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This paper attempts to derive a mathematical formulation for real-practiceclinical laboratory schedul-ing, and to present a syntactic adaptive problemsolver by leveraging landscape structures. After formulating scheduling ofmedical tests as a distributed scheduling problem in heterogeneous, flexiblejob shop environment, we establish a mixed integer programming model tominimize mean test turn-around time. Preliminary landscape analysis sustainsthat these clinics-orientated scheduling instances are difficult to solve. Thesearch difficulty motivates the search for an adaptive problem solver to reducerepetitive algorithm-tuning work, but with a guaranteed convergence. Yet, undera search strategy, relatedness from exploitation competence to landscapetopology is not transparent. Under strategies that impose different-magnitudeperturbations, we investigate changes in landscape struc-ture and find thatdisturbance amplitude, local-global optima connectivity, landscape's ruggednessand plateau size fairly predict strategies' efficacy. Medium-size instances of100 tasks are easier un-der smaller-perturbation strategies that lead tosmoother landscapes with smaller plateaus. For large-size instances of 200-500tasks, existing strategies at hand, having either larger or smallerperturba-tions, face more rugged landscapes with larger plateaus that impedesearch. Our hypothesis that me-dium perturbations may generate smootherlandscapes with smaller plateaus drives our design of this new strategy and itsverification by experiments. Composite neighborhoods managed by meta-Lamarckianlearning show beyond average performance, implying reliability when priorknowledge of landscape is unknown.

A Syntactic Adaptive Problem Solver Learning Landscape Structures for Scheduling in Clinical Laboratory