# Chapter C Waiting Lines

Save this PDF as:

Size: px
Start display at page:

## Transcription

1 Supplement C Waiting Lines Chapter C Waiting Lines TRUE/FALSE 1. Waiting lines cannot develop if the time to process a customer is constant. Answer: False Reference: Why Waiting Lines Form Keywords: waiting, line, customers 2. The four elements common to all waiting-line situations are a customer population, a waiting line of customers, the service facility, and a priority rule. Answer: True Keywords: priority, rule, customer, line, population 3. A phase represents a single step in providing a service. Answer: True Keywords: phase, service 4. A bank that dedicates one window for commercial account customers and one window for personal account channel has two channels. Answer: True Keywords: queue, channel 5. If the service system generates customers according to a Poisson distribution, the exponential distribution describes the probability that the next customer will arrive in the next T time periods. Answer: True Reference: Probability Distributions Keywords: Poisson, exponential, distribution 180

2 6. The mean of the Poisson distribution is equal to its standard deviation. Answer: False Reference: Probability Distributions Keywords: Poisson, distribution, mean, variance 7. Short queue lengths typically mean not enough capacity. Answer: False Keywords: queue, capacity, length 8. The number of customers in queue and being served also relates to service efficiency and capacity. Answer: True Keywords: customer, queue, capacity, efficiency 9. Long lines always mean long waiting times. Answer: False Keywords: line, waiting, time 10. It is impossible for management to affect the rate of customer arrivals. Answer: False Reference: Decision Areas for Management Keywords: arrival, rate, affect 11. Management, servers, and customers would all be happy if, in a single-server situation, the parameter µ is much greater than λ. Answer: True Keywords: single, server, arrival, service, rate 181

3 MULTIPLE CHOICE 12. Which of the following is LEAST likely to benefit from waiting line analysis? a. Capacity planning b. Inventory management c. Budget planning d. Scheduling Reference: Uses of Waiting-Line Theory Keywords: single, server, service, rate 13. The best example of a finite customer population is: a. the car-buying public of an automotive manufacturer. b. the constituents in a precinct lining up to vote. c. the messages arriving at a major ISP mail server. d. the members of the Management department at your university waiting to speak to the Dean about their department chairman. Keywords: customer, population, finite 14. The distinction between an infinite customer population and a finite customer population is: a. whether the potential number of customers is appreciably affected by the number of customers already in the system. b. whether the number of potential customers exceeds the square of the number of servers. c. whether the number of potential customers exceeds the number of servers raised to the power of the number of channels. d. if the number of customers exceeds infinity. Keywords: customer, population, finite 15. Ed Deadbeat races to the Bursar s Office on the first day of class and notes that the line is four students long. Ed figures that the wait will be at least ten minutes and, having better uses of his time, he decides to proceed to the next item on his to-do list. Ed s behavior is best described as: a. reneging. b. balking. c. blocking. d. queuing. Keywords: balking, balk 182

4 16. India Sisson wants to grab a latte before heading to her marketing class, knowing that the jolt of a double tall mocha is the only thing that can possibly keep her eyes open during today s presentation on the four P s. The barista is slower than molasses in January and India notes that the pace of the line won t permit her to grab her favorite seat in the back row of her class. She decides to risk marketing without a latte and leaves the line before getting served. India s behavior is best described as: a. balking. b. blocking. c. reneging. d. queuing. Keywords: reneging, renege 17. The single, multiple, and finite queuing models all assume that: a. the arrival rate exceeds the service rate.. b. the number of servers exceeds the number of customers. c. the number of customers exceeds the number of servers. d. the customers are patient. Keywords: reneging, renege, balking, balk, patient 18. An automatic, drive-through car wash is an example of a: a. single-channel, single-phase arrangement. b. single-channel, multiple-phase arrangement. c. multiple-channel, single-phase arrangement. d. multiple-channel, multiple-phase arrangement. Keywords: single, channel, phase 19. A drive-through system at a fast food restaurant where the first facility takes the order, the second takes the money, and the third provides the food is an example of: a. single-channel, single-phase arrangement. b. single-channel, multiple-phase arrangement. c. multiple-channel, single-phase arrangement. d. multiple-channel, multiple-phase arrangement. Keywords: single, channel, multiple, phase 183

5 20. A bank lobby with six teller windows, each with a separate line, is an example of a: a. single-channel, single-phase arrangement. b. single-channel, multiple-phase arrangement. c. multiple-channel, single-phase arrangement. d. multiple-channel, multiple-phase arrangement. Keywords: multiple, channel, single, phase 21. A Laundromat where there are washing machines and dryers is an example of a: a. single-channel, single-phase arrangement. b. single-channel, multiple-phase arrangement. c. multiple-channel, single-phase arrangement. d. multiple-channel, multiple-phase arrangement. Keywords: multiple, channel, phase 22. A super computer-accessory discount store often has customers who leave the checkout line before being served because of excessive waiting times. The store has a(n): a. infinite customer population with balking customers. b. infinite customer population with reneging customers. c. finite customer population with balking customers. d. finite customer population with reneging customers. Keywords: infinite, customer, balk 23. A homemade-ice cream shop owner has noticed that, often, potential customers will stop outside the store, assess the wait in line, and then pass by. The shop has a(n): a. infinite customer population with balking customers. b. infinite customer population with reneging customers. c. finite customer population with balking customers. d. finite customer population with reneging customers. Keywords: customer, balk, infinite, population 184

6 24. The owner of a desktop publishing company has seven loyal clients who periodically require his services. The owner has: a. an infinite customer population of patient customers. b. an infinite population of impatient customers. c. a finite customer population. d. a finite customer population with balking customers. Keywords: finite, customer, population 25. Customers arrive according to a Poisson distribution. The average number of customer arrivals per hour is four. The probability that three customers will arrive in the next two hours is: a. less than or equal to b. greater than but less than or equal to c. greater than but less than or equal to d. greater than Reference: Probability Distributions Keywords: probability, customer, arrival 26. Customers arrive according to a Poisson distribution. The average number of customer arrivals per hour is six. The probability that four customers will arrive in the next three hours is: a. less than or equal to b. greater than 0.01 but less than or equal to c. greater than 0.02 but less than or equal to d. greater than Reference: Probability Distributions Keywords: arrival, probability 27. Customers arrive according to a Poisson distribution. The average number of customer arrivals per hour is three. The probability that four customers will arrive in the next two hours is: a. less than or equal to b. greater than 0.10 but less than or equal to c. greater than 0.12 but less than or equal to d. greater than Reference: Probability Distributions Keywords: arrival, probability 185

