed well compared with other MGPMs considered.

In Taiwan, the air cargo terminal business is a government franchise. Due to changing order and delivery modes, the volume of cargo exports has gradually decreased in recent years, generating increasing competitiveness within the air cargo terminal industry. Previous studies of air cargo terminals have largely focused on manpower supply planning and business performance. When a company pursues sustainable development and growth, it can improve the quality of the service provided, promote better customer satisfaction and enhance its competitiveness and customer loyalty in order to enjoy sustained development and higher profits. Having carried out a literature review, exploration and interviews with experts, this study develops a preliminary structure that is based on theory and practice and develops a framework for air cargo terminal service quality using the decision-making trial and evaluation laboratory (DEMATEL) method combined with the analytic network process (ANP). Eight key criteria are examined, namely, ‘New facilities or high-tech equipment,’ ‘Professional spirit,’ ‘Security and safety,’ ‘Sense of trust,’ ‘Ability to handle unusual cargo,’ ‘Professional,’ ‘Standard operation processes,’ and ‘Customer equity.’ The causality among each of these principles is also evaluated using the D-ANP questionnaire that was completed by 25 business forwarders. With respect to the quality of service demanded by forwarders, different strategies were drafted separately and provide a frame of reference for air cargo terminal operators.

Grey prediction models for time series have been widely applied to demand forecasting because only limited data are required for them to build a time series model without any statistical assumptions. Previous studies have demonstrated that the combination of grey prediction with neural networks helps grey prediction perform better. Some methods have been presented to improve the prediction accuracy of the popular GM(1,1) model by using the Markov chain to estimate the residual needed to modify a predicted value. Compared to the previous Grey-Markov models, this study contributes to apply the functional-link net to estimate the degree to which a predicted value obtained from the GM(1,1) model can be adjusted. Furthermore, the troublesome number of states and their bounds that are not easily specified in Markov chain have been determined by a genetic algorithm. To verify prediction performance, the proposed grey prediction model was applied to an important grey system problem—foreign tourist forecasting. Experimental results show that the proposed model provides satisfactory results compared to the other Grey-Markov models considered.