As a major challenge and opportunity for traditional manufacturing, intelligent manufacturing is facing the needs of sustainable development in future. Sustainability assessment undoubtedly plays a pivotal role for future development of intelligent manufacturing. Aiming at this, the paper presents the digital twin driven information architecture of sustainability assessment oriented for dynamic evolution under the whole life cycle based on the classic digital twin mapping system. The sustainability assessment method segment of the architecture includes indicator system building, indicator value determination, indicator importance degree determination and intelligent manufacturing project assessing. A novel approach for treating the ambiguity of expert’ judgment in indicator value determination by introducing trapezoidal fuzzy number into analytic hierarchy process is proposed, while the complexity of the influence relationship among the indicators is processed by the integration of complex networks modeling and PROMETHEE II for the indicator importance degree determination. A two-stage evidence combination model based on evidence theory is built for intelligent manufacturing project assessing lastly. The presented digital-twin-driven information architecture and the sustainability assessment method is tested and validated on a study of sustainability assessment of 8 intelligent manufacturing projects of an air conditioning enterprise. The results of the presented method were validated by comparing them with the results of the fuzzy and rough extension of the PROMETHEE II, TOPSIS and VIKOR methods, indicator importance degree determining method by entropy and indicator value determining method by accurate expert scoring.

Increasingly, circular economy (CE) has been adopted globally to operationalize supply chain sustainability. The development of industry 4.0 technologies provides a new opportunity to improve the effectiveness and efficiency of adoption of CE, in particular, from the waste management perspective. More recently, scholars acknowledge the need for more studies on industry 4.0 and CE-driven sustainability aspects in supply chains. This research aims to fill the literature void and make a contribution from the perspective of smart waste management in supply chains using industry 4.0-based CE operations. Eleven key drivers were identified through semi-structured interviews, administered to experienced supply chain practitioners in China. A fuzzy DEMATEL method was used to analyse the interrelationships among these key drivers. The results show that the most fundamental causal drivers of smart waste management are overcoming operational challenges, recovering value, speeding up operations, saving cost and improving profit. There is a virtuous cycle between market demand and the improving price-performance ratio of industry 4.0 technologies. Our findings are part of the development of a bottom-up approach to adopting smart waste management in supply chains. The interrelationships identified in this research provide valuable insights into driving forces. Organizations, policy makers and technology providers can apply these insights when utilizing industry 4.0 technologies to improve supply chain waste management in line with the CE principle, and to achieve supply chain sustainability.

With the continuous pressures on customized requirements demands, large-scale multiple-stage production systems in industrial parks are suffering from the complex dynamic impact of uncertainties. These uncertainties are caused by the randomness of customer orders, dynamics of production demand, the uncertainty of production resources, etc. It brings enormous challenges to system operation and decision-making for the Production Logistics (PL) system, which includes how to acquire the full-scale, accurate and real-time execution status of the PL system, and how to make a systematic and adaptability decision by synchronizing every stage in the system under the uncertainties. Taking the PL system of an industrial park as the research object, this paper analyses the problems and challenges in the operation mode of the transportation stage, the perception of dynamics, and the synchronization management and control between the related stages in the dynamic environment. Then the mode of Vehicle Routing Problems with Pick-up and Delivery (VRPDP) is improved and a digital twin-driven decision-making information architecture is proposed, meanwhile, a real-time dynamic synchronization control mechanism is developed to synchronizing the PL system through the transportation stage. Finally, a case study is proposed to verify the feasibility and validity of the proposed method under dynamic disturbance.