7 28. Customers arrive according to a Poisson distribution. The average number of customer arrivals per hour is two. The probability that five customers will arrive in the next three hours is: a. less than or equal to b. greater than 0.10 but less than or equal to c. greater than 0.12 but less than or equal to d. greater than Reference: Probability Distributions Keywords: probability, arrival 29. Customers are serviced at a rate of four customers per hour according to an exponential distribution. What is the probability that customer service will require fewer than 30 minutes? a. Less than or equal to 0.50 b. Greater than 0.50 but less than or equal to 0.60 c. Greater than 0.60 but less than or equal to 0.70 d. Greater than 0.70 Reference: Probability Distributions Keywords: service, rate, probability 30. Customers are serviced at a rate of six customers per hour according to an exponential distribution. What is the probability that customer service will require fewer than 20 minutes? a. Less than or equal to 0.70 b. Greater than 0.70 but less than or equal to 0.80 c. Greater than 0.80 but less than or equal to 0.90 d. Greater than 0.90 Reference: Probability Distributions Keywords: service, rate, probability 31. Customers are serviced at a rate of three customers per hour according to an exponential distribution. What is the probability that customer service will require fewer than 10 minutes? a. Less than or equal to 0.40 b. Greater than 0.40 but less than or equal to 0.45 c. Greater than 0.45 but less than or equal to 0.50 d. Greater than 0.50 Reference: Probability Distributions Keywords: customer, service, rate, probability 186

8 32. Customers are serviced at a rate of five customers per hour according to an exponential distribution. What is the probability that customer service will require fewer than 20 minutes? a. Less than or equal to 0.75 b. Greater than 0.75 but less than or equal to 0.80 c. Greater than 0.80 but less than or equal to 0.85 d. Greater than 0.85 Reference: Probability Distributions Keywords: customer, service, rate, probability 33. Customers are serviced at a rate of 10 customers per hour according to an exponential distribution. What is the probability that customer service will require fewer than two minutes? a. Less than or equal to 0.25 b. Greater than 0.25 but less than or equal to 0.30 c. Greater than 0.30 but less than or equal to 0.35 d. Greater than 0.35 Reference: Probability Distributions Keywords: customer, service, rate, probability 34. With a single-server model, increasing the service rate while holding all other factors constant will: a. increase the utilization of the server. b. increase the time spent per customer. c. decrease the probability that there are two customers in the system at any time. d. decrease the arrival rate of customers. Keywords: single, server, service, rate 35. With a single-server model, increasing the promotions for a service through advertising will most likely: a. increase the utilization of the server. b. decrease the average number of customers in the service system. c. decrease the average time a customer spends in the system. d. increase the probability that the server will be idle. Difficulty: Hard Keywords: single, server, utilization, arrival, rate 187

9 36. With a single-server model, increasing the capital-to-labor ratio will most likely: a. increase the utilization of the server. b. have no effect on the operating characteristics because they are affected only by work-methods changes. c. decrease the probability that there are zero customers in the system at any time. d. decrease the average number of customers in the waiting line. Reference: Multiple sections Difficulty: Hard Keywords: single, server, capital, labor, length 37. With a single-server model, increasing the arrival rate by 10 percent and also increasing the service rate by 10 percent will result in: a. an increase in the utilization of the server. b. an increase in the average number of customers in the system. c. a decrease in the average time spent in the system, including service. d. an increase in the waiting-line time. Difficulty: Hard Keywords: single, server, arrival, service, rate 38. With a single-server model, increasing the arrival rate by 10 percent and also increasing the service rate by 10 percent will result in: a. a decrease in the utilization of the server. b. no change in the average number of customers in the service system. c. an increase in the average number of customers in the waiting line. d. an increase in the waiting time in line. Difficulty: Hard Keywords: single, server, arrival, service, rate 39. With a single-server model, increasing the arrival rate by 10 percent and also increasing the service rate by 10 percent will result in: a. no change in the probability that there are n customers in the system. b. a decrease in the average waiting time in line. c. an increase in the average time spent in the system, including service. d. an increase in the average number of customers in the system. Difficulty: Hard Keywords: single, server, arrival, service, rate 188

10 40. With a multiple-server model, increasing the arrival rate by 10 percent and also increasing the service rate of each server by 10 percent will result in: a. a decrease in the utilization of the system. b. no change in the average number of customers in the waiting line. c. a decrease in the average number of customers in the waiting line. d. an increase in the waiting time in line. Difficulty: Hard Keywords: multiple, server, arrival, service, rate, queue, length 41. With a finite-source model, increasing the arrival rate by 10 percent and also increasing the service rate by 10 percent will result in: a. an increase in the utilization of the server. b. an increase in the average number of customers in the service system. c. a decrease in the average time spent in the system, including service. d. an increase in the waiting time in line. Difficulty: Hard Keywords: finite, source, arrival, rate, service 42. With a finite-source model, increasing the arrival rate by 10 percent and also increasing the service rate by 10 percent will result in: a. a decrease in the utilization of the server. b. no change in the average number of customers in the system. c. an increase in the average number of customers in the waiting line. d. an increase in the waiting time in line. Difficulty: Hard Keywords: finite, source, arrival, rate, service 43. In the single-server model: a. customers are assumed to arrive at constant intervals of time. b. the variability of customer arrivals is most often described by a Poisson distribution. c. the mean of the distribution of customer arrivals must be greater than the variance of customer arrivals to get meaningful results. d. the probability of n arrivals in T time periods comes from a normal distribution. Keywords: Poisson, single, server, arrival 189

11 44. In the single-server model: a. the service time of a customer is most often described by an exponential distribution. b. the service time depends on the number of customers in the system as long as there is at least one customer in the waiting line. c. the mean of the service-time distribution must be as great as the target service time for a feasible solution. d. service times are always constant to avoid large waiting lines. Keywords: single, server, exponential, distribution 45. In order to have equivalent performance on average waiting time in a single server model, an increase in interarrival time must be accompanied by: a. an increase in µ. b. an increase in the number of servers. c. an increase in the number of channels. d. an increase in the line length. Reference: Using Waiting Line Models to Analyze Operations Keywords: phase Scenario C.1 A single ticket taker can tear tickets and direct movie patrons to their seats at a rate of 90 per hour. Customers arrive every minute for assistance and always wait, regardless of how long the line gets. Arrivals are governed by the Poisson distribution and service is governed by the exponential distribution. 46. Use the information in Scenario C.1. What is the utilization of the ticket taker? a b c d Keyword: utilization 47. Use the information in Scenario C.1. What is the probability that customers with tickets arrive and the ticket taker is not helping another patron? a b c d Keywords: probability, system, empty 190

12 48. Use the information in Scenario C.1. What is the average number of customers in line? a b c d Keywords: customers, line, queue, length 49. Use the information in Scenario C.1. What is the average number of people waiting in line and being seated? a b c d Keywords: waiting, served, queue, system 50. Use the information in Scenario C.1. What is the average time a customer must wait in line? a minutes b minutes c minutes d minutes Keywords: queue, time, waiting 51. Use the information in Scenario C.1. What is the average combined time a customer waits in line and spends being seated by the ticket taker? a minute b minutes c minutes d minutes Keywords: time, system Scenario C.2 Weary travelers arrive at Will Rogers International Airport, pick up their luggage, stumble to their cars, and proceed to the parking lot attendant to pay for their parking. Traveler interarrival times are exponentially distributed, as are the service times of the attendant. On average, travelers arrive every 25 seconds. The attendant can process three travelers per minute. 191

