04:00 PM - 04:30 PM
A master surgery scheduling problem in a private hospital
We consider the problem of optimizing the operating room schedule of a central and private hospital in Lisbon, Portugal. It performs about 8,000 surgeries per year, 2% of which are from the emergency service. The hospital has 16 surgical specialties and about 200 surgeons operate in its surgical suite. Its surgical suite has eight rooms, one of which is still closed due to the hospital’s workload and capacity. Currently, the surgical suite’s regular work schedule is between 8:00am and 11:00pm, from Monday to Friday but extra time is regularly used. Days are divided in two periods: Morning from 8:00am to 3:00pm, and Afternoon from 3:00pm to 11:00pm. The practice of this hospital is to assign rooms, periods and days to surgical teams and/or specialties. Therefore, a room in a period of a day can be assigned to more than one surgical team, and if that happens they should manage the time between then. We had access to the data of all inpatients and outpatients in the last two years and we were asked to study the current master surgical plan: a simple (handmade) and cyclic block schedule, with few changes from week to week. As this task is becoming even more difficult with the increasing of the workload, we proposed ourselves to find a more suitable master surgical schedule with a MILP model. In our view of the problem, an objective concerns the preferences and availability of the surgical team. As most of the surgeons work in other hospitals, some may like/need to have a fixed scheduled time per week, while others may rather have the opportunity to operate in different periods and days. In addition, we would be happy to take this work to the next stage by solving the associated sequencing problem, where surgeries are assigned to blocks.
04:30 PM - 05:00 PM
Bed leveling of a surgery Department using Variable Neighbourhood Search
This work here presented deals with the problem of Operating room(OR) scheduling over a given planning horizon where a set of elective surgery patients, wait for being admitted to a set of available operating room block times.
The objective is to define a patient assignment able to level the post-surgery ward bed occupancies during the days, allowing a regular and smooth workload in the ward, reserving a certain quantity of beds that will be used by emergent patient (usually represented by the concurrent flow of patients from Emergency Department to hospital wards), and improving the quality of care provided to patients (e.g. reducing elective patient cancellations).
In this model we exploited the flexibility of the Variable Neighbourhood Search, with the development of a solution framework that can be easily adapted to different hospital operative contexts of OR Scheduling.
In order to better validate the framework, preliminary results reported are tested on a set of real based instances of a Surgery Department of a large italian public hospital.
Keywords: Operating room planning, bed levelling, Variable Neighbourhood Search.
05:00 PM - 05:30 PM
Development of a Benchmark Set and Instance Classification System for Surgery Scheduling
Numerous benchmark sets exist for combinatorial optimization problems. However, in healthcare scheduling only a few benchmark sets are known, mainly focused on nurse rostering. One of the most studied topics in the healthcare scheduling literature is surgery scheduling, for which there is no widely used benchmark set. An effective benchmark set should be diverse, reflect the real world, contain large instances, and be extendable. This paper proposes a benchmark set for surgery scheduling algorithms, which satisfies these four criteria.
Surgery scheduling instances are characterized by their underlying case mix. A case mix describes the volume and properties of all relevant surgery types. For any given case mix, unlimited instances can be generated randomly. As each operating room department has its own case mix, a diverse benchmark set should contain instances based on a variety of case mixes. To identify diverse case mixes, we propose a classification based on the surgery type duration and the coefficient of variation. The case mix classification gives insight into the scheduling complexity and expected performance of algorithms. Furthermore, suitable algorithms for hospitals that have a specific case mix can be developed and evaluated using the benchmark set.