The implementation of the autonomous unmanned aerial mobility is a game changer for the on-demand delivery service in the crowded urban setting. In this study, the first of its kind commercial unmanned aerial vehicle (UAV) urban delivery program in China is targeted. Different from the traditional ground pickup and delivery services, the aerial mode considers not only the time window constraints, but also the spatial conflicts incurred during the take-off and landing operations of UAVs. To obtain the optimal flying routes of the focused problem, a mixed integer programming model is formulated. Due to its inherent complexity, the optimal schedule cannot be attained within acceptable time via the off-the-shelf solvers. To help speed up the solving process, a branch-and-cut based exact algorithm is proposed, together with a series of customized valid inequalities. To further accelerate, a greedy insertion heuristic is designed to secure high-quality initial solutions. In the numerical section, it is observed that the algorithm proposed in this paper can help solve the real-life on-demand UAV delivery problem to near optimum (within 5% optimality gap) within reasonable computation time (in 5 minutes). Note to Practitioners—With the increase of labor cost, the distribution cost increases very rapidly. In the meantime, the employment of automated vehicles for logistics reshapes the landscape of the urban last-mile delivery. As an efficient courier carrier, the unmanned aerial vehicle (UAV) is trending the autonomous delivery endeavour. When integrating UAVs into the urban delivery program, practitioners need to pay special attention to the scheduling of UAVs at the operational level in addition to the hardware of the UAVs. To help solve the UAV dispatch problem, we propose an online scheduling scheme, considering the spatial conflict constraints in the actual UAV operations. And an exact algorithm is designed to accelerate the solving process. Numerical experiments demonstrate that the proposed algorithm can achieve near optimal dispatch plan with 5% optimality gap in 5 minutes. Furthermore, it is discovered that the demand pooling is an essential decision to make for UAV-based delivery. Longer pooling time can increase the UAV efficiency with more realized demand information, but too much pooling could lead to prolonged customer waiting and a low service level.

Effective sales prediction for e-commerce would assist retailers in developing accurate production and inventory control plans, which would further help them to reduce inventory costs and overdue losses. This paper develops a systematic method for e-commerce sales prediction, with a particular focus on predicting the sales of products with short shelf lives. The short-shelf-life product sales prediction problem is poorly addressed in the existing literature. Unlike products with long shelf lives, short-shelf-life products such as fresh milk exhibit significant fluctuations in sales volume and incur high inventory costs. Therefore, accurate prediction is crucial for short-shelf-life products. To solve these issues, a stacking method for prediction is developed based on the integration of GRU and LightGBM. The proposed method not only inherits the ability of the GRU model to capture timing features accurately but also acquires the ability of LightGBM to solve multivariable problems. A case study is applied to examine the accuracy and efficiency of the GRU-LightGBM model. Comparisons among other sales prediction methods such as ARIMA and SVR are also presented. The comparative results show that the GRU-LightGBM model is able to predict the sales of short-shelf-life products with higher accuracy and efficiency. The selected features of the GRU-LightGBM model are also useful due to their interpretability while developing sales strategies.

In the modern age of Industry 4.0 and manufacturing servitisation, energy saving and environment consciousness are regarded as vital themes in manufacturing processes to reduce carbon tax and achieve sustainable development. For the past 20 years, the concept of green manufacturing has grown from infancy to a fully formed framework agreed upon by world‐leading enterprises. With the unprecedented development of the information technology today, the industrial data collected could assist in the in‐depth study on green manufacturing, which ranges from the operations of machining tools all the way to supply chain management. The wide scope of research promises a tremendous amount of annual publications in this field. To better facilitate follow‐up research work, the present study provides a systematic overview of green manufacturing‐related areas, including research progress and the developed features. The article set retrieved from the Web of Science contains 5989 documents related to green manufacturing. It is revealed that Journal of Cleaner Production is the most productive journal, archiving documents within the scope of green manufacturing. P. R. China tops the list of the number of documents with 1357 documents (22.66%), while Zhejiang University is the most productive institution. As the cooperation network indicates, P. R. China and the United States maintain the strongest collaborative links with other countries/regions. Finally, possible future directions are recommended based on the findings in the study. For instance, additive manufacturing technology and industrial IoT both have a great potential in green manufacturing; the weak link between the disciplines of manufacturing engineering and environmental science is expected to be strengthened, and a stronger international cooperation is believed to be beneficial to the field for the otherwise isolated countries/regions.

To gain sustainable development, it is a trend that manufacturing companies are change the value chain from manufacturing-centric to service-centric. Therefore, the capability of the manufacturing service is as significant as the production ability of enterprises, which reflects the supply chain management (SCM), flexible production, production efficiency, and other indicators of the enterprises. It is the first paper to discuss the sustainability of service-oriented manufacturing using bibliometric analysis. It derives a detailed review and future outlook on the development of manufacturing servitization, indicating the research directions for future development, and provides a valuable reference for researchers in related directions. The bibliometric analysis discusses countries or regions, research areas, authors, keywords, institutions, and journals based on the literature data from the Web of Science (WoS). The results show that research on manufacturing services has gradually received attention since its inception and has become popular since 2008. The papers published from 2008 to 2021 account for 77.62%. The USA is the most studied country on this topic, followed by China and the UK. The International Journal of Production Research regarding the most quantity of articles, and Beihang University is the most influential institution in this field. The largest amount of articles published in the area of “business and economics”, amounting to 1565 articles. In recent years, the main research areas included “Industry 4.0”, “cloud manufacturing (CMfg)”, “Internet of Things (IoT)”, “big data” and “services innovation”. Finally, “digital and intelligent manufacturing” and “product-service systems” are potential research directions for the future.

The optimal power flow problem is one of the most widely used problems in power system optimizations, which are multi-modal, non-linear, and constrained optimization problems. Effective constrained optimization methods can be considered in tackling the optimal power flow problems. In this paper, an $ϵ$-constrained method-based adaptive differential evolution is proposed to solve the optimal power flow problems. The $ϵ$-constrained method is improved to tackle the constraints, and a p-best selection method based on the constraint violation is implemented in the adaptive differential evolution. The single and multi-objective optimal power flow problems on the IEEE 30-bus test system are used to verify the effectiveness of the proposed and improved $\epsilon$adaptive differential evolution algorithm. The comparison between state-of-the-art algorithms illustrate the effectiveness of the proposed and improved $\epsilon$adaptive differential evolution algorithm. The proposed algorithm demonstrates improvements in nine out of ten cases.