13 52. Use the information in Scenario C.2. How many minutes per hour is the attendant not serving customers? a. Fewer than or equal to 13 b. Greater than 13 but fewer than or equal to 17 c. Greater than 17 but fewer than or equal to 21 d. Greater than 21 Keyword: utilization 53. Use the information in Scenario C.2. What is the probability that a traveler will pull up to the attendant s service window without having to wait for another customer? a. Less than or equal to 0.15 b. Greater than 0.15 but less than or equal to 0.25 c. Greater than 0.25 but less than or equal to 0.35 d. Greater than 0.35 Keywords: system, empty 54. Use the information in Scenario C.2. What is the average number of customers in line? a. Fewer than or equal to 1.0 b. Greater than 1.0 but fewer than or equal to 2.0 c. Greater than 2.0 but fewer than or equal to 3.0 d. Greater than 3.0 Keywords: line, queue, length 55. Use the information in Scenario C.2. What is the average number of customers in the system? a. Fewer than or equal to 3.0. b. Greater than 3.0 but fewer than or equal to 5.0. c. Greater than 5.0 but fewer than or equal to 7.0. d. Greater than 7.0. Keywords: customer, system 56. Use the information in Scenario C.2. What is the average time a customer spends in line? a. Less than or equal to 1.0 minute. b. Greater than 1.0 but fewer than or equal to 2.0 minutes. c. Greater than 2.0 but fewer than or equal to 3.0 minutes. d. Greater than 3.0 minutes. Keywords: time, queue, line, customer 192

14 57. Use the information in Scenario C.2. What is the average amount of time a customer spends waiting in line and being served? a. Less than or equal to 1.0 minutes b. Greater than 1.0 minutes but fewer than or equal to 1.50 minutes c. Greater than 1.5 minutes but fewer than or equal to 2.0 minutes d. Greater than 2.0 minutes Keywords: customer, time, system Scenario C.3 Customers arrive at the one remaining full-service gas station in the country at the rate of 45 per minute and are served by the first available of three pump jockeys who can dole out gas and check oil at the rate of 20 customers per minute. Both service and interarrival times are governed by the exponential distribution. The probability that no pump jockey is busy is Use the information in Scenario C.3. What is the utilization of the pump jockeys? a. Less than or equal to 60 percent b. Greater than 60 percent but less than or equal to 70 percent c. Greater than 70 percent but less than or equal to 80 percent d. Greater than 80 percent Keyword: utilization 59. Use the information in Scenario C.3. What is the probability that an arrival at the gas station must wait? a. Less than or equal to 0.50 b. Greater than 0.50 but less than or equal to 0.60 c. Greater than 0.60 but less than or equal to 0.70 d. Greater than 0.70 Keywords: probability, wait 60. Use the information in Scenario C.3. What is the average number of customers in line? a. Fewer than or equal to 2.0 b. Greater than 2.0 but fewer than or equal to 3.0 c. Greater than 3.0 but fewer than or equal to 4.0 d. Greater than 4.0 Keywords: number, customers, line, queue 193

15 61. Use the information in Scenario C.3. What is the average number of customers in the system? a. Fewer than or equal to 3.0 b. Greater than 3.0 but fewer than or equal to 3.5 c. Greater than 3.5 but fewer than or equal to 4.0 d. Greater than 4.0 Keywords: customer, system 62. Use the information in Scenario C.3. What is the average time a customer spends in the system? a. Fewer than or equal to five seconds b. Greater than five seconds but fewer than or equal to six seconds c. Greater than six seconds but fewer than or equal to seven seconds d. Greater than seven seconds Keywords: system, time Scenario C.4 Customers arrive at a ticket counter at the rate of 50 customers per hour, according to a Poisson distribution. There are three ticket agents. Customers select the first available agent from one line. Each agent can process 20 customers per hour with exponential service times. 63. Use the information in Scenario C.4. What is the average utilization of the three-agent system? a. Less than or equal to 80 percent b. Greater than 80 percent but less than or equal to 85 percent c. Greater than 85 percent but less than or equal to 90 percent d. Greater than 90 percent Keywords: utilization, system, server 64. Use the information in Scenario C.4. What is the average number of customers waiting in line for service? a. Fewer than or equal to four b. Greater than four but fewer than or equal to 4.5 c. Greater than 4.5 but fewer than or equal to five d. Greater than five Keywords: line, queue, length 194

16 65. Use the information in Scenario C.4. What is the average waiting time in line? a. Fewer than or equal to 3.5 minutes b. Greater than 3.5 minutes but fewer than or equal to 4.5 minutes c. Greater than 4.5 minutes but fewer than or equal to 5.5 minutes d. Greater than 5.5 minutes Keywords: wait, queue, line 66. Use the information in Scenario C.4. What is the average time spent in the system? a. Fewer than or equal to 6.5 minutes b. Greater than 6.5 minutes but fewer than or equal to 7.5 minutes c. Greater than 7.5 minutes but fewer than or equal to 8.5 minutes d. Greater than 8.5 minutes Keywords: time, system Scenario C.5 A trucking firm has five trucks that each requires service at an average rate of once every 50 hours, according to an exponential distribution. The firm has a mechanic who needs five hours to complete the average job with exponential service times. 67. Use the information in Scenario C.5. What is the probability that there will be no trucks in the system? a. Less than or equal to 0.50 b. Greater than 0.50 but less than or equal to 0.60 c. Greater than 0.60 but less than or equal to 0.70 d. Greater than 0.70 Keywords: probability, system, customer 68. Use the information in Scenario C.5. What is the average utilization of the mechanic? a. Less than or equal to 50 percent b. Greater than 50 percent but less than or equal to 60 percent c. Greater than 60 percent but less than or equal to 70 percent d. Greater than 70 percent Keyword: utilization 195

17 69. Use the information in Scenario C.5. What is the average number of trucks waiting for service? a. Fewer than or equal to 0.10 b. Greater than 0.10 but fewer than or equal to 0.14 c. Greater than 0.14 but fewer than or equal to 0.18 d. Greater than 0.18 Keywords: queue, line, length 70. Use the information in Scenario C.5. What is the average number of trucks in line waiting and being serviced? a. Fewer than or equal to 0.30 trucks b. Greater than 0.30 trucks but fewer than or equal to 0.40 trucks c. Greater than 0.40 trucks but fewer than or equal to 0.50 trucks d. Greater than 0.50 trucks Keywords: system, length, customers 71. Use the information in Scenario C.5. What is the average waiting time of trucks in line? a. Fewer than or equal to 1.5 hours b. Greater than 1.5 hours but fewer than or equal to 1.7 hours c. Greater than 1.7 hours but fewer than or equal to 1.9 hours d. Greater than 1.9 hours Keywords: wait, time, queue 72. Use the information in Scenario C.5. What is the average time a truck spends in the system? a. Fewer than or equal to 6.5 hours b. Greater than 6.5 hours but fewer than or equal to 7.0 hours c. Greater than 7.0 hours but fewer than or equal to 7.5 hours d. Greater than 7.5 hours Keywords: time, system, customer Scenario C.6 The Jackson Machine Company has four cutting tools that need to be refurbished after an average of 30 hours, according to an exponential distribution. The single machine that refurbishes the tools needs 15 hours for each tool on the average, with exponential service times. 196

