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Distributed manufacturing involving heterogeneous factories presents significant challenges to enterprises. Furthermore, the need to prioritize various jobs based on order urgency and customer importance further complicates the scheduling process. Consequently, this study addresses the practical issue by tackling the distributed heterogeneous hybrid flow shop scheduling problem with multiple priorities of jobs (DHHFSP-MPJ). The primary objective is to simultaneously minimize the total weighted tardiness and total energy consumption. To solve DHHFSP-MPJ, a double deep Q-network-based co- evolution (D2QCE) is developed with four features: i) The global and local searches are allocated into two populations to balance computational resources; ii) A hybrid heuristic strategy is proposed to obtain an initialized population with great convergence and diversity; iii) Four knowledge-based neighborhood structures are proposed to accelerate converging. Next, the double deep Q-Network is applied to learn operator selection; and iv) An energy-efficient strategy is presented to save energy. To verify the effectiveness of D2QCE, five state-of-the-art algorithms are compared on 20 instances and a real-world case. The results of numerical experiments indicate that: i) The D2QN can learn fast by only consuming a few computation resources and can select the best operator. ii) Combining D2QN and co- evolution can vastly improve the performance of evolutionary algorithms for solving distributed shop scheduling. iii) The proposed D2QCE has better performance than state-of-the-arts for DHHFSP-MPJ. Note to Practitioners—This paper is inspired by a real-world problem encountered in blanking workshop systems within the manufacturing of large engineering equipment. In this practical scenario, jobs come with varying priorities and distinct due dates. Balancing these priority and due date constraints while efficiently scheduling a considerable volume of jobs to enhance enterprise profitability poses a significant challenge. Thus, this scheduling problem is abstracted to the distributed heterogeneous hybrid flow shop scheduling problem with multiple priorities of jobs. The objectives are minimizing weighted due date delay and total energy consumption. Notably, this model has never been studied before. To address this, we’ve formulated a mixed-integer linear programming model and developed a novel co- evolutionary algorithm based on double deep Q-networks (DQN). Our approach introduces several key components. First, we present a co- evolutionary framework to strike a balance between global and local search aspects. Additionally, we’ve devised three problem-specific enhancement strategies to expedite convergence, which include hybrid initialization, local search techniques, and energy-saving measures. To accelerate the learning process of selecting the optimal operator with minimal computational resources, we employ the double DQN. Experimental results demonstrate the superior performance of our approach, outperforming state-of-the-art algorithms when applied to a real-world case. In summary, this work proposes an extended DHHFSP and provides a case of designing the deep learning-assisted evolutionary algorithm. However, online deep reinforcement learning (DRL) consumes additional time, and the generalization of online DRL needs to be improved. In future research, we will consider the dynamic events such as new jobs insert and due date change for the blanking workshop. Moreover, the end-to-end model will be considered to save energy and realize sustainable DRL.

With the development of the economy, distributed manufacturing has gradually become the mainstream production mode. This work aims to solve the energy-efficient distributed flexible job shop scheduling problem (EDFJSP) while simultaneously minimizing makespan and energy consumption. Some gaps are stated following: 1) the previous works usually adopt the memetic algorithm (MA) with variable neighborhood search. However, the local search (LS) operators are inefficient due to strong randomness; 2) the confidence-based adaptive operator selection model follows the experiences of the major crowds, which ignores the efficient operators with low weight, so it can not select the really efficient operator; 3) the previous works lack of efficient strategy to save energy; and 4) the mainstream memetic framework adopts LS to all solutions, which causes the population to converge too quickly and the diversity is extremely reduced. Thus, we propose a surprisingly popular-based adaptive MA (SPAMA) to overcome the above deficiencies. The contributions are as follows: 1) four problem-based LS operators are employed to improve the convergence; 2) a surprisingly popular degree (SPD) feedback-based self-modifying operators selection model is proposed to find the efficient operators with low weight and correct crowd decision making; 3) the full active scheduling decoding is presented to reduce the energy consumption; and 4) an elite strategy is designed to balance the resources between global and LS. In order to evaluate the effectiveness of SPAMA, it is compared with state-of-the-art algorithms on Mk and DP benchmarks. The results demonstrate the superiority of SPAMA to the state-of-art algorithms for solving EDFJSP.

Our previous studies found that Sea Buckthorn polyphenols (SBP) extract inhibits fatty acid synthase (FAS) in vitro. Thus, we continued to explore possible effects and underlying mechanisms of SBP on complicated metabolic disorders in long-term high-fat-diet (HFD)-fed mice. To reveal that, an integrated approach was developed in this study. Targeted quantitative lipidomics with a total of 904 unique lipids mapping contributes to profiling the comprehensive features of disarranged hepatic lipid homeostasis and discovering a set of newfound lipid-based biomarkers to predict the occurrence and indicate the progression of metabolic disorders beyond current indicators. On the other hand, technologies of intermolecular interactions characterization, especially surface plasmon resonance (SPR) assay, contribute to recognizing targeted bioactive constituents present in SBP. Our findings highlight hepatic lipid homeostasis maintenance and constituent–FAS enzyme interactions, to provide new insights that SBP as a functional food alleviates HFD-induced metabolic disorders in mice viareprograming hepatic lipid homeostasis caused by targeting FAS, owing to four polyphenols directly interacting with FAS and cinaroside binding to FAS with good affinity.