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

Optimisation de laboratoires médicaux

This thesis focuses on the optimization of clinical laboratory design and operating decisions. A clinicallaboratory is an organization gathering human and machinery resources to analyze blood samples. Inthis thesis, a decision support tool including mathematical models, a heuristic algorithm and acustomized simulation model is developed to aid decision makers for the main strategic, tactical andoperational problems in clinical laboratory design and operations management. This decision supporttool follows a top-down stepwise framework starting from strategic problems and ending withoperational ones, including a recursive loop for modification and improvement. In this thesis, machineselection and facility layout are studied as the main strategic problems, analyzer configuration problemas the tactical problem, and assignment, aliquoting, and scheduling as the principal operationalproblems. In order to deal with machine selection problem for clinical laboratory, a mathematical modelis proposed which aids to select the most appropriate machines to equip the system. To tackle physicalarrangement of instruments within the laboratory area, a heuristic approach is developed. The proposedheuristic comprises the key constraints of laboratory layout design. To address the analyzerconfiguration problem which mainly deals with the assignment of chemical materials to the analyzersin clinical laboratory, a bi-objective mathematical model is developed. In addition, to determine anefficient assignment of sample tubes to the analyzers, a mathematical model with three objectives isproposed. A customized, flexible, and fine-grained simulation model is developed in FlexSim to studythe clinical laboratory designed through the outputs of developed mathematical models and layoutalgorithm. Simulation model plays a key role in the proposed framework as it is used for many purposes.The simulation model helps the designer to construct and analyze a complete clinical laboratory takinginto account all major features of the system. This simulation attribute provides the ability to scrutinizethe system behaviour and to find out whether the designed system is efficient. System performanceanalysis through simulation and resulting key performance indicators give helpful feedbacks for systemimprovement. Furthermore, simulation model can be fruitful to decide on scheduling, aliquoting andstaffing problems through the evaluation of various scenarios proposed by decision maker for each ofthese problems. To verify the validity of the proposed framework, data extracted from a real case isused. The output results seal on the applicability and the efficiency of the proposed framework as wellas competency of proposed techniques to deal with each optimization problem. To the best of ourknowledge, this thesis is one of the leading studies on the optimization of clinical laboratories.Cette thèse porte sur l'optimisation de la conception et des décisions opérationnelles des laboratoires d'analyses médicales. Dans cette thèse, un outil d'aide à la décision comprenant des modèles mathématiques, un algorithme heuristique et un modèle de simulation personnalisé est développé pour aider les décideurs à résoudre les principaux problèmes stratégiques, tactiques et opérationnels en conception et gestion des opérations des laboratoires d'analyses médicales. Dans cette thèse, la sélection des machines et la disposition des instruments sont étudiées en tant que principaux problèmes stratégiques, le problème de configuration des analyseurs en tant que problème tactique et l’affectation, l’aliquotage et l'ordonnancement en tant que principaux problèmes opérationnels. Un modèle de simulation personnalisé et flexible est développé dans FlexSim pour étudier le laboratoire d'analyse médicale conçu à l'aide des résultats de modèles mathématiques et d'un algorithme de layout développés. Le modèle de simulation aide le concepteur à construire et à analyser un laboratoire complet en tenant compte de toutes les principales caractéristiques du système. Cet attribut de simulation permet d'analyser le comportement du système et de déterminer si le système conçu est efficace. Pour vérifier la validité du cadre proposé, les données extraites d’un cas réel sont utilisées. Les résultats de sortie scellent l'applicabilité et l'efficacité du cadre proposé ainsi que la compétence des techniques proposées pour traiter chaque problème d'optimisation. À notre connaissance, cette thèse est l’une des principales études sur l’optimisation des laboratoires d'analyses médicales

Optimisation of the structure of the clinical laboratory

In many clinical laboratories span of control problems make it necessary to divide the clinical laboratory into sections (departments, job shops). A section is in fact a set of workstations where tests are performed on samples. A mathematical programming model is described which clusters workstations in such way that the maximum idle time of staff in staff assignment periods is minimized. An example demonstrates the usefulness of the approach