18 73. Use the information in Scenario C.6. What is the probability that there will be no tools in the system? a. Less than or equal to 0.09 b. Greater than 0.09 but less than or equal to 0.13 c. Greater than 0.13 but less than or equal to 0.17 d. Greater than 0.17 Keywords: probability, system, customer 74. Use the information in Scenario C.6. What is the average utilization of the refurbishing machine? a. Less than or equal to 40 percent b. Greater than 40 percent but less than or equal to 50 percent c. Greater than 50 percent but less than or equal to 60 percent d. Greater than 60 percent Keywords: utilization, server, system 75. Use the information in Scenario C.6. What is the average waiting time of tools in line? a. Fewer than or equal to 15 hours b. Greater than 15 hours but fewer than or equal to 25 hours c. Greater than 25 hours but fewer than or equal to 35 hours d. Greater than 35 hours Keywords: wait, queue, time 76. The average lead time of a unit of product through a manufacturing station is 10 minutes. The production rate has been steady at five units per hour. The average work-in-process inventory at this station is: a. 50 units. b. 10 units. c. 5 units. d units. Keywords: WIP, work, process, Little 197

19 77. The production rate has been steady at 20 units per hour. The average work-in-process inventory at this station is 15 units. What is the average lead time through this manufacturing station? a. 15 minutes b. 30 minutes c. 45 minutes d. 1 hour Keyword: Little s Law 78. The average lead time of a unit of product through a manufacturing station is 10 minutes. The average work-in-process inventory at this station has been 30 pieces. What is the production rate? a pieces per minute b pieces per minute c pieces per minute d. 3 pieces per minute Keywords: production, rate, Little s Law 79. Customers arrive at the local grocery store at a steady rate of 40 an hour. Thirty percent of these customers purchase 10 items or less and go to the express line where they enjoy an average checkout time of 5 minutes. Customers that don t meet the express line criterion go to the other checkout line where they spend an average of 10 minutes checking out. How many people are checking out in the express line on average? a. One b. Three c. Twelve d. One hundred Forty Four Keywords: production, rate, Little s Law 80. Customers arrive at the local grocery store at a steady rate of 40 an hour. Thirty percent of these customers purchase 10 items or less and go to the express line where they enjoy an average checkout time of 5 minutes. Customers that don t meet the express line criterion go to checkout line #2 where they spend an average of 10 minutes checking out. How many people are checking out in line #2 on average? a. Less than or equal to one b. Greater than one but less than or equal to three c. Greater than three but less than or equal to five d. Greater than five Keywords: production, rate, Little s Law 198

20 81. Customers arrive at the local grocery store at a steady rate of 40 an hour. Thirty percent of these customers purchase 10 items or less and go to the express line where they enjoy an average checkout time of 5 minutes. Customers that don t meet the express line criterion go to checkout line #2 where they spend an average of 10 minutes checking out. How many people are checking out in both lines on average? a. Less than or equal to one b. Greater than one but less than or equal to three c. Greater than three but less than or equal to five d. Greater than five Keywords: production, rate, Little s Law 82. Customers arrive at the local grocery store every 30 seconds on average. Thirty percent of these customers spend an average of 15 minutes shopping and purchase 10 items or less. These lucky customers go to the express line where they enjoy an average checkout time of 5 minutes. Customers that don t meet the express line criterion average 40 minutes in the store and go to checkout line #2 where they spend an average of 10 minutes checking out. How many people are shopping at any given time? a. Less than or equal to 35 b. Greater than 35 but less than or equal to 70 c. Greater than 70 but less than or equal to 105 d. Greater than 105 Difficulty: Hard Keywords: production, rate, Little s Law 83. Customers arrive at the local grocery store every 30 seconds on average. Thirty percent of these customers spend an average of 15 minutes shopping and purchase 10 items or less. These lucky customers go to the express line where they enjoy an average checkout time of 5 minutes. Customers that don t meet the express line criterion average 40 minutes in the store and go to checkout line #2 where they spend an average of 10 minutes checking out. How many people are checking out at any given time? a. Greater than 15 b. Greater than 10 but less than or equal to 15 c. Greater than 5 but less than or equal to 10 d. Less than or equal to 5 Difficulty: Hard Keywords: production, rate, Little s Law 199

21 84. In a waiting-line problem, increasing advertising expenditures, increasing the number of promotions, or changing the price of a service is most likely to affect: a. customer arrival rates. b. service rates. c. the priority rule. d. the line arrangement. Reference: Decision Areas for Management Keywords: arrival, rate, price, promotion, advertising 85. In a waiting-line problem, assigning additional employees to a service facility will have a direct effect on: a. the line arrangement. b. service rates. c. the size of the customer population. d. the number of service phases. Reference: Decision Areas for Management Keywords: service, rate, servers 86. An operations manager decides that customers should be processed based on the anticipated duration of their request instead of their arrival time. The system has been changed in what fashion? a. Number of phases b. Server efficiency c. Priority rule d. Line arrangement Reference: Decision Areas for Management Keywords: priority, rule 87. An operations manager organizes a process improvement team and the resulting process has a lower µ. Which statement is the best description of this change? a. Arrival rate has fallen. b. There are fewer phases in the new system. c. Interarrival time has fallen. d. Server efficiency has improved. Reference: Decision Areas for Management Keywords: efficiency, server, rate 200

22 88. Instead of having one worker perform all eight steps to process a customer, the erudite operations manager assigns each step to a different worker. In doing so, the operations manager has increased the: a. number of phases. b. number of channels. c. arrival rate. d. interarrival rate. Reference: Decision Areas for Management Keywords: phase 89. If the nature of the customer population, constraints on the line, the priority rule, and the service time distribution renders waiting line theory no longer useful, then an operations manager should rely on: a. decision tree analysis. b. linear programming. c. simulation. d. Little s Law. Reference: Decision Areas for Management Keywords: simulation FILL IN THE BLANK 90. A(n) is one or more customers waiting for service. Answer: waiting line/queue Reference: Why Waiting Lines Form Keywords: waiting, line, customer 91. The is an input that generates potential customers. ustomer population Keywords: customer, population 92. A(n) selects the next customer to be served at the service facility. Answer: priority rule Keywords: priority, rule, customer 201

25 PROBLEMS 103. Customers in a small retail store arrive at the single cashier at the rate of 10 per hour. The average service time for the cashier is five minutes. Arrivals tend to follow a Poisson distribution, and service times follow an exponential distribution. a. What is the average utilization of the cashier? b. What is the average number of customers in the system? c. What is the average number of customers in line? d. What is the average time spent in the system? e. What is the average time spent in line? Answer: Servers 1(Number of servers is assumed to be one in single-server model.) Arrival Rate ( ) 10 Service Rate ( ) 12 Probability of zero customers in the system (P0) Probability of 1 customers in the system Average utilization of the server ( ) a. Average number of customers in the system (L) b. Average number of customers in line (L q) Average waiting/service time in the system (W) Average waiting time in line (W q) Keywords: utilization, number, system, queue, line, length, time c d e. 204

