Minimum Cardinality Matrix Decomposition into Consecutive-Ones Matrices: CP and IP Approaches.- Connections in Networks: Hardness of Feasibility Versus Optimality.- Modeling the Regular Constraint with Integer Programming.- Hybrid Local Search for Constrained Financial Portfolio Selection Problems.- The "Not-Too-Heavy Spanning Tree" Constraint.- Eliminating Redundant Clauses in SAT Instances.- Cost-Bounded Binary Decision Diagrams for 0-1 Programming.- YIELDS: A Yet Improved Limited Discrepancy Search for CSPs.- A Global Constraint for Total Weighted Completion Time.- Computing Tight Time Windows for RCPSPWET with the Primal-Dual Method.- Necessary Condition for Path Partitioning Constraints.- A Constraint Programming Approach to the Hospitals / Residents Problem.- Best-First AND/OR Search for 0/1 Integer Programming.- A Position-Based Propagator for the Open-Shop Problem.- Directional Interchangeability for Enhancing CSP Solving.- A Continuous Multi-resources cumulative Constraint with Positive-Negative Resource Consumption-Production.- Replenishment Planning for Stochastic Inventory Systems with Shortage Cost.- Preprocessing Expression-Based Constraint Satisfaction Problems for Stochastic Local Search.- The Deviation Constraint.- The Linear Programming Polytope of Binary Constraint Problems with Bounded Tree-Width.- On Boolean Functions Encodable as a Single Linear Pseudo-Boolean Constraint.- Solving a Stochastic Queueing Control Problem with Constraint Programming.- Constrained Clustering Via Concavity Cuts.- Bender's Cuts Guided Large Neighborhood Search for the Traveling Umpire Problem.- A Large Neighborhood Search Heuristic for Graph Coloring.- Generalizations of the Global Cardinality Constraint for Hierarchical Resources.- A Column Generation Based Destructive Lower Bound for Resource Constrained Project Scheduling Problems.

Integration of AI and OR techniques in constraint programming for combinatorial optimization problems : 4th International Conference, CPAIOR 2007, Brussels, Belgium, May 23-26, 2007 : proceedings

Outpatient appointment systems in healthcare: A review of optimization studies

Patient Flow: Reducing Delay in Healthcare Delivery

No-shows in appointment scheduling - a systematic literature review.

http://mauricio.resende.info/doc/gagt.pdf

An evolutionary algorithm for manufacturing cell formation

Clinical overbooking is intended to reduce the negative impact of patient no-shows on clinic operations and performance. In this paper, we study the clinical scheduling problem with overbooking for heterogeneous patients, i.e. patients who have different no-show probabilities. We consider the objective of maximizing expected profit, which includes revenue from patients and costs associated with patient waiting times and physician overtime. We show that the objective function with homogeneous patients, i.e. patients with the same no-show probability, is multimodular. We also show that this property does not hold when patients are heterogeneous. We identify properties of an optimal schedule with heterogeneous patients and propose a local search algorithm to find local optimal schedules. Then, we extend our results to sequential scheduling and propose two sequential scheduling procedures. Finally, we perform a set of numerical experiments and provide managerial insights for health care practitioners.

Clinic scheduling models with overbooking for patients with heterogeneous no-show probabilities

'No-shows' or missed appointments result in under-utilized clinic capacity. We develop a logistic regression model using electronic medical records to estimate patients' no-show probabilities and illustrate the use of the estimates in creating clinic schedules that maximize clinic capacity utilization while maintaining small patient waiting times and clinic overtime costs. This study used information on scheduled outpatient appointments collected over a three-year period at a Veterans Affairs medical center. The call-in process for 400 clinic days was simulated and for each day two schedules were created: the traditional method that assigned one patient per appointment slot, and the proposed method that scheduled patients according to their no-show probability to balance patient waiting, overtime and revenue. Combining patient no-show models with advanced scheduling methods would allow more patients to be seen a day while improving clinic efficiency. Clinics should consider the benefits of implementing scheduling software that includes these methods relative to the cost of no-shows.

/pdf/using-no-show-modeling-to-improve-clinic-performance-3p5acypyxj.pdf

Using no-show modeling to improve clinic performance.

Chemotherapy operations planning and scheduling in oncology clinics is a complex problem due to several factors such as the cyclic nature of chemotherapy treatment plans, the high variability in resource requirements (treatment time, nurse time, pharmacy time) and the multiple clinic resources involved. Treatment plans are made by oncologists for each patient according to existing chemotherapy protocols or clinical trials. It is important to strictly adhere to the patient’s optimal treatment plan to achieve the best health outcomes. However, it is typically difficult to attain strict adherence for every patient due to side effects of chemotherapy drugs and limited resources in the clinics. In this study, our aim is to develop operations planning and scheduling methods for chemotherapy patients with the objective of minimizing the deviation from optimal treatment plans due to limited availability of clinic resources (beds/chairs, nurses, pharmacists). Mathematical programming models are developed to solve ...

http://www.optimization-online.org/DB_FILE/2010/02/2543.pdf

Chemotherapy Operations Planning and Scheduling

We propose an integrated algorithm that will solve the part-family and machinecell formation problem by simultaneously considering the within-cell layout problem. To the best of our knowledge, this is the first study that considers the efficiency of both individual cells and the overall system in monetary terms. Each cell should make at least a certain amount of profit to attain self-sufficiency, while we maximize the total profit of the system using a holonistic approach. The proposed algorithm provides two alternative solutions; one with independent cells and the other one with inter-cell movement. Our computational experiments indicate that the results are very encouraging for a set of randomly generated problems.

/pdf/cellular-manufacturing-system-design-using-a-holonistic-dy9gl6683e.pdf

Cellular manufacturing system design using a holonistic approach

Due to increasing demand, the oncology clinics have been experiencing higher workloads and increasing delays in laboratory, pharmacy and chemotherapy administration areas. In this study, we worked with an oncology clinic where patients receive chemotherapy treatment. A discrete event simulation model is developed to evaluate the operational performance in the clinic and to identify initiatives for improvement in process flow, scheduling and staffing. A mathematical programming model is developed to generate balanced appointment schedules for oncologist visit and chemotherapy treatment. Our results show that patient waiting times and clinic total working times can be reduced, and a more balanced resource utilisation can be achieved using better scheduling methods.

/pdf/improvement-of-chemotherapy-patient-flow-and-scheduling-in-37qupo3j9c.pdf

Ayten Turkcan

Papers

Clinic scheduling models with overbooking for patients with heterogeneous no-show probabilities

Using no-show modeling to improve clinic performance.

Chemotherapy Operations Planning and Scheduling

Cellular manufacturing system design using a holonistic approach

Improvement of chemotherapy patient flow and scheduling in an outpatient oncology clinic