论文标题
用于临床实验室计划的句法自适应问题解决方案学习景观结构
A Syntactic Adaptive Problem Solver Learning Landscape Structures for Scheduling in Clinical Laboratory
论文作者
论文摘要
本文试图通过利用景观结构来提出用于实践临床实验室调整的数学公式，并通过利用景观结构来提出句法自适应问题解决者。在将医疗测试的调度作为异质，灵活的车间环境中的分布式调度问题制定后，我们建立了混合整数编程模型，以最大程度地减少平均测试转弯时间。初步景观分析认为，这些面向诊所的调度实例难以解决。搜索困难激发了寻找自适应问题解决者的搜索，以减少重复算法调整工作，但可以保证收敛。但是，在搜索策略下，从剥削能力到景观拓扑的相关性并不透明。在施加不同磁性扰动的策略下，我们研究了景观结构的变化，发现干扰幅度，局部全球最佳连接，景观的坚固性和高原大小相当可预测策略的功效。 100个任务的中型实例是更容易的较小的扰动策略，导致具有较小高原的景观更平滑。对于大尺寸的200-500任务实例，现有的策略，具有较大或较小的厌食状态，面临更坚固的景观，并具有较大的高原，妨碍搜索。我们的假设是，米 - 扰动可能会产生更平稳的景观，而较小的高原可以推动我们对这一新策略的设计及其通过实验进行验证。由Meta-Lamarckian学习的综合街区超出了平均表现，这意味着当景观的先验知识尚不清楚时，这意味着可靠性。
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.