26 104. A professor sits in his plush office and patiently answers questions from his students the afternoon before the final exam. His class is a mass lecture of 350 students, so he keeps socialization to a minimum and concentrates on providing explanations as quickly as possible. On average, he can answer 60 questions an hour and students arrive at his office every five minutes. Student arrivals are Poisson distributed, each student has only one question and answer times are exponentially distributed. a. What fraction of his time is the professor spending answering questions? b. What is the average number of students waiting outside his office? c. What is the average time a student spends in line outside the professor s office? Answer: a. b. c. 12 customers/hour Average utilization customers/hour Queue length L q 0.05 customers Waiting time in queue W q hours = minutes = seconds Keywords: utilization, time, length, queue, line, system 205

27 105. A harvesting crew that follows the wheat harvest has six combines, each requiring service at an average rate of once every 40 hours, according to an exponential distribution. The firm has a mechanic who needs four hours to complete the average repair with exponential service times. a. What is the average utilization of the mechanic? b. What is the average number of combines in the system? c. What is the average number of combines in line? d. What is the average time spent being repaired and waiting? e. What is the average time spent in line waiting for repair? Answer: Customers 6 Arrival Rate ( ) Service Rate ( ) 0.25 Probability of zero customers in the system (P0) Probability of 1 customers in the system #N/A Average utilization of the server ( ) a. Average number of customers in the system (L) b. Average number of customers in line (L q) c. Average waiting/service time in the system (W) d. Average waiting time in line (W q) e. Keywords: utilization, time, length, number, queue, line, system 206

Managing Waiting Lines McGraw-Hill/Irwin Copyright 2008 by The McGraw-Hill Companies, Inc. All rights reserved. 12-2 Where the Time Goes In a life time, the average person will spend: SIX MONTHS Waiting

### Waiting Line Models. 4EK601 Operations Research. Jan Fábry, Veronika Skočdopolová

Waiting Line Models 4EK601 Operations Research Jan Fábry, Veronika Skočdopolová Waiting Line Models Examples of Waiting Line Systems Service System Customer Server Doctor s consultancy room Patient Doctor

### Queuing Theory 1.1 Introduction

Queuing Theory 1.1 Introduction A common situation occurring in everyday life is that of queuing or waiting in a line. Queues (waiting lines) are usually seen at bus stop, ticket booths, doctor s clinics,

### INTRODUCTION AND CLASSIFICATION OF QUEUES 16.1 Introduction

INTRODUCTION AND CLASSIFICATION OF QUEUES 16.1 Introduction The study of waiting lines, called queuing theory is one of the oldest and most widely used Operations Research techniques. Waiting lines are

### Prof. John W. Sutherland. March 20, Lecture #25. Service Processes & Systems Dept. of Mechanical Engineering - Engineering Mechanics

Lecture #25 Prof. John W. Sutherland March 20, 2006 Where the Time Goes In a life time, the average American will spend-- SIX MONTHS Waiting at stoplights EIGHT MONTHS Opening junk mail ONE YEAR Looking

### QUEUING THEORY 4.1 INTRODUCTION

C h a p t e r QUEUING THEORY 4.1 INTRODUCTION Queuing theory, which deals with the study of queues or waiting lines, is one of the most important areas of operation management. In organizations or in personal

### Queuing Models. Queue. System

Queuing Models Introduction The goal of Queuing model is achievement of an economical balance between the cost of providing service and the cost associated with the wait required for that service This

### ISM 270. Service Engineering and Management Lecture 7: Queuing Systems, Capacity Management

ISM 270 Service Engineering and Management Lecture 7: Queuing Systems, Capacity Management 1 Queuing Systems CHARACTERISTICS OF A WAITING LINE SYSTEM Arrival Characteris=cs Wai=ng Line Characteris=cs Service

### Textbook: pp Chapter 12: Waiting Lines and Queuing Theory Models

1 Textbook: pp. 445-478 Chapter 12: Waiting Lines and Queuing Theory Models 2 Learning Objectives (1 of 2) After completing this chapter, students will be able to: Describe the trade-off curves for cost-of-waiting

### Lecture 45. Waiting Lines. Learning Objectives

Lecture 45 Waiting Lines Learning Objectives After completing the lecture, we should be able to explain the formation of waiting lines in unloaded systems, identify the goal of queuing ( waiting line)

### Chapter 13. Waiting Lines and Queuing Theory Models

Chapter 13 Waiting Lines and Queuing Theory Models To accompany Quantitative Analysis for Management, Eleventh Edition, by Render, Stair, and Hanna Power Point slides created by Brian Peterson Learning

### Chapter 14. Waiting Lines and Queuing Theory Models

Chapter 4 Waiting Lines and Queuing Theory Models To accompany Quantitative Analysis for Management, Tenth Edition, by Render, Stair, and Hanna Power Point slides created by Jeff Heyl 2008 Prentice-Hall,

### Chapter 7A Waiting Line Management. OBJECTIVES Waiting Line Characteristics Suggestions for Managing Queues Examples (Models 1, 2, 3, and 4)

Chapter 7A Waiting Line Management 1 OBJECTIVES Waiting Line Characteristics Suggestions for Managing Queues Examples (Models 1, 2, 3, and 4) Components of the Queuing System 2 Customer Arrivals Servicing

### Mathematical approach to the analysis of waiting lines

Queueing Theory Mathematical approach to the analysis of waiting lines This theory is applicable to a wide range of service operations, including call centers, banks, post offices, restaurants, theme parks,

### INDIAN INSTITUTE OF MATERIALS MANAGEMENT Post Graduate Diploma in Materials Management PAPER 18 C OPERATIONS RESEARCH.

INDIAN INSTITUTE OF MATERIALS MANAGEMENT Post Graduate Diploma in Materials Management PAPER 18 C OPERATIONS RESEARCH. Dec 2014 DATE: 20.12.2014 Max. Marks: 100 TIME: 2.00 p.m to 5.00 p.m. Duration: 03

### Queuing Theory. Carles Sitompul

Queuing Theory Carles Sitompul Syllables Some queuing terminology (22.1) Modeling arrival and service processes (22.2) Birth-Death processes (22.3) M/M/1/GD/ / queuing system and queuing optimization model

### WAITING LINE MODELS Introduction

WAITING LINE MODELS Introduction Professor Robert Saltzman Operations Analysis Queuing Analysis: The Study of Waiting Lines Lines are everywhere in the service sector: Grocery store checkout Airport bag

### Short-Term. Scheduling

Short-Term 15 Scheduling PowerPoint presentation to accompany Heizer, Render, Munson Operations Management, Twelfth Edition Principles of Operations Management, Tenth Edition PowerPoint slides by Jeff

### Learning Objectives. Scheduling. Learning Objectives

Scheduling 16 Learning Objectives Explain what scheduling involves and the importance of good scheduling. Discuss scheduling needs in high-volume and intermediate-volume systems. Discuss scheduling needs

### Manufacturing Resource Planning

Outline Manufacturing Resource Planning MRP The Strategic Importance of Short- Term Scheduling Scheduling Issues Forward and Backward Scheduling Scheduling Criteria Outline Continued Scheduling Process-Focused

### Introduction - Simulation. Simulation of industrial processes and logistical systems - MION40

Introduction - Simulation Simulation of industrial processes and logistical systems - MION40 1 What is a model? A model is an external and explicit representation of part of reality as seen by the people

### Application of Queuing Theory in a Small Enterprise

Application of Queuing Theory in a Small Enterprise Anindita Sharma 1#, Parimal Bakul Barua 2# 1 M.E. student, 2 Professor and H.O.D. # Department of Mechanical Engineering, Jorhat Engineering College,

### OPERATIONS RESEARCH Code: MB0048. Section-A

Time: 2 hours OPERATIONS RESEARCH Code: MB0048 Max.Marks:140 Section-A Answer the following 1. Which of the following is an example of a mathematical model? a. Iconic model b. Replacement model c. Analogue

### Queuing Theory: A Case Study to Improve the Quality Services of a Restaurant

Queuing Theory: A Case Study to Improve the Quality Services of a Restaurant Lakhan Patidar 1*, Trilok Singh Bisoniya 2, Aditya Abhishek 3, Pulak Kamar Ray 4 Department of Mechanical Engineering, SIRT-E,

### Revista Economică 66:5 (2014) THE IMPORTANCE OF PROPER MANAGEMENT OF WAITING LINES

THE IMPORTANCE OF PROPER MANAGEMENT OF WAITING LINES TROANCA Dumitru 1 Lucian Blaga University of Sibiu, Romania Abstract The waiting lines are a part o our existence, more or less. Nobody wants to spend

### 9.7 Summary. 9.8 Training Cases. 394 Business Process Modeling, Simulation and Design

394 Business Process Modeling, Simulation and Design experience more than 14 min of cycle time under the new design, which more than satisfies the goal of at most 30 min. Furthermore, no type 3 patient

### Solutions Manual Discrete-Event System Simulation Fifth Edition

Solutions Manual Discrete-Event System Simulation Fifth Edition Jerry Banks John S. Carson II Barry L. Nelson David M. Nicol August 10, 2009 Contents 1 Introduction to Simulation 1 2 Simulation Examples

### Solutions Manual Discrete-Event System Simulation Fifth Edition

Solutions Manual Discrete-Event System Simulation Fifth Edition Jerry Banks John S. Carson II Barry L. Nelson David M. Nicol August 10, 2009 Contents 1 Introduction to Simulation 1 2 Simulation Examples

### Hamdy A. Taha, OPERATIONS RESEARCH, AN INTRODUCTION, 5 th edition, Maxwell Macmillan International, 1992

Reference books: Anderson, Sweeney, and Williams, AN INTRODUCTION TO MANAGEMENT SCIENCE, QUANTITATIVE APPROACHES TO DECISION MAKING, 7 th edition, West Publishing Company,1994 Hamdy A. Taha, OPERATIONS

### Use of Queuing Models in Health Care - PPT

University of Wisconsin-Madison From the SelectedWorks of Vikas Singh December, 2006 Use of Queuing Models in Health Care - PPT Vikas Singh, University of Arkansas for Medical Sciences Available at: https://works.bepress.com/vikas_singh/13/

### Modeling and Performance Analysis with Discrete-Event Simulation

Simulation Modeling and Performance Analysis with Discrete-Event Simulation Chapter 2 Simulation Examples Simulation using a Table Introducing simulation by manually simulating on a table Can be done via

### Queueing Theory and Waiting Lines

Queueing Theory and Waiting Lines Most waiting line problems are trying to find the best service Large staff => Good Service Small staff => Poor Service What is Best It depends on the organization! Most

### Simulation Examples. Prof. Dr. Mesut Güneş Ch. 2 Simulation Examples 2.1

Chapter 2 Simulation Examples 2.1 Contents Simulation using Tables Simulation of Queueing Systems Examples A Grocery Call Center Inventory System Appendix: Random Digitsit 1.2 Simulation using Tables 1.3

### Simulating Queuing Models in SAS

ABSTRACT Simulating Queuing Models in SAS Danny Rithy, California Polytechnic State University, San Luis Obispo, CA This paper introduces users to how to simulate queuing models using SAS. SAS will simulate

### The Queueing Theory. Chulwon Kim. November 8, is a concept that has driven the establishments throughout our history in an orderly fashion.

The Queueing Theory Chulwon Kim November 8, 2010 1 Introduction The idea of a queue is one that has been around for as long as anyone can remember. It is a concept that has driven the establishments throughout

### 4 BUILDING YOUR FIRST MODEL. L4.1 Building Your First Simulation Model. Knowing is not enough; we must apply. Willing is not enough; we must do.

Pro, Second Edition L A B 4 BUILDING YOUR FIRST MODEL Knowing is not enough; we must apply. Willing is not enough; we must do. Johann von Goethe In this lab we build our first simulation model using Pro.

### Abstract. Introduction

Queuing Theory and the Taguchi Loss Function: The Cost of Customer Dissatisfaction in Waiting Lines Ross Fink and John Gillett Abstract As customer s wait longer in line they become more dissatisfied.

### An-Najah National University Faculty of Engineering Industrial Engineering Department. System Dynamics. Instructor: Eng.

An-Najah National University Faculty of Engineering Industrial Engineering Department System Dynamics Instructor: Eng. Tamer Haddad Introduction Knowing how the elements of a system interact & how overall

### CH-1. A simulation: is the imitation of the operation of a real-world process WHEN SIMULATION IS THE APPROPRIATE TOOL:

CH-1 A simulation: is the imitation of the operation of a real-world process WHEN SIMULATION IS THE APPROPRIATE TOOL: 1. Simulation enables the study of, and experimentation with, the internal interactions

### Assume only one reference librarian is working and M/M/1 Queuing model is used for Questions 1 to 7.

3 Test 2, Spring 2009 During a normal week, the reference desk of JMU East Library serves students at the rate of one every 6 minutes. Assume the service time is exponentially distributed. It is observed

### D.K.M.COLLEGE FOR WOMEN (AUTONOMOUS), VELLORE-1. OPERATIONS RESEARCH

D.K.M.COLLEGE FOR WOMEN (AUTONOMOUS), VELLORE-1. OPERATIONS RESEARCH UNIT-1 DECISION THEORY SECTION -A 1. Define Essential Elements in Decision Model? 2. Explain steps in Decision theory approach. 3. A

### Chapter III TRANSPORTATION SYSTEM. Tewodros N.

Chapter III TRANSPORTATION SYSTEM ANALYSIS www.tnigatu.wordpress.com tedynihe@gmail.com Lecture Overview Traffic engineering studies Spot speed studies Volume studies Travel time and delay studies Parking

### University Question Paper Two Marks

University Question Paper Two Marks 1. List the application of Operations Research in functional areas of management. Answer: Finance, Budgeting and Investment Marketing Physical distribution Purchasing,

### Slides 2: Simulation Examples

Slides 2: Simulation Examples Today I ll present several examples of simulations that can be performed by devising a simulation table either manually or with a spreadsheet. This will provide insight into

### OPERATING SYSTEMS. Systems and Models. CS 3502 Spring Chapter 03

OPERATING SYSTEMS CS 3502 Spring 2018 Systems and Models Chapter 03 Systems and Models A system is the part of the real world under study. It is composed of a set of entities interacting among themselves

### Simulation Technique for Queuing Theory: A Case Study

Oct - Dec 2015 Transactions 2(8): 388-396 eissn : 2349 0020 pissn : 2394-4544 S T A T I S T I C S C A S E S T U D Y Simulation Technique f Queuing They: A Case Study Dr. R. Ramakrishna 1 and Mr. KedirMohamedhusien

### Production and Operations Management

POM, Chapter Production and Operations Management Chapter Norman Gaither Greg Frazier Shop-Floor Planning and Control in Manufacturing Slides Prepared by John Loucks 7 8 B [----------[ ----------] E [--------------

### Scheduling Processes 11/6/16. Processes (refresher) Scheduling Processes The OS has to decide: Scheduler. Scheduling Policies

Scheduling Processes Don Porter Portions courtesy Emmett Witchel Processes (refresher) Each process has state, that includes its text and data, procedure call stack, etc. This state resides in memory.

### SCHEDULING AND CONTROLLING PRODUCTION ACTIVITIES

SCHEDULING AND CONTROLLING PRODUCTION ACTIVITIES Al-Naimi Assistant Professor Industrial Engineering Branch Department of Production Engineering and Metallurgy University of Technology Baghdad - Iraq dr.mahmoudalnaimi@uotechnology.edu.iq

### Production and Operations Management

Production and Operations Management Norman Gaither Greg Frazier Slides Prepared by John Loucks 999 South-Western College Publishing 0 Chapter Shop-Floor Planning and Control in Manufacturing 3 4 5 6 7

### Queuing CEE 320. Anne Goodchild CEE 320

Queuing Anne Goodchild Fundamentals of Queuing Theory Microscopic traffic flow Different analysis than theory of traffic flow Intervals between vehicles is important Rate of arrivals is important Queuing

### CSC 553 Operating Systems

CSC 553 Operating Systems Lecture 9 - Uniprocessor Scheduling Types of Scheduling Long-term scheduling The decision to add to the pool of processes to be executed Medium-term scheduling The decision to

### PRODUCTION ACTIVITY CONTROL (PAC)

PRODUCTION ACTIVITY CONTROL (PAC) Concerns execution of material plans Contains shop floor control (SFC), and vendor scheduling and follow-up SFC encompasses detailed scheduling and control of individual

### Global Journal of Advance Engineering Technology and Sciences

Global Journal of Advanced Engineering Technologies and Sciences AN IMPROVED MODEL FOR ATM IN A WIRELESS COMMUNICATION NETWORK USING QUEUING TECHNIQUE Orji Hope Ekpereamaka *1, Udeh Ikemefuna James 2,

### ANS: Q2 and Q6: VORA, Chapter 9, Inventory Management:

OPERATIONS RESEARCH Q1. What is Operations Research? Explain how Operations Research helps in decision making. Or Explain how Operations Research helps in managerial decision making process. Q2.What are

### On the real final exam on Dec. 13, 2006, you will receive a printed copy of the questions with a cover page showing your last name and campus

On the real final exam on Dec. 13, 2006, you will receive a printed copy of the questions with a cover page showing your last name and campus computing ID. You will also be able to access the questions

### Chapter 2 Simulation Examples. Banks, Carson, Nelson & Nicol Discrete-Event System Simulation

Chapter 2 Simulation Examples Banks, Carson, Nelson & Nicol Discrete-Event System Simulation Purpose To present several examples of simulations that can be performed by devising a simulation table either

### Production Planning & Control BMM4823. Scheduling. by Dr. Ahmad Nasser Mohd Rose

Production Planning & Control BMM4823 Scheduling by Dr. Ahmad Nasser Mohd Rose nasser@ump.edu.my Chapter Description Aims To understand the importance of short term scheduling in planning To determine

### Service efficiency evaluation of automatic teller machines a study of Taiwan financial institutions with the application of queuing theory

Service efficiency evaluation of automatic teller machines a study of Taiwan financial institutions with the application of queuing theory Pei-Chun Lin Department of Transportation and Communication Science

### International Journal of Scientific & Engineering Research, Volume 8, Issue 1, January ISSN

International Journal of Scientific & Engineering Research, Volume 8, Issue 1, January-2017 1700 ANALYSIS OF MULTIPLE-QUEUE MULTIPLE-SERVER QUEUING SYSTEM: A CASE STUDY OF FIRST BANK NIG. PLC, AFIKPO BRANCH

### AN M/M/1/N FEEDBACK QUEUING SYSTEM WITH REVERSE BALKING

Journal of Reliability and Statistical Studies ISSN (Print): 0974-8024, (Online):2229-5666 Vol. 8, Issue 1 (2015): 31-38 AN M/M/1/N FEEDBACK QUEUING SYSTEM WITH REVERSE BALKING 1 Rakesh Kumar, 2 Bhupender

### The application of queuing theory in the effective management of time in money deposit banks - A study of Zenith bank PLC in Enugu Metropolis

The application of queuing theory in the effective management of time in money deposit banks - A study of Zenith bank PLC in Enugu Metropolis ABSTRACT Ugwa Magnus Department of Business Administration

### Paper: 10, Services Marketing Module: 29, Managing Waiting Lines

Paper: 10, Services Marketing Module: 29, Managing Waiting Lines 29. Managing Waiting Lines 1.0 Introduction A newly opened fast food service restaurant in the busy commercial area of Central Delhi was

### CHAPTER 5: DISCRETE PROBABILITY DISTRIBUTIONS

Discrete Probability Distributions 5-1 CHAPTER 5: DISCRETE PROBABILITY DISTRIBUTIONS 1. Thirty-six of the staff of 80 teachers at a local intermediate school are certified in Cardio- Pulmonary Resuscitation

### Optimal staffing policy for queuing systems with cyclic demands: waiting cost approach

007-0417 Optimal staffing policy for queuing systems with cyclic demands: waiting cost approach Pen-Yuan Liao Department of Business Management, College of Management, National United University, 1 Lien

### and type II customers arrive in batches of size k with probability d k

xv Preface Decision making is an important task of any industry. Operations research is a discipline that helps to solve decision making problems to make viable decision one needs exact and reliable information

### Banks, Carson, Nelson & Nicol

Banks, Carson, Nelson & Nicol Discrete-Event System Simulation Purpose To present several examples of simulations that can be performed by devising a simulation table either manually or with a spreadsheet.

### 9 SIMULATION OUTPUT ANALYSIS. L9.1 Terminating versus Nonterminating Simulations

L A B 9 SIMULATION OUTPUT ANALYSIS Nothing has such power to broaden the mind as the ability to investigate systematically and truly all that comes under thy observation in life. Marcus Aurelius In this

### Chapter 2 Simulation Examples. Banks, Carson, Nelson & Nicol Discrete-Event System Simulation

Chapter 2 Simulation Examples Banks, Carson, Nelson & Nicol Discrete-Event System Simulation Purpose To present several examples of simulations that can be performed by devising a simulation table either

### WAYNE STATE UNIVERSITY Department of Industrial and Manufacturing Engineering May, 2010

WAYNE STATE UNIVERSITY Department of Industrial and Manufacturing Engineering May, 2010 PhD Preliminary Examination Candidate Name: 1- Sensitivity Analysis (20 points) Answer ALL Questions Question 1-20

### QUEUING THEORY APPLICATION AT TICKET WINDOWS IN RAILWAY STATIONS (A STUDY OF THE LAGOS TERMINUS, IDDO, LAGOS STATE, NIGERIA)

Equatorial Journal of Computational and Theoretical Science, 2 (1): 1-5 Journal Homepage: www.erjournals.com ISSN: 0184-7937 QUEUING THEORY APPLICATION AT TICKET WINDOWS IN RAILWAY STATIONS (A STUDY OF

### Queues (waiting lines)

Queues (waiting lines) Non-people queues Great inefficiencies also occur because of other kinds of waiting than people standing in line. For example, making machines wait to be repaired may result in lost

### BUSSINES SIMULATING PROCES FOR THE PRODUCTION SURROUND, USING QUEUEING SYSTEM SIMULATION WITH WINQSB

7 th International Conference Research and Development in Mechanical Industry RaDMI 2007 16-20. September 2007, Belgrade, Serbia BUSSINES SIMULATING PROCES FOR THE PRODUCTION SURROUND, USING QUEUEING SYSTEM

### PRACTICE PROBLEM SET Topic 1: Basic Process Analysis

The Wharton School Quarter II The University of Pennsylvania Fall 1999 PRACTICE PROBLEM SET Topic 1: Basic Process Analysis Problem 1: Consider the following three-step production process: Raw Material

### DESIGN OF SERVICE SYSTEM FOR INSURANCE BUSINESS FACING CUSTOMER IMPATIENCE USING QUEUING THEORY

DESIGN OF SERVICE SYSTEM FOR INSURANCE BUSINESS FACING CUSTOMER IMPATIENCE USING QUEUING THEORY Rakesh Kumar 1, Bhupender Kumar Som 2 1 School of Mathematics, Shri Mata Vaishno Devi University, Katra,

### G54SIM (Spring 2016)

G54SIM (Spring 2016) Lecture 03 Introduction to Conceptual Modelling Peer-Olaf Siebers pos@cs.nott.ac.uk Motivation Define what a conceptual model is and how to communicate such a model Demonstrate how

### Use of Monte Carlo Simulation for Analyzing Queues in a Financial Institution

International Journal on Future Revolution in Computer Science & Communication Engineering ISSN: 2454-4248 Use of Monte Carlo Simulation for Analyzing Queues in a Financial Institution A Case Study In

### Chapter 8 Variability and Waiting Time Problems

//9 Chapter 8 Variability and Waiting Problems A Call Center Example Arrival Process and Service Variability Predicting Waiting s Waiting Line Management 8. The Operation of a Typical Call Center Incoming

### Production planning and. Unit 3

1 Production planning and scheduling Unit 3 2 Background Because the aircraft manufacturing industry is highly sensitive to fluctuating demands and to their corresponding impact on production costs, a

### For Questions 1 to 6, refer to the following information

For Questions 1 to 6, refer to the following information The Box-and-Whisker plots show the results of the quiz and test for QMS102 in Fall2010 Question 1. Calculate the mode for the quiz result of QMS102

### The Application of Waiting Lines System in Improving Customer Service Management: The Examination of Malaysia Fast Food Restaurants Industry

IOP Conference Series: Earth and Environmental Science PAPER OPEN ACCESS The Application of Waiting Lines System in Improving Customer Service Management: The Examination of Malaysia Fast Food Restaurants

### Application of queuing theory in construction industry

Application of queuing theory in construction industry Vjacheslav Usmanov 1 *, Čeněk Jarský 1 1 Department of Construction Technology, FCE, CTU Prague, Czech Republic * Corresponding author (usmanov@seznam.cz)

### Application the Queuing Theory in the Warehouse Optimization Jaroslav Masek, Juraj Camaj, Eva Nedeliakova

Application the Queuing Theory in the Warehouse Optimization Jaroslav Masek, Juraj Camaj, Eva Nedeliakova Abstract The aim of optimization of store management is not only designing the situation of store

### INTRODUCTION TO HOM. The current version of HOM addresses five key competitive advantage drivers

1 INTRODUCTION TO HOM 1. OVERVIEW HOM is a software system designed to help mid level managers and owners of small businesses gain competitive advantage from operations. It is also useful for business

### Efficiency of Controlled Queue system in Supermarket using Matlab / Simulink

Volume 114 No. 6 2017, 283-288 ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu ijpam.eu Efficiency of Controlled Queue system in Supermarket using Matlab /

### Spring 1992 Design and Implementation of an Efficient Mail Distribution Route for the New Federal Reserve Bank Building in Dallas

S. S 1992-04 Spring 1992 Design and Implementation of an Efficient Mail Distribution Route for the New Federal Reserve Bank Building in Dallas Choi Mercer I Design and Implementation of an Efficient Mail

### Effective Business Management in Uncertain Business Environment Using Stochastic Queuing System with Encouraged Arrivals and Impatient Customers

Proceedings of International Conference on Strategies in Volatile and Uncertain Environment for Emerging Markets July 14-15, 2017 Indian Institute of Technology Delhi, New Delhi pp.479-488 Effective Business

### Evaluation of Value and Time Based Priority Rules in a Push System

Evaluation of Value and Time Based Priority Rules in a Push System Dr. V. Arumugam * and Abdel Monem Murtadi ** * Associate Professor, Business and Advanced Technology Center, Universiti Teknologi Malaysia,

### SSRG International Journal of Mechanical Engineering (SSRG-IJME) volume 2 Issue 5 May 2015

Chi Square Method for Testing the Inter- Arrival and Service Patterns Satish Verma 1, Dr. Sridhar. K 2, Ankit Kashyap 3 1 (Mechanical Department, C.S.I.T Durg, C.S.V.T.U Bhilai, India) 2 (Mechanical Department,

### 4. Management Information Systems and Decision Support Systems

4. Management Information Systems and Decision Support Systems As we have seen, a transaction processing system serves as the foundation for the other systems. Transaction processing systems are operational

### Service Operations (SO) Post Graduate Program for Working Executives Week 5

Service Operations (SO) Post Graduate Program for Working Executives 2014-15 Week 5 Vinay Kumar Kalakbandi Assistant Professor Operations & Systems Area 09/11/2014 Vinay Kalakbandi 1 Recap Service quality

### Intro to O/S Scheduling. Intro to O/S Scheduling (continued)

Intro to O/S Scheduling 1. Intro to O/S Scheduling 2. What is Scheduling? 3. Computer Systems Scheduling 4. O/S Scheduling Categories 5. O/S Scheduling and Process State 6. O/S Scheduling Layers 7. Scheduling

### BSc (Hons) Business Information Systems. Examinations for / Semester 2

BSc (Hons) Business Information Systems Cohort: BIS/14B/FT Examinations for 2015-2016 / Semester 2 MODULE: QUANTITATIVE ANALYSIS FOR BUSINESS MODULE CODE: Duration: 3 Hours Instructions to Candidates:

### Techniques of Operations Research

Techniques of Operations Research C HAPTER 2 2.1 INTRODUCTION The term, Operations Research was first coined in 1940 by McClosky and Trefthen in a small town called Bowdsey of the United Kingdom. This

Q 1 Furgon Van Hire rents out trucks and vans. One service they offer is a sameday rental deal under which account customers can call in the morning to hire a van for the day. Five vehicles are available