Chapitre 4 Files d’attente pour la planification des capacités

1. Chapitre 4Files d’attente pour la planification des capacités

2. Système M/M/1 RESULTATS: Probabilité stationnaire ou distribution: pn = rn(1-r), n≥0 Où r = l/m est appelé le taux de trafic. Ls = nombre moyen de clients dans le système = l/(m-l) Ws = temps moyen passé dans le système = 1/(m-l) Lq = longueur moyenne de file d'attente = l2/(m-l)m Wq = temps d'attente moyen = l/(m-l)m r = taux d'utilisation du serveur = l/m p0 = Taux d'oisiveté du serveur = 1 - l/m P(n > k) = probabilité d'avoir plus de k clients = (l/m)k+1

3. Système M/M/c – Modèle Erlang C Un système M/M/c est une file d'attente : • composé de c serveurs identiques • dont les arrivées forment un processus de POISSON • la durée de service suit une distribution exponentielle. Le processus N(t), le nombre de clients présents dans le système à la date t, est un processus de naissance et de mort. • Taux de l'événement "arrivée" : l. • Taux de "fin de service" : N(t)m si N(t)  c et cm si N(t) > c. Condition de stabilité: l< c m.

4. Système M/M/c – Modèle Erlang C Probabilité stationnaire ou distribution: a= l/m : offered load • = l/cm : traffic intensity pn = an/n! p0, " 0 < n  c

5. Système M/M/c – Modèle Erlang C Ls = nombre moyen de clients présents dans le système = Lq + a Ws = temps moyen passé dans le système des clients = Wq + 1/m Lq = longueur moyenne de file d'attente = Wq = temps d'attente moyen = Lq / l a = nombre de serveurs occupés, a = a

6. Système M/M/c – Modèle Erlang C C(c,a) = probabilité de délai de prise en charge d’un client = pc + pc+1 + ... wq = random waiting time of a customer T = Waiting time target g(T) = Service level = P(wq ≤ T)

7. M/M/c avec clients impatients –Erlang B • Le système est similaire au système M/M/c à l’exception des clients perdus arrivés lorsque tous les serveurs sont occupés.

8. M/M/c avec clients impatients –Erlang B Probabilité stationnaire ou distribution: a= l/m : offered load • = l/cm : traffic intensity pn = an/n! p0, " 0 < n  c

9. M/M/c avec clients impatients –Erlang B Erlag loss function or Erlang B formula = Pourcentage des clients perdus ou overflow probability Charge réelle des serveurs

10. M/M/c avec clients impatients –Erlang B Normal approximation for staffing Erlang Loss systems Condition: high offered load (a > 4) and high targeted service level N(t) = number of patients : approximately normally distributed E[N(t)]  a In M/M/∞ system, N(t) =d POISSON(a), i.e. E[N(t)] = a, Var[N(t)] = a Square-Root-Staffing-Formula for a delay probability a Where F is the cdf of the standard normal distribution

11. Computation issues of Erlang B and C formula

12. Approche file d’attente - Staffing the number of nurses A hospital is exploring the level of staffing needed for a booth in the local mall, where they would test and provide information on the diabetes. Previous experience has shown that, on average, every 6.67 minutes a new person approaches the booth. A nurse can complete testing and answering questions, on average, in twelve minutes. Assuming s = 2, 3, 4 nurses, a hourly cost of 40€ per nurse and a customer waiting cost of 75€ per hour in the system. Determine the following: patient arrival rate, service rate, overall system utilisation, nb of patients in the system (Ls), the average queue length (Lq), average time spent in the system (Ws), average waiting time (Wq), probability of no patient, probability of waiting, total system costs.

13. Approche file d’attente - Staffing the number of nurses

14. Approche file d’attente - number of beds Target occupancy level Consider obsterics units in hospitals. Obsterics is generally operated independently of other services, so its capacity needs can be determined without regard to other services. It is also one for which the use of a standard M/M/s queueing model is quite good. Most obsterics patients are unscheduled and the assumption of Poisson arrivals has been shown to be a ggod one in studies of unscheduled hospital admissions. In addition, the coefficient of variation (CV) of the length of stay (LOS), which is defined as the ratio of the standard deviation to the mean, is typically very close to 1 satisfying the service time assumption of the M/M/s model.

15. Approche file d’attente - number of beds Since obsterics patients are considered emergent, the American College of Obsterics and Gynecology (ACOG) recommends that occupancy levels of obsterics units not exceeding 75%. Many hospitals have obsterics units operating below this level. However, some have eliminated beds to reduce « excess » capacity and costs and 20% of NY hospitals had obsterics units that would be considered over-utilized by this standard. Assuming the target occupancy level of 75%, what is the probability of delay for lack of beds for a hospital with s = 10, 20, 40, 60, 80, 100, 150, 200 beds. Lesson : For the sameoccupancylevel, the probability of delaydecreaseswith the size of the service.

16. Approche file d’attente - number of beds Evaluation of capacity based on a delay target leads to very important conclusion. Though there is no standard delay target, it has been suggested that the probability of delay for an obsterics bed should not exceed 1%. What is the size of an obsterics unit (nb of beds) necessary to achieve a probability of delay not exceeding 1% while keeping the target occupancy level of 60%, 70%, 75%, 80%, 85%? Lesson : Achieving high occupancy level while having small probability of delay is only possible for obsterics unit of large hospitals. Capacity cut should be made with clear understanding of the impact. Simple and naive analysis based on average could lead to bad decisions.

17. Approche file d’attente - number of beds Impact of seasonality Consider an obsterics unit with 56 beds which experiences a significant degree of seasonality with occupancy level varying from a low of 68% in January to about 88% in July. What is the probability of delay in January and in July? If, as is likely, there are several days when actual arrivals exceed the month average by 10%, what is the probability of delay for these days in July? Lesson : Capacity planning should not be based only on the yearly average. Extra bed capacity should be planned for predictable demand increase during peak times.

18. Approche file d’attente - number of beds Impact of clinical organisation Consider the possiblity of combining cardiac and thoracic surgery patients as thoracic patients are relatively few and require similar nursing skills as cardiac patients. The average arrival rate of cardiac patients is 1,91 bed requests per day and that of thoracic patients is 0,42. No additional information is available on the arrival pattern and we assume Poisson arrivals. The average LOS (Length Of Stay) is 7,7 days for cardiac patients and 3,8 days for thoracic patients. What is the number of beds for cardiac patients and thoracic patients in order to have average patient waiting time for a bed E(D) not exceeding 0,5, 1, 2, 3 days? What is the number of beds if all patients are treated in the same nursing unit? Delay in this case measures the time a patient coming out of surgery spends waiting in a recovery unit or ICU until a bed in the nursing unit is available. Long delays cause backups in operating rooms/emergency rooms, surgery cancellation and ambulance diversion.

19. Approche file d’attente - number of beds Lesson : Personal and equipment flexibility and service pooling can achieve higher occupancy level and reduction of beds. However, priority given to one patient group could significantly degrade the waiting time of other patients if all treated in the same nursing unit.

20. Staffing ED with Erlang loss systems Goal: meeting lossprobabilitytarget for stationaryarrival rate or dynamicarrival rate

21. Staffing ED with Erlang C systems Goal: meeting waiting time target for stationaryarrival rate or dynamicarrival rate