Lab Assignment Airport

UNIVERSITY OF MASSACHUSETTS DARTMOUTH



MNE 530 Lab Assignment Airport October 2016

Under the guidance of Professor Soheil Yousefsibdari By Umesh Babariya ID: 01541559

UMD October 2016/MNE 530

Page 1

INDEX

SR. NO 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

CHAPTER NAME Problem Introduction Basic Definitions Part A: Arena Simulation Model • Flow chart of Module • Building the Module Part A: Result Part B: Arena Simulation Model • Flow chart of Module • Building the Module Part B: Result And Comparison Part C: Arena Simulation Model • Flow chart of Module • Building the Module Part C: Result Part D: Arena Simulation Model • Flow chart of Module • Building the Module Part D: Result

PAGE NO. 3 4 5

7 8

9 10

11 12

13



UMD October 2016/MNE 530

Page 2

PROBLEM INTRODUCTION 1. Problem description Travelers arrive at the main entrance door of an airline terminal according to an exponential interarrival time distribution with mean 1.6 minutes. The travel time from the entrance to the checkin is distributed uniformly between 2 and 3 minutes. At the check-in counter, travellers wait in a single line until one of the five agents is available to serve them. The check-in time (in minutes) follows a Weibull distribution with parameters b = 7.76 and a = 3.91 . Upon completion of their check-in, travellers exit to their gates. a. Create a simulation model, including animation, of this system. Run the simulation for ten 16-hour shifts to determine the average time in system, average number of passengers completing check-in, and the average length of the check-in queue. b. Modify the foregoing model by adding agent breaks. The 16 hours are divided into two 8-hour shifts. Agent breaks are staggered, starting at 90 minutes into each shift. Each agent is given one 15-minute break. Agent lunch breaks (30 minutes) are also staggered, starting 3.5 hours into each shift. Compare the results of this model to the results without agent breaks. c. During the verification process, it was discovered that there were really two types of passengers. The first passenger type arrives according to an exponential interarrival distribution with mean 2.4 minutes and has a service time (in minutes) following a gamma distribution with parameters b = 0.42 and a = 10.4 . The second type of passenger arrives according to an exponential distribution with mean 4.4 minutes and has a service time (in minutes) following 3 plus an Erlang distribution with parameters ExpMean = 0.54 and k = 15 (i.e., the Expression for the service time is 3 + ERLA(0.54, 15)). Modify the model from part b to include this new information, and compare the results. d. Now consider the problem in part a (no staff break and no two-type customer). Now imagine that snow storm takes place in one of the major destination of the airport. The storm causes a delay and if a passenger’s destination is stormed, they do not get served and need to wait in another line (not at the counter) for the storm to be cleared. The chance of storm is 2% in a given day and the probability of an arriving customer to be the stormed city (if any storm occurs) is 20%. Simulate this situation and evaluate how long it take for the airport to return to its normal operation.

UMD October 2016/MNE 530

Page 3

BASIC DEFINITIONS 1. System: A collection of entities (people, machines, etc.) that interact together toward the accomplishment of some logical end. 2. Model: An abstract representation of a system, usually containing logical or mathematical relationships that describe a system in terms of its state, events, entities and attributes, and lists. 3. System state: Collection of variables containing all the information necessary to describe the system at any point in time. Defined relative to the objectives of the study. 4. Activity: Duration of specified length (such as a service time or interarrival time) that is specified by a constant or by a probability distribution. 5. Event: An instantaneous occurrence that may change the state of the system. 6. Entity: An object or component of the system which requires explicit representation in the system (e.g., server, customer, machine). 7. Attributes: Properties of a given entity. Could include customer priority, routing order of a job through a job shop, time of arrival for a customer, status (busy or idle) of a server. 8. List: Collection of (permanently or temporarily) associated entities, ordered in some logical fashion. Often list of customers waiting in queue, ordered by FIFO or priority. 9. Various statistics to be reviewed: - time spent in the system for each part type - number of parts in the queue of each machine - - production rate for each part type - number of parts discarded

UMD October 2016/MNE 530

Page 4

PART A ARENA SIMULATION MODEL

1. Flowchart of the Arena model

Figure 1: Flowchart for travelers arriving at the Airport

2. Building the Module:

•

Step 1: CREATE Module and ROUTE Module

Figure 2: Creating and assigning

As mentioned in the problem Travelers arrive at the main entrance door of an airline terminal according to an exponential interarrival time distribution with mean 1.6 minutes. The travel time from the entrance to the check-in is distributed uniformly between 2 and 3 minutes. So, in CREATE module value 1.6 is given to the random exponential distribution. Travel time is given in terms of route module.

UMD October 2016/MNE 530

Page 5

•

Step 2: Process Module At the check-in counter, travellers wait in a single line until one of the five agents is available to serve them. The check-in time (in minutes) follows a Weibull distribution with parameters and . Upon completion of their check-in, travellers exit to their gates. So, using the PROCESS module check in distribution is given and SET of 5 agents are selected as a Resources. Figure 3: Check in Process

•

Step 3: Run Setup

As shown in the figure Run the simulation for ten 16-hour shifts to determine the average time in system, average number of passengers completing check-in, and the average length of the check-in queue Replication Length is set to 16 hours.

•

Step 4: Developing the LOGIC

According to problem description we need to design a LOGIC such that arrival travelers need to send to the check in process. So, ROUTE and Station module is used to send the arrival travelers to Check in process. Here as shown in the process flow chart of model all travelers are heading towards their gate once they are done with check in. Figure 4: The logic of the problem

UMD October 2016/MNE 530

Page 6

PART A RESULT •

Animation of the Model:





Figure 5: Animation of the system

Figure sows the animation of the problem. Passengers arrived at the check in counter are waiting at the counter, getting served with five agents and leaving the system and heading towards their gates. •

Collected statics from the report. Average time of a passenger in the system Number of Passenger completing check-in Average length of a Check-in queue

0.2681 599 2.5442

UMD October 2016/MNE 530

Page 7



PART 2 ARENA SIMULATION MODEL

Modified Flowchart of model

Figure 6: Flowchart with agents having Breaks

For part b of the problem I have modified the foregoing model by adding agent breaks. Initially all agents had a fixed capacity of service. This fixed capacity can be changed from the Resource module of Basic Process. As shown in the figure agents type is now change to ‘Based on Schedule’.



Figure 7: Scheduling breaks

The 16 hours are divided into two 8-hour shifts. Agent breaks are staggered, starting at 90 minutes into each shift. Each agent is given one 15-minute break. Agent lunch breaks (30 minutes) are also staggered, starting 3.5 hours into each shift. Compare the results of this model to the results without agent breaks.

UMD October 2016/MNE 530

Page 8

PART B RESULT

•

Collected statics from the report. Average time of a passenger in the system Number of Passenger completing check-in Average length of a Check-in queue •

Comparison:

Average time of a passenger in the system Number of Passenger completing check-in Average length of a Check-in queue

•

0.4760 624 10.9324

Part A 0.2681

Part B 0.4760

599

624

2.5442

10.9324

From the above comparison we can say that adding brakes to the agents increase the system efficiency. Average number of passenger completing check in increases from 599 to 624.

UMD October 2016/MNE 530

Page 9

PART C ARENA SIMULATION MODEL

1. Flowchart of the Arena model

Figure 