一种利用景观结构的临床实验室调度句法自适应问题求解器
A Syntactic Adaptive Problem Solver Learning Landscape Structures for Scheduling in Clinical Laboratory
AI总结：
本文为临床实验室调度建立混合整数规划模型，通过分析景观结构提出一种句法自适应求解器，采用中等扰动和元拉马克学习，有效求解不同规模实例并保证收敛。
摘要：
本文旨在为实际临床实验室调度问题推导出数学公式，并通过利用景观结构提出一种句法自适应问题求解器。在将医学检验调度问题表述为异构柔性作业车间环境下的分布式调度问题后，我们建立了一个混合整数规划模型，以最小化平均检验周转时间。初步的景观分析表明，这些面向诊所的调度实例难以求解。搜索难度促使我们寻求一种自适应问题求解器，以减少重复的算法调参工作，同时保证收敛性。然而，在某种搜索策略下，开发能力与景观拓扑之间的关联并不透明。在施加不同幅度扰动的策略下，我们研究了景观结构的变化，发现扰动幅度、局部-全局最优连通性、景观的崎岖度和平坦区大小能较好地预测策略的有效性。对于100个任务的中等规模实例，采用较小扰动策略更容易求解，这些策略产生更平滑且平坦区更小的景观。对于200-500个任务的大规模实例，现有的策略（无论扰动较大还是较小）都面临更崎岖且平坦区更大的景观，从而阻碍搜索。我们假设中等扰动可能产生更平滑且平坦区更小的景观，这一假设指导了我们设计新策略并通过实验验证。由元拉马克学习管理的复合邻域表现出优于平均的性能，表明在景观先验知识未知时具有可靠性。
英文摘要：
This paper attempts to derive a mathematical formulation for real-practice clinical laboratory schedul-ing, and to present a syntactic adaptive problem solver by leveraging landscape structures. After formulating scheduling of medical tests as a distributed scheduling problem in heterogeneous, flexible job shop environment, we establish a mixed integer programming model to minimize mean test turn-around time. Preliminary landscape analysis sustains that these clinics-orientated scheduling instances are difficult to solve. The search difficulty motivates the search for an adaptive problem solver to reduce repetitive algorithm-tuning work, but with a guaranteed convergence. Yet, under a search strategy, relatedness from exploitation competence to landscape topology is not transparent. Under strategies that impose different-magnitude perturbations, we investigate changes in landscape struc-ture and find that disturbance amplitude, local-global optima connectivity, landscape's ruggedness and plateau size fairly predict strategies' efficacy. Medium-size instances of 100 tasks are easier un-der smaller-perturbation strategies that lead to smoother landscapes with smaller plateaus. For large-size instances of 200-500 tasks, existing strategies at hand, having either larger or smaller perturba-tions, face more rugged landscapes with larger plateaus that impede search. Our hypothesis that me-dium perturbations may generate smoother landscapes with smaller plateaus drives our design of this new strategy and its verification by experiments. Composite neighborhoods managed by meta-Lamarckian learning show beyond average performance, implying reliability when prior knowledge of landscape is unknown.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
