  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.
