Smart box enabled product-service system for cloud logistics Modern logistics takes significant progress and rapid developments with the prosperity of E-commerce, particularly in China. Typical challenges that logistics industry is facing now are composed by a lack of sharing, standard, cost-effective and environmental package, and efficient optimization method for logistics tasks distribution. As a result, it is difficult to implement green, sustainable logistics services. Three important technologies, Physical Internet (PI), Product Service System (PSS) and cloud computing (CC), are adopted and developed to address the above issues. PI is extended to design a world-standard green recyclable smart box that is used to encapsulate goods. Smart box enabled product service system is constructed to provide an innovative sustainable green logistics service, and high-quality packaging, as well as reduce logistics cost and environmental pollution. A real-time information driven logistics tasks optimization method is constructed by designing a cloud logistics platform based on cloud computing. On this platform, a hierarchical tree-structure network for customer orders is built up to achieve the order-box matching of function. Then, a distance clustering analysis algorithm is presented to group and form the optimal clustering results for all customer orders, and a real-time information driven optimization method for logistics orders is proposed to minimize the unused volume of containers. Finally, a case study is simulated to demonstrate the efficiency and feasibility of proposed cloud logistics optimization method.

Typical challenges that managers of remanufacturing face are composed of the lack of timely, accurate, and consistent information of remanufacturing resources. Therefore, it is difficult to implement real-time production scheduling for the shop floor. To address this problem, the authors applied the concept of the 'Internet of Things' to the remanufacturing of automobile engines to form an Internet of Manufacturing Things environment. Under the Internet of Manufacturing Things, an identification technology for disassembled engine parts was designed, and the real-time status of the remanufacturable resources can be monitored. Based on the captured remanufacturing information, a real-time production scheduling method was developed, and a mathematical model was developed to achieve cost reduction, dynamic management of remanufacturable resources, and energy consumption decrease. To obtain an optimal solution, a Pareto-based optimization method was used. Finally, a case study was performed to analyze the effectivity of the proposed method. The results showed that the remanufacturing cost and energy consumption were reduced by 34% and 34% respectively, and the worker load rate was more balanced. These improvements can contribute to more sustainable development and greener production within the remanufacturing industry, especially for remanufacturing of automobile engines.

Centralized and one-way logistics services and the lack of real-time information of logistics resources are common in the logistics industry. This has resulted in the increased logistics cost, energy consumption, logistics resources consumption, and the decreased loading rate. Therefore, it is difficult to achieve efficient, sustainable, and green logistics services with dramatically increasing logistics demands. To deal with such challenges, a real-time information-driven dynamic optimization strategy for smart vehicles and logistics tasks towards green logistics is proposed. Firstly, an 'Internet of Things'-enabled real-time status sensing model of logistics vehicles is developed. It enables the vehicles to obtain and transmit real-time information to the dynamic distribution center, which manages value-added logistics information. Then, such information can be shared among logistics companies. A dynamic optimization method for smart vehicles and logistics tasks is developed to optimize logistics resources, and achieve a sustainable balance between economic, environmental, and social objectives. Finally, a case study is carried out to demonstrate the effectiveness of the proposed optimization method. The results show that it contributes to reducing logistics cost and fuel consumption, improving vehicles' utilization rate, and achieving real-time logistics services with high efficiency.

With the rapid advancement and widespread application of information and sensor technologies in manufacturing shop floor, the typical challenges that cloud manufacturing is facing are the lack of real‐time, accurate, and value‐added manufacturing information, the efficient shop floor scheduling strategy, and the method based on the real‐time data. To achieve the real‐time data‐driven optimization decision, a dynamic optimization model for flexible job shop scheduling based on game theory is put forward to provide a new real‐time scheduling strategy and method. Contrast to the traditional scheduling strategy, each machine is an active entity that will request the processing tasks. Then, the processing tasks will be assigned to the optimal machines according to their real‐time status by using game theory. The key technologies such as game theory mathematical model construction, Nash equilibrium solution, and optimization strategy for process tasks are designed and developed to implement the dynamic optimization model. A case study is presented to demonstrate the efficiency of the proposed strategy and method, and real‐time scheduling for four kinds of exceptions is also discussed.