Presentation on theme: "Capacity Setting and Queuing Theory"— Presentation transcript:
1 Capacity Setting and Queuing Theory BAHC 510Lecture 6US ElectionNov 6, 2012
2 Capacity and Resources A key lever for improving patient flow.How do we measure capacity?What is the capacity of a 20 seat restaurant?A 16 bed ward?Capacity is a RATECustomers/hourPatients/dayWe can view a 16 bed ward as a queuing system with 16 serversWhat is the capacity of a bed?Does this analogy apply to the restaurant?A system is composed of resources with capacities.Often we use the expressions “resource” and “capacity” interchangeably (hopefully without confusion)
3 How Much Capacity is Needed? or How Many Resources are Needed? Surge capacityBase capacity
4 Capacity tradeoffs when demand is variable Too much capacity or too many resources = idlenessNot enough capacity = waitsThe resource manager must trade these off taking into account system objectives and available resourcesShould we set capacity equal to demand?What does this mean?This is called a balanced systemIt works perfectly when there is no variation in the systemIt works terribly when there is variation! Why?Once behind, you never can catch up.Queuing theory quantifies these tradeoffs in terms of performance measures.
5 Queuing Models(Mathematical) queuing models help us set capacity (or determine the number of resources needed) to meet:Service level targetsAverage wait time targetsAverage queue length targetsQueuing models provide a more precise alternative to simulationThey provide insights into how to plan, operate and manage a systemWhere are there queues in the health care system?
6 A single server queuing system BufferServerA queue forms in a bufferServers may be people or physical spaceThe buffer may have a finite or unlimited capacityThe most basic models assume “customers” are of one typeand have common arrival and service rates
7 A multiple (N) server queuing system Buffer.ServerServer
8 Several parallel singer server queues BufferServerBufferServerBufferServer
9 Parallel Queues vs. Multiple server Queues Provide examples of multiple server queues (MSQs)Provided examples of parallel queues (PQs)In what situations would each of these queuing systems be most appropriate? Why?
10 Networks of queuesMost health care systems are interconnected networks of queues and servers with multiple waiting points and heterogeneous customers.Provide some examples.Often we model these complex systems with simulation.But in some cases we can use formulae to get results
11 Queuing Theory background Developed to analyze telephone systems in the 1930’s by Erlang.How many lines are needed to ensure a caller tries to dial and obtains a “line”.Depending on the system configuration an arriving customer may either be blocked or enter a queue and wait for service.Now they are applied to analyze internet traffic, telecommunications systems, call centers, airport security lines, banks and restaurants, rail networks, etc.
12 Queues and Variability There are two components of a queuing system subject to variabilityThe inter-arrival times of “jobs”The service times or LOSWhy are these variable?We describe this variability byMeanStandard deviationProbability distributionUsually the normal distribution doesn’t fit wellOften an exponential distribution fits wellIf we know its rate or mean we know everything about it.
13 The exponential distribution P(T ≤ t) = 1 – e-λtThe quantity λ is the rate.The mean and standard deviation of the exponential distribution is 1/λ.The median is ln(2)/ λ = .693/ λExponential distributions don’t allow negative times and have a small probability of long service times.Example; Patients arrive at rate 4 per hour.The mean inter-arrival time is 15 minutes.The median inter-arrival time is minutes.What is the probability that the time between two arrivals is less than 10 minutes (1/6 of an hour)P( T ≤ 1/6) = 1 – e-4∙(1/6) = 1- e-2/3 = .487.The exponential distribution underlies queuing theory.A queue with exponential service times and exponential inter-arrival times and N (FCFS) servers and an infinite waiting room is called an M/M/N queue.
14 Capacity management and queuing systems Capacity management involves determining the number of servers to use and the size of the waiting rooms.ExamplesHow many long term care beds are needed?How many porters are needed?How many nurses are needed?How many cubicles are needed in an ED?Some healthcare systems have no buffers; all the waiting is done outside of the system or in upstream resources.ALC cases waiting for LTC beds
15 Analyzing a queuing system OutputsCapacity UtilizationWait Time in QueueQueue LengthBlocking ProbabilityService LevelsInputsArrival RateService RateNumber of ServersBuffer SizePopulation SizeQueueAnalyzerArmann Ingolfsson’s Queuing Calculator
16 Some Fundamental Quantities InputsThe arrival rate per hour: λThe service rate per hour: µThe number of servers: sCan be 1 or moreThe buffer size: KCan be finite or infiniteDerived QuantitiesThe offered load: λ/ µ = RExample; λ = 100 calls/hr and µ = 5 calls/ hrThen the offered load is 20 (this quantity is unit less)This means the system needs at least 20 servers to meet its workloadAnother important quantity is the traffic intensity ρ = R/sIt gives the offered load per serverIn example if there are 25 servers (N=25) ; ρ =20/25 =.8So servers should be busy 80% of the time.If the traffic intensity exceeds 1; the system will be unstable.
17 Examples of Queuing Systems Walk-in clinic with 6 seats and 2 physicianss = 2K = 6Long term care facility with 100 bedss= 100K = ?A Finite Capacity Loss SystemModel for an (old-fashion) phone systems serversK= 0When all servers are busy, system is blocked and customers are lostA Congestion SystemK= ∞When all servers are busy; customers wait
18 Performance Measures Capacity Utilization Probability the system is emptyAverage waiting time (in queue) – WqFlow Time - Average Total Time in System – WAverage queue length – LqAverage number of jobs in the system - LProbability that a customer waits for serviceProbability that there are k customers in the systemService Level – Probability that a customer waits less than T time units for service.
19 An Example - M/M/1 QueueAssume exponential inter-arrival time and service time distributions, infinite capacity and 1 server (s=1)Calculations below are based on analytical expressions available in most operations research texts on OR.Customers arrive at rate 4 per hour, mean service time is 10 minutes.Service rate is 6 per hourSystem utilization = Probability the server is occupied = = 2/3.Safety capacity = service rate – arrival rate = 2P(System is empty) = 1- = 1/3.P(k in the system) = k(1- ) = (1/3)(2/3)kAverage Time in system= 1/safety capacity = ½ hourAverage Time in queue = Average time in system – average service time = ½ - 1/6 = 1/3 hourAverage Queue Length = 2/(1- ) = 4/3Suppose arrival rate increases to 5.9 customers per hour.Then =5.9/6 = .9833So P(System is empty) = .0167; Average time in system = 10 hours and Average number of customers in the system = 58.9!
20 About the Waiting Line Analyzer An M/M/s queue is the same as an M/M/1 queue except that there may be more than one server.In this model, there is a single buffer and s servers in the resource pool.Jobs are processed on a FIFO basis.When there are more than s jobs in the system, the buffer is occupied and waiting for service occurs. The Erlang-C formula gives the probability an arriving job has to wait.An M/M/s/K queue is an M/M/c queue with a finite buffer of size K.There are at most K + s customers in the system.When the buffer is filled, the system is blocked and customers are lost.QUEUECALC computes performance measures forM/M/s queuesM/M/s queues with a finite buffer sizeM/M/s queues with a finite population sizeM/G/1 queuesIn addition for a fixed TFor specified s it computes the percentage of jobs waiting less than T time unitsIt computes the number of servers needed to achieve a specified service levelHow many servers are needed so that 90% of jobs wait no more than 10 minutes for service.
21 Problem 1Patients arrive at rate 5/hr. They require on average 1 hour of treatment.What is the offered load?How many service providers do we need to ensure that the average wait time is 20 minutes or less?Assume a large waiting room.Observe that we require more than 5 servers to ensure a stable system.Run “The Waiting Line Analyzer” to findFor 6 service providers - Average number in queue is 2.94 and average wait time in queue is hours or minutesNote that with 6 service providers the probability a customer waits which equals the probability all 6 are occupied occurs 58.75% of time.The capacity utilization is 83%For 7 service providers – Average number in queue is and average wait is hours or 9.28 minutes.Note that with 7 service providers the probability a customer waits which equals the probability all 7 are occupied occurs 32.41% of time.The capacity utilization is 71%Observe the trade-off between capacity utilization and service!
22 More on Problem 1 Service Levels Suppose our target service times are 6 and 10 minutes – fill in the following tableServersP(Wq ≤ 6)P(Wq ≤ 10)Capacity Utilization678910
23 More on Problem 1 Servers P(Wq ≤ 6) P(Wq ≤ 10) Capacity Utilization 6 .47.5083%7.73.7771%8.88.9063%9.95.9656%10.9850%
24 Still more on Problem 1Let’s explore relationship between (traffic intensity) utilization, queue lengths and wait timesAssume 5 servers increase arrival rate to 5.Conclusion – as traffic intensity increases to 1 queue lengths and wait time increase rapidlyArrival RateUtilizationWait time in queue (hrs)Queue Length480%0.552.224.590%1.526.864.998%9.5046.564.9999.8%99.50496.5
25 Problem 2 – A small walk in clinic A walk in clinic has 3 doctors;Average time spent with a patient is 12 minutes (5/hr)Patients arrive at rate of 12 per hourHow many chairs should we have in the waiting room so only 5% of patients are turned away?SolutionAssume first an infinite waiting roomThis shows average queue length is 2.59Now try a model with a finite waiting room.With 3 chairs 9% balk and 52% waitWith 4 chairs 7% balk and 55% waitWith 5 chairs 5% balk and 58% waitIn this last case average waiting time is .038 hoursThis seems too fast.
26 Problem 3 – Blocking in a Hospital Ward Bed requests arrive at the rate of 3 per day.Patients remain in beds for about 5 daysHow many beds are required so that the probability a patient is not admitted on arrival is less than 10%?This is a finite capacity queuing system with no waiting roomService rate = 1/5 = 0.2 patients per dayOffered load = 3/.2 = 15 so we need at least 15 beds.Model this as a finite capacity queuing system with no waiting room – we want the blocking probability to be less than 0.1.With 15 beds 18% are blockedWith 16 beds 14% are blockedWith 17 beds 11% are blockedWith 18 beds 9% are blockedIn this case (s=18) the capacity utilization is 76%Graph gives occupancy distribution or census.This probability is computed using the Erlang-B formula
27 How can queuing theory improve porter scheduling? Assumption: Porters handle 3.3 trips/hour
28 Implications of queuing formulas As the safety capacity vanishes, or equivalently, the traffic intensity increases to 1:waiting time increases without bound!queue lengths become arbitrarily long!In the presence of variability in inter-arrival times and service times, a balanced system will be highly unstable.These formulas enable the manager to derive performance measures on the basis of a few basic descriptors of the queuing systemThe arrival rateThe service rateThe number of serversWhen the system has a finite buffer, the percentage of jobs that are blocked can also be computed
29 SummaryWhen the manager knows the arrival rate and service rate, he/she can compute:The average number of jobs in the queue.The average time spent in the queue.The probability an arriving patient has to wait.The system utilization.This can be done without simulation!This information can be used to set capacity or explore the sensitivity of recommendations to assumptions or changes.Thus queuing theory provides a powerful tool to manage capacity.
30 Don’t Match Capacity with Demand If service rate is close to arrival rate then there will be long wait times.Recall average queue length = 2/(1- )For traffic intensity near 1, queue length will be very small.No variability – All procedures take exactly the same time, patients are scheduled to appear at the completion of the proceeding procedure and arrive at that time.Safety capacity – Service Rate – Arrival Rate
31 Performance measure formulas (M/M/1 queue – no limit on queue size) System Utilization = P(Server is occupied) = If traffic intensity increases, the likelihood the server is occupied increasesThis occurs if the arrival rate increases or the service rate decreasesP(System is empty) = 1- P(k in system) = k(1- )Average Time in System = 1/ Safety capacityAverage Time in Queue = Average time in system – average service timeIf safety capacity decreases; time in queue increases!Average Number of jobs in the system (including being served) = /(1- )Average Queue Length = 2/(1- )If we know safety capacity, service time and traffic intensity, we can compute all system propertiesLittle’s Law holds toonumber in queue = arrival rate x waiting time in queue
32 Idle Capacity And Wait Time Targets Theoretical –based on queueing formula.