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The concept of digital twinning has become a hot topic in the manufacturing industry in recent years. The emerging digital twin technology is an intelligent technology that makes full use of multimodels, big data, and interdisciplinary knowledge, which provides some new approaches for the field of the intelligent manufacturing industry. The job shop scheduling problem has been an important research field in the discrete manufacturing industry. Digital twin technology is adopted to solve the problem of job shop scheduling, which provides the possibility for the intelligent development of workshops. Based on digital twin technology and combined with the actual problem of production line scheduling, we propose a new intelligent scheduling platform to solve the shop scheduling problems above. Meanwhile, based on the prediction and diagnosis of multisource dynamic interference in the workshop production process by big data analysis technology, the corresponding interference strategy is formulated in advance by the scheduling cloud platform. The model simulation experiment of intelligent dispatching cloud platform was carried out, and some enterprises in intelligent manufacturing workshop were taken as examples to verify the superiority of the dispatching cloud platform. Finally, we look forward to the future research direction of intelligent manufacturing based on digital twin technology.

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Energy efficiency (EE) is a critical performance indicator for the device-to-device (D2D) communication underlaying cellular networks due to limited battery capacity and serious interference between user equipment. In this study, we proposed a power control and channel allocation scheme for the EE maximization of the D2D pairs, while jointly reusing uplink–downlink resources and guaranteeing the cellular users’ (CUs) quality of service (QoS). The formulated problem was a mixed-integer nonlinear programming (MINLP) problem, which is generally an unsolved non-deterministic polynomial-time hardness (NP-hard) problem within polynomial time. To make it tractable to solve, the original problem was divided into two sub-problems: power control and channel allocation. A power control algorithm based on the Lambert W function was proposed to maximize the EE of the individual D2D pair. Assigning either an uplink or downlink resource to reuse, the EE of each D2D pair was calculated using the power control results. A channel allocation scheme based on the Kuhn–Munkres algorithm utilized the EE weights to optimize the overall EE of the D2D pairs. The simulation results verified the theoretical analysis and proved that the proposed algorithm could remarkably improve the EE of D2D pairs while guaranteeing the QoS of the CUs.