Operational strategy of customized bus considering customers' variety seeking behavior and service level.

In recent years, customized bus (CB) is becoming an innovative model of the public transport system (PTS) in China. This service provides an advanced, timely, personalized, and flexible responses to the demand of the PTS, especially of commuters. Obviously, it could have an impact on public transport (PT) service. For instance, CB services influence the customers' variety-seeking behavior, which results in changes of customer options in different periods. Therefore, customers could be divided into different groups of preference. In this paper, we construct a two-period Hotelling game model by variety-seeking behavior and service level. The results show that, in the first stage, the departure frequency of CB and PT is influenced by variety-seeking behavior and service level. In the second stage, departure frequency is influenced by service level only. Next, although the departure frequency of bus impacts the changes of passenger ratios in different stages, service level appreciably impacts passenger number. From customer surplus and social welfare, we find that adjustment of service level can increase social welfare, which means that CB and PT will reach an optimum effect. The present work can provide a valuable reference to policymakers, practitioners, and others.

A novel predict-then-optimize method for sustainable bike-sharing management: a data-driven study in China.

Sustainable operations management will appeal to the post-pandemic world. As the economy recovers, the surging demand for low-carbon bike-sharing has led to exacerbated mismatch in urban transportation. It is a serious challenge to optimize the reallocation schedule of sharing bikes among multiple positions in a network. To address the problem, we develop a novel predict-then-optimize method consisting of a data-driven robust optimization model and a branch-and-price algorithm. The optimization model derives the predicted demand surplus of each position based on historical data, enabling the optimal reallocation schedule in the network at minimum operational costs. Based on the prediction, the branch-and-price algorithm can find out the best routes of assigning bikes to specific positions that further improves transportation efficiency. Finally, we deploy the predict-then-optimize method to a realistic bike-sharing network in one major city of China. The computational results demonstrate that our method can significantly save the cost of operations and reduce the waste of resources. Therefore, the novel predict-then-optimize method has a great potential to facilitate the sustainable development of bike-sharing systems in urban transportation.

Driving Mechanism of Power Battery Recycling Systems in Companies.

In recent years, international environmental and public health research has become a hot topic, and battery recycling, which is often mentioned separately from waste disposal, has likewise become an academic topic. Battery recycling research is beneficial not only for controlling toxic and harmful substances, but also for public health. In addition, battery recycling brings value-added benefits to company management. As the most important link in the battery supply chain, the driving mechanism of battery recycling in the new electric vehicle industry will become particularly important. The subject of battery recycling is diverse, and the relationships among influencing factors are complex, thereby presenting a fluctuating state. Against this background, this study constructs a system dynamics model from the perspective of a main sorting and recycling system, a technological innovation subsystem and a replacement subsystem. Moreover, this study examines the driving mechanism of the power battery recycling system of a microlevel company. Focusing on the systematic impact of technological innovation capability and substitution, we find that the technological innovation drive of companies increases the total effect of required costs and product demands. It is embodied in two aspects, that is, the increase in the recovery rate leads to an increase in demand, whereas the increase in actual expenditures is less than the increase in technology-driven benefits. After technological innovation capability is improved, the effect of the technological innovation multiplier on the driving mechanism of companies is shown as rapid response time changes. In the substitution component of a company recycling system, we find that the maximum substitution rate limiting expectations has no significant impact on product differentiation. The leading effect of technological innovation capability is more obvious than that of substitution activity. Based on these findings, several suggestions for company operation and environmental governance are presented.

Fiscal Decentralization and Local Environmental Pollution in China.

Fiscal decentralization is one of the tools for the central government to engage local governments in environment management. However, its effects are inconclusive. This paper aims to examine the impact of fiscal decentralization on environmental pollution and the role of government environmental preference in China's provinces. The results show that fiscal revenue decentralization exacerbates local environmental pollution more seriously than expenditure decentralization. This negative environmental effect of fiscal decentralization could be moderated by government environmental preference. Based on our findings, it is recommended that China's local governments should improve environmental preference so that fiscal decentralization can create a win-win situation for the economy and environment. Furthermore, the different effects of fiscal revenue and expenditure decentralization create a necessity for differentiated management of fiscal decentralization by the central and local governments.

Recent Development in Big Data Analytics for Business Operations and Risk Management.

"Big data" is an emerging topic and has attracted the attention of many researchers and practitioners in industrial systems engineering and cybernetics. Big data analytics would definitely lead to valuable knowledge for many organizations. Business operations and risk management can be a beneficiary as there are many data collection channels in the related industrial systems (e.g., wireless sensor networks, Internet-based systems, etc.). Big data research, however, is still in its infancy. Its focus is rather unclear and related studies are not well amalgamated. This paper aims to present the challenges and opportunities of big data analytics in this unique application domain. Technological development and advances for industrial-based business systems, reliability and security of industrial systems, and their operational risk management are examined. Important areas for future research are also discussed and revealed.