Bài giảng Operations Management - Chapter 18: Management of Waiting Lines

Management ofWaiting LinesMcGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.You should be able to:What imbalance does the existence of a waiting line reveal?What causes waiting lines to form, and why is it impossible to eliminate them completely?What metrics are used to help managers analyze waiting lines?What are some psychological approaches to managing lines, and why might a manager want to use them?What very important lesson does the constant service time model provide for managers?18-*Student SlidesQueuing theoryMathematical approach to the analysis of waiting linesApplicable to many environmentsCall centersBanksPost officesRestaurantsTheme parksTelecommunications systemsTraffic management18-*Student SlidesWaiting lines tend to form even when a system is not fully loadedVariabilityArrival and service rates are variableServices cannot be completed ahead of time and stored for later useStudent Slides18-*Why waiting lines cause concern:The cost to provide waiting spaceA possible loss of business when customers leave the line before being served or refuse to wait at allA possible loss of goodwillA possible reduction in customer satisfactionResulting congestion may disrupt other business operations and/or customers18-*Student SlidesThe basic characteristics of waiting linesPopulation sourceNumber of servers (channels)Arrival and service patternsQueue disciplineStudent Slides18-*Calling populationArrivalsWaitinglineExitServiceSystemProcessing OrderStudent Slides18-*Infinite sourceCustomer arrivals are unrestrictedThe number of potential customers greatly exceeds system capacityFinite sourceThe number of potential customers is limitedStudent Slides18-*ChannelA server in a service systemIt is assumed that each channel can handle one customer at a timePhasesThe number of steps in a queuing systemStudent Slides18-*Arrival patternMost commonly used models assume the arrival rate can be described by the Poisson distributionArrivals per unit of timeEquivalently, interarrival times are assumed to follow the negative exponential distributionThe time between arrivalsService patternService times are frequently assumed to follow a negative exponential distribution18-*Student SlidesManagers typically consider five measures when evaluating waiting line performance:The average number of customers waiting (in line or in the system)The average time customers wait (in line or in the system)System utilizationThe implied cost of a given level of capacity and its related waiting lineThe probability that an arrival will have to wait for service18-*Student SlidesFour basic infinite source modelsAll assume a Poisson arrival rateSingle server, exponential service timeSingle server, constant service timeMultiple servers, exponential service timeMultiple priority service, exponential service time18-*Student SlidesAppropriate for cases in which the calling population is limited to a relatively small number of potential callsArrival rates are required to be PoissonUnlike the infinite-source models, the arrival rate is affected by the length of the waiting lineThe arrival rate of customers decreases as the length of the line increases because there is a decreasing proportion of the population left to generate calls for serviceService rates are required to be exponential18-*Student SlidesIf those waiting in line have nothing else to occupy their thoughts, they often tend to focus on the fact they are waiting in lineThey will usually perceive the waiting time to be longer than the actual waiting timeSteps can be taken to make waiting more acceptable to customersOccupy them while they waitIn-flight snackHave them fill out forms while they waitMake the waiting environment more comfortableProvide customers information concerning their wait18-*Student SlidesManagers must carefully weigh the costs and benefits of service system capacity alternativesOptions for reducing wait times:Work to increase processing rates, instead of increasing the number of servers Use new processing equipment and/or methodsReduce processing time variability through standardizationShift demandStudent Slides18-*