Denton, Brian T.

Background and Aims. Colorectal cancer, a leading cause of cancer death, is preventable with colonoscopic screening. Colonoscopy cost is high, and optimizing resource utilization for colonoscopy is important. This study’s aim is to evaluate resource allocation for optimal use of facilities for colonoscopy screening. Method. The authors used data from a computerized colonoscopy database to develop a discrete event simulation model of a colonoscopy suite. Operational configurations were compared by varying the number of endoscopists, procedure rooms, the patient arrival times, and procedure room turnaround time. Performance measures included the number of patients served during the clinic day and utilization of key resources. Further analysis included considering patient waiting time tradeoffs as well as the sensitivity of the system to procedure room turnaround time. Results. The maximum number of patients served is linearly related to the number of procedure rooms in the colonoscopy suite, with a fixed room to endoscopist ratio. Utilization of intake and recovery resources becomes more efficient as the number of procedure rooms increases, indicating the potential benefits of large colonoscopy suites. Procedure room turnaround time has a significant influence on patient throughput, procedure room utilization, and endoscopist utilization for varying ratios between 1:1 and 2:1 rooms per endoscopist. Finally, changes in the patient arrival schedule can reduce patient waiting time while not requiring a longer clinic day. Conclusions. Suite managers should keep a procedure room to endoscopist ratio between 1:1 and 2:1 while considering the utilization of related key resources as a decision factor as well. The sensitivity of the system to processes such as turnaround time should be evaluated before improvement efforts are made.

The goal of “Practice Abstracts” is to present interesting, topical, and novel applications of operations research methodology to a wide range of industrial applications. “Practice Abstracts” are intended to provide Interfaces readers with short (2–4 page) descriptions of the most relevant aspects of operations research-based projects, in a form that is accessible to academics and practitioners in other organizations. Contributions should be sent for evaluation to the editor of “Practice Abstracts,” Brian T. Denton, Division of Health Care Policy and Research, Pavilion 3, Mayo Clinic, 200 First Street SW, Rochester, Minnesota 55905, denton.brian@mayo.edu.

The goal of “Practice Abstracts” is to present interesting, topical, and novel applications of operations research methodology to a wide range of industrial applications. “Practice Abstracts” are intended to provide Interfaces readers with short (2–4 page) descriptions of the most relevant aspects of operations research-based projects, in a form that is accessible to academics and practitioners in other organizations. Contributions should be sent for evaluation to the editor of “Practice Abstracts,” Brian T. Denton, Division of Health Care Policy and Research, Pavilion 3, Mayo Clinic, 200 First Street SW, Rochester, Minnesota 55905, denton.brian@mayo.edu.

IBM Systems and Technology Group uses operations research models and methods extensively for solving large-scale supply chain optimization (SCO) problems for planning its extended enterprise semiconductor supply chain. The large-scale nature of these problems necessitates the use of computationally efficient solution methods. However, the complexity of the models makes developing robust solution methods a challenge. We developed a mixed-integer programming (MIP) model and supporting heuristics for optimizing IBM’s semiconductor supply chain. We designed three heuristics, driven by practical applications, for capturing the discrete aspects of the MIP. We leverage the model structure to overcome computational hurdles resulting from the large-scale problem. IBM uses the model and method daily for operational and strategic planning decisions and has saved substantial costs.

The goal of “Practice Abstracts” is to present interesting, topical, and novel applications of operations research methodology to a wide range of industrial applications. “Practice Abstracts” are intended to provide Interfaces readers with short (2–4 page) descriptions of the most relevant aspects of operations research-based projects, in a form that is accessible to academics and practitioners in other organizations. Contributions should be sent for evaluation to the editor of “Practice Abstracts,” Brian T. Denton, Division of Health Care Policy and Research, Pavilion 3, Mayo Clinic, 200 First Street SW, Rochester, Minnesota 55905, denton.brian@mayo.edu.