Last update: 9 November 2018

Problem description

The patient admission scheduling problem under uncertainty (PASU) has been formally introduced by Ceschia and Schaerf – Ceschia, S., Schaerf, A.. Modeling and Solving the Dynamic Patient Admission Scheduling Problem under Uncertainty. Artificial Intelligence in Medicine, 56(3): 199-205, 2012.

The problem consists in defining for each patient an admission date within a range of possible days and assigning him/her to the most suitable bed/room/department.

Each patient has a registration date, an earliest admission date, a latest admission date and a length of stay. Each room has a defined capacity, a level of expertise (high, medium, null) in treating each specialty, a set of available equipment, that could be mandatory or preferred for patients. There are three room gender policies: the restricted gender policy (RGP) means that only male or female patients can be assigned in a given room; instead, the dependent gender policy (DGP) states that only patients with the same gender of the other patients staying in the room can be assigned. Uncertainty concerns length of stay. In fact, some patients have an overstay risk, i.e. their stay could be extended. Some rooms have an age policy (e.g., paediatric or geriatric rooms).

The best patient-to-bed assignment, which consists in matching patients' characteristics and room characteristics as much as possible, is hard to determine manually. Then, efficient quantitative approaches have to be proposed.  

Benchmark instances

Benchmarch instances are available by following this link . 

Results on the benchmark instances

The following table summarizes the main features of the benchmark instances. The last two columns report our results. The bed assignment is evaluated with the default penalty values defined by Ceschia & Schaerf (2012). 

Table Keys:FI= Family of instances, De= number of departments, R= number of rooms, F= number of features, P= number of patients, S= number of specialities, D= planning horizon (in days).
FI De R F P S D Results Results
Small short 4 4 50 3 14 2616.12 SS1  2616.34 SS2
Small mid 100 3 28  5907.34 SM1 5901.76 SM2
Small long 200 3 56  11887.92 SL1 11912.78 SL2
Med short 6 40  250 10 14  11740.78  MS1 11855.12 MS2
Med mid 40  500 10 28  25960.66 MM1 26000.22 MM2
Med long 6 40 5 1000 10 56 // // 57301.91 ML2
Large short 8 160 6 1000 15 14 // // 34157.90  LS2

 

Publication (Omega: The International Journal of Management Science - Under Review)

Efficient matheuristics based on large neighborhood search algorithm for offline patient admission planning and room assignment problems. Rosita Guido, Vittorio Solina, Domenico Conforti.