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Cloud computing is widely applied in modern industrial areas due to its technological advancement, cost reduction, and applicability. Packets (tasks) belonging to different applications (agents) compete to share the common cloud resource through a series of edge nodes (processors) in pursuit of fast transmission. This paper abstracts the cloud computing system as a multi-agent flow-shop scheduling (MAFS) problem. The objective is to minimise the total completion time of several agents with the restriction that the maximum lateness cannot exceed a given bound. Given the complexity of the considered problem, a branch and bound algorithm combined with several pruning rules and lower bounds is proposed to obtain optimal solutions. Furthermore, the considered problem is generalised to a bi-scenario version, and a bi-population cooperative co-evolutionary (BCCE) algorithm is proposed to solve it. A reinforcement learning-based method is presented to generate the initial population. Several problem-specific intensification strategies are constructed to explore promising solutions. Comprehensive experiments verified the effectiveness of the proposed algorithms. The industrial data from the China Earthquake Network Centre further confirmed the superiority of the BCCE algorithm. Overall, the MAFS model and the proposed algorithms effectively enhance the user experience and reasonably guarantee revenue. [ABSTRACT FROM AUTHOR]

Simple Summary: Proton pump inhibitors (PPIs) and H2 receptor antagonists (H2RAs) are commonly used for acid reduction in patients with peptic ulcer disease or esophageal reflux. Previous studies have shown that acid-reducing agents (ARAs) might affect bevacizumab treatment. This study found that PPI use worsened the oncological outcomes in patients with metastatic colorectal cancer (mCRC) undergoing bevacizumab treatment. There was a negative dose–response relationship when compared to H2RA use. Physicians should consider the benefits and risks of long-term use of ARAs, especially PPIs, in mCRC patients treated with bevacizumab. Background: Approximately one-third of patients with advanced colorectal cancer (CRC) and treated with bevacizumab are prescribed proton pump inhibitors (PPIs) or H2 receptor antagonists (H2RAs). However, there is limited data on the effects of PPIs and H2RAs in these patients. To investigate the oncological outcomes of PPI and H2RA use in CRC patients treated with bevacizumab, we performed a retrospective cohort study using the Taiwan National Health Insurance Research Database and Taiwan Cancer Registry Database from 2005 to 2020. Methods: In CRC patients treated with bevacizumab, the PPI users and H2RA users were matched with patients without acid-reducing agents (ARAs) by 1:4 propensity score matching. PPI users and H2RA users were matched with propensity scoring in a 1:1 ratio. We divided patients into 4 cumulative PPI dosage levels to assess the dose–response relationship. The primary endpoints were 5-year overall survival and cancer-specific survival. Results: Compared with ARA non-users, both H2RA users and PPI users were associated with reduced overall survival. PPI users were associated with more significant negative effects on overall survival. Compared with H2RA users, PPI users were associated with lower 5-year overall survival (aHR: 1.19, 95% CI: 1.09–1.31) and cancer-specific survival (aHR: 1.20, 95% CI: 1.09–1.31). A similar dose–response relationship was observed for PPI users in terms of 5-year overall survival and cancer-specific overall survival. Conclusions: Compared to H2AR use, PPI use was associated with dose-dependent poorer oncological outcomes in metastatic CRC patients treated with bevacizumab. [ABSTRACT FROM AUTHOR]

Customer satisfaction is a prevalent issue amongst manufacturing enterprises. Multi-agent scheduling models aim to optimise the given criteria for improving customer satisfaction by fulfilling the customisation requirements. An investigation is executed on a bi-agent flowshop scheduling model, where a mass of tasks maintained by two competitive agents share a group of successive processors over time. The objective is to determine a feasible schedule that minimises the weighted combination of makespans belonging to two different agents. Asymptotic and worst-case analyses are conducted on a class of dominant-agent-based heuristics proposed to find approximate solutions for large-scale instances. An effective branch and bound algorithm is presented to achieve optimal solutions for small-scale instances, where the release-date-based branching rules and the preemption-based lower bounds significantly speed up the convergence of the proposed algorithm. A discrete artificial bee colony algorithm is introduced to find high-quality solutions for medium-scale instances. Extensive computational experiments are conducted to reveal the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Shared bicycles provide a green, environmentally friendly, and healthy mode of transportation that effectively addresses the "final mile" problem in urban travel. However, the uneven distribution of bicycles and the imbalance of user demand can significantly impact user experience and bicycle usage efficiency, which makes it necessary to predict bicycle demand. In this paper, we propose a novel shared-bicycle demand prediction method based on station clustering. First, to address the challenge of capturing patterns in station-level bicycle demand, which exhibits significant fluctuations, we employ a clustering method that combines graph information from the bicycle transfer graph and potential energy. This method aggregates closely related stations into corresponding prediction regions. Second, we use the GCN-CRU-AM (Graph Convolutional Network-Gated Recurrent Unit-Attention Mechanism) model to predict bicycle demand in each region. This model extracts the spatial information and correlation between regions, integrates time feature data and local weather data, and assigns weights to the input features. Finally, experimental results based on the data from Citi Bike System in New York City demonstrate that the proposed model achieves a more accurate demand prediction. [ABSTRACT FROM AUTHOR]

Recently, finding solutions to assembly flowshop scheduling problems is a topic of extensive discussion in research communities. While existing research assumes that job processing times are constant numbers, in several practical situations, due to several external factors like machine breakdowns, working environment changes, worker performance instabilities, and tool quality variations and unavailability, job processing times may vary. In this study, therefore, we address a two-stage assembly flowshop scheduling problem with two scenario-dependent jobs processing times to minimise the maximum makepsan among both scenarios (called robust makespan) In view of the NP-hard nature, we first derive a dominance property and a lower bound to propose a branch-and-bound algorithm to find a permutation schedule with minimum makespan. Following that, we use Johnson's rule to propose eight polynomial heuristics for finding near-optimal solutions. Furthermore, we propose four cloud theory-based simulated annealing (CSA) hyper-heuristic algorithms incorporating seven low level heuristics to solve a robust two-stage assembly flowshop problem with scenario-dependent processing times. Finally, we empirically evaluate the effectiveness of all the proposed algorithms in minimising the robust makespan. [ABSTRACT FROM AUTHOR]