4 Presentations

• 
  11:00 AM - 11:30 AM

  Replenishment of an Automated Drug Dispensing System

  • Scott Sampson, presenter, Brigham Young University

  Automation has disrupted manufacturing industries in both good and bad ways. Quality and productivity have increased, but often with a loss of worker jobs. Service industries such as healthcare have been more resistant to automation. However, technologies have recently advanced to such a great degree that auto-mation is becoming a competitive necessity. This study focuses on drug dispens-ing, where automation is quickly assuming many functions. Centralized phar-macies can meet the needs of geographically dispersed customers with very little human labor. One of the greatest potential benefits of automated drug dispens-ing systems is the tremendous amount of data that they collect through normal operation. We consider a case study of centralized drug distribution that feeds inventory to 170 automated dispensing cabinets (ADCs). We discuss some of the ways in which data from the ADCs might be used to optimize the replenishment policies.

• 
  11:30 AM - 12:00 PM

  Evaluating replenishment systems for disposable supplies at the operating theater: a simulation case study

  • Karen Moons, presenter, KU Leuven
  • Geert Waeyenbergh, KU Leuven
  • Paul Timmermans, UZ Leuven
  • Dirk De Ridder, UZ Leuven
  • Liliane Pintelon, KU Leuven

  Ensuring cost containment while providing high quality patient care is of para-mount concern to hospitals. The operating theater in particular is a major cost driver for any hospital, and is among the most critical resources in terms of both capacity and patient care. This research investigates effective inventory management and distribution systems as a first step towards realizing efficiency improvements in the internal operating theater supply chain. Discrete-Event Simulation is used to model part of the internal distribution process in the operating theater at the University Hospital in Leuven (Belgium) and to identify improvements by focusing on the replenishment process. A logistics performance measurement framework based on Analytic Network Process, as a popular Multi-Criteria Decision-Making (MCDM) technique, is adopted to assess three replenishment scenarios. The best performing scenario is selected by calculating the Internal Logistics Efficiency Performance (ILEP) index as an evaluation basis. This research indicates that industrial engineering techniques, such as simulation and MCDM, which are successfully applied in other industrial sectors, can also be adopted to realize efficiency opportunities in healthcare logistics.

• 
  12:00 PM - 12:30 PM

  Simultaneous optimization of appointment grid and technologist scheduling in a radiology center

  • Dina Ben Tayeb, presenter, Polytechnique Montréal
  • Nadia Lahrichi, Polytechnique Montréal
  • Louis-Martin Rousseau, Polytechnique Montréal

  The objective of this paper is to simultaneously optimize the appointment grid and the technologist scheduling by considering the demand. We develop an integer programming model by integrating the constraints of appointments and technologist schedules. We evaluate the optimization model using a real case of the Magnetic Resonance Imaging in the CHUM radiology department. The proposed approach provides a decision tool for outpatient centers, and improves resource utilization as well as patient access to the service.

• 
  12:30 PM - 01:00 PM

  Uncertainty in the Blood Donation Appointment Scheduling: Key Factors and Research Perspectives

  • Semih Yalçındağ, Yeditepe University
  • Ettore Lanzarone, presenter, CNR-IMATI

  We consider the management of a blood collection center, which includes the features of both a production system and a service provider. In particular, we analyze the scheduling of donors and the related appointment system, addressing the so-called Blood Donation Appointment Scheduling (BDAS) problem. From the production system viewpoint, the requirement is to balance the production of blood units between days, to meet the requirement of a constant supply of blood units from hospitals and transfusion centers; from the service provider perspective,
  appointments allow to reduce waiting times and improve the quality perceived by donors. Thus, the goals of the BDAS are to guarantee a quite constant production of blood units and to reduce physicians’ overtimes while including appointments
  and free slots for donors without a reservation. A framework for the BDAS problem has been recently proposed, in which slots are first preallocated to the different blood types and then assigned to the donors when they call to make a reservation. However, this framework refers to a deterministic layout in which all input parameters are assumed to be known in advance. On the contrary, the BDAS problem is stochastic in nature and includes stochastic parameters that must be predicted from historical data. In this paper, we analyze the uncertainty sources to determine the
  most critical ones. Then, we propose research directions to properly include uncertainty in the BDAS framework, considering both stochastic programming and robust optimization methodologies.