Research interests

1. Research interests Viviane Gascon Vietnam 2010

2. Nurse scheduling Viviane Gascon and Éric Gagné

3. Nurse scheduling: home care nurses of a medical clinic • Nurses are assigned to one of the sectors covered by the clinic. • Full time and part time nurses. • Nurses work on weekends every 4 or 5 weeks. • Nurses are on duty every 7 weeks. • Hard constraints : • Nurses work a number of days specified by their category (full time or part time). • A nurse must work in her assigned sector.

4. Nurse scheduling: soft constraints • The demand/surplus of nurses must be spread among the working days. • At least one nurse must work in each sector for every working day of the week. • The maximum number of consecutive working days must not exceed 5 days. • The Holidays must be fairly spread among nurses. • Nurses’ requirements for days off and vacations should be satisfied. • 0-1-0 patterns must be avoided (no working day between two days off).

5. Nurse scheduling: objectives • Minimize deviations for the six soft constraints • Maximize number of nurses working on Mondays and Fridays. Goal programming problem

6. Nurse scheduling: mathematical model • Variables

7. Nurse scheduling: solving method • Each aggregate objective function is given a priority. • Nurses on duty, on duty during a Holiday and nurses’ schedules from the previous planning period are known. • An adapted Tabu search approach was used

8. Routing home care nurses Bouazza Elbenani, Jacques Ferland and Viviane Gascon

9. Routing home care nurses • A public clinic plan visits to patients who need medical treatments at home; • Each patient on the list must be visited on a given day; • The territory covered by the clinic is divided into sectors; • Each sector is assigned to some nurses; • Each nurse is assigned to a sector. • A nurse assigned to a specific sector can visit patients from another sector, if necessary; • The clinic can use nurses from the recall list; • The problem considers constraints of the vehicle routing problem with time windows…. and more.

10. Routing home care nurses problem • each patient is visited once by a nurse • each nurse comes back to the clinic she left • no subtour • routes’ length is limited VRP problem with time windows and multiple vehicles? + List of patients constantly evolving Continuity of care by nurses If blood samples, return to the clinic earlier

11. Home care nurses: specific constraints • Continuity of care: A patient should always be visited by the same nurse. This constraint is modeled by a penalty cost introduced in the objective function when a patient is not visited by his/her regular nurse. • Blood sample constraints: If a blood sample is taken before 10h00 the nurse must go back to the clinic before 10h00 to drop the blood sample; If a blood sample is taken between 10h00 and 11h00 the nurse must go back to the clinic before 11h00 to drop the blood sample.

12. Home care nurses: objectives Main objective: minimize total distance Secondary objectives: • minimize use of nurses from the recall list; • penalize the visit of a patient by another nurse than his/her regular nurse; • penalize the visit of a patient by a nurse from another sector than his/her usual sector. • minimize number of patients not visited Each nurse being assigned to a sector, a problem can be defined for each sector. The global problem takes into account the fact that a nurse can visit patients from another sector than hers.

13. Home care nurses: solving method Tabu search approach Phase 1: Solve the sector problems Phase 2: Solve the global problem

14. Analysis of a blood sample process Viviane Gascon, Mathilde Bélanger and Katie Hébert

15. Problem overview • Hospital with no appointments for blood samples • Two types of patients • with priority (coumadin, hyperglycemia) • standards • High demand : average of 350 patients every day • High waiting time for patients • Technologists are unsatisfied with their work • Patients are unsatisfied with the service

16. Patient’s trajectory

17. Average service times Leading times Waiting times Priority Standards Priority Standards Priority Standards Min 00:02:54 00:04:07 Counter Blood Counter Blood Counter Blood Counter Blood Max 01:47:01 04:04:15 00:01:29 00:03:02 00:02:05 00:03:20 Ave. Min 00:28:43 01:26:34 00:00:00 00:00:16 00:00:00 00:00:23 Max 00:17:07 01:41:36 02:06:42 02:18:44 Ave. 00:02:39 00:21:43 00:38:49 00:41:40 Sciences de la gestion ANALYSE DES DONNÉES ET RÉSULTATS Temps d’attente et de service moyen selon les jours de la semaine

18. Point d’équilibre Arrival of « coumadin » Laboratoire interdisciplinaire de recherche et d’intervention dans les services de santé

19. SIMULATION MODEL With simulation techniques we can model a process and propose scenarios. • Basic model : models the real process from data collected • Three scenarios : the objective is to reduce total waiting time and total leading time Distribution laws for service times

20. Total mean leading time (min) Prio Std Observed time 28,07 85,75 Basic model 28,42 84,69 Difference 1,25% -1,24% BASIC SIMULATION MODEL

21. Prio Std Total mean leading time Minutes % Minutes % Observed time 28 86 Scenario 1 10 -66% 58 -32% Scenario 2 9 -69% 49 -43% Scenario 3 8 -73% 40 -54% SCENARIOS • Scenario 1 : Modify technologists and clerks’ working schedules, all technologists must serve patients with priority, adjust arrival rate of patients • Scenario 2 : Scenario 1 plus reduce time between service for counters and for the blood samples themselves • Scenario 3 : Scenario 2 plus a higher reduction for the times between service, reduce the number of technologists by one and reduce the distance traveled by patients to go to the technologists’ room. Results