1 research outputs found
Mixed integer programming and adaptive problem solver learned by landscape analysis for clinical laboratory scheduling
This paper attempts to derive a mathematical formulation for real-practice
clinical laboratory scheduling, and to present an 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 turnaround time. Preliminary landscape analysis sustains that these
clinics-orientated scheduling instances are difficult to solve. The search
difficulty motivates the design of 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 structure 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 under smaller-perturbation strategies that lead to
smoother landscapes with smaller plateaus. For large-size instances of 200-500
tasks, extant strategies at hand, having either larger or smaller
perturbations, face more rugged landscapes with larger plateaus that impede
search. Our hypothesis that medium 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