Long-term average throughput-utilization utility maximization in platform-aggregated manufacturing service collaboration

IF 12.2 1区 工程技术 Q1 ENGINEERING, INDUSTRIAL
Yanshan Gao , Ying Cheng , Lei Wang , Fei Tao , Qing-Guo Wang , Jing Liu
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引用次数: 0

Abstract

Enhancing capacity utilization of manufacturing resources is of utmost importance in tackling the current challenges of meeting customized and small-batch market demands. Given the research highlights on platform-based manufacturing service collaboration (MSC) offering high-quality service solutions, efficient service scheduling strategies are urgently needed to maximize overall utility amidst great computational complexity and unpredictable task arrivals. To address this issue, this paper proposes a novel distributed online task dispatch and service scheduling (DOTDSS) strategy in platform-aggregated MSC. What sets our method apart is its goal to optimize a long-term average utility performance with considering queuing dynamics of manufacturing services in multi-task processing, thereby maintaining sustainable platform operations. Firstly, we jointly consider task dispatch and service scheduling decisions into the formulation of a quality-of-service aware (QoS) stochastics optimization problem. The newly constructed logarithmic utility function effectively strikes a trade-off between the throughput and capacity utilization of manufacturing services with diverse capabilities. By incorporating the goal of reducing queue lengths, we then transform the optimization problem into a form with less computational complexity and guaranteed optimality using Lyapunov optimization. We further propose a DOTDSS strategy that relies solely on the current system state and queue information to generate scalable MSC solutions. It does not need to predict task arrival statistics in advance, and it exhibits great adaptability to uncertainties in task arrivals and service availabilities. Finally, numerical results based on simulation data and real workload traces demonstrate the effectiveness of our method. It also shows that the aggregation collaboration pattern among a group of candidates can achieve better performance than that by the optimal candidate alone.
平台聚合制造服务协作中的长期平均吞吐量-利用率效用最大化
提高制造资源的产能利用率对于应对当前满足定制化和小批量市场需求的挑战至关重要。鉴于基于平台的制造服务协作(MSC)提供高质量服务解决方案的研究重点,迫切需要高效的服务调度策略,以便在计算复杂性高和任务到达不可预测的情况下实现整体效用最大化。为解决这一问题,本文提出了平台聚合式 MSC 中的新型分布式在线任务调度和服务调度(DOTDSS)策略。我们的方法与众不同之处在于,它的目标是在多任务处理中考虑制造服务的排队动态,优化长期平均效用性能,从而保持平台的可持续运营。首先,我们在制定服务质量(QoS)随机优化问题时共同考虑了任务调度和服务调度决策。新构建的对数效用函数有效地权衡了具有不同能力的生产服务的吞吐量和产能利用率。通过纳入减少队列长度的目标,我们利用 Lyapunov 优化将优化问题转化为计算复杂度更低且保证最优的形式。我们进一步提出了一种 DOTDSS 策略,该策略仅依靠当前的系统状态和队列信息来生成可扩展的 MSC 解决方案。它无需提前预测任务到达统计信息,对任务到达和服务可用性的不确定性具有很强的适应性。最后,基于仿真数据和真实工作负载跟踪的数值结果证明了我们方法的有效性。它还表明,一组候选者之间的聚合协作模式能取得比单独最优候选者更好的性能。
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来源期刊
Journal of Manufacturing Systems
Journal of Manufacturing Systems 工程技术-工程:工业
CiteScore
23.30
自引率
13.20%
发文量
216
审稿时长
25 days
期刊介绍: The Journal of Manufacturing Systems is dedicated to showcasing cutting-edge fundamental and applied research in manufacturing at the systems level. Encompassing products, equipment, people, information, control, and support functions, manufacturing systems play a pivotal role in the economical and competitive development, production, delivery, and total lifecycle of products, meeting market and societal needs. With a commitment to publishing archival scholarly literature, the journal strives to advance the state of the art in manufacturing systems and foster innovation in crafting efficient, robust, and sustainable manufacturing systems. The focus extends from equipment-level considerations to the broader scope of the extended enterprise. The Journal welcomes research addressing challenges across various scales, including nano, micro, and macro-scale manufacturing, and spanning diverse sectors such as aerospace, automotive, energy, and medical device manufacturing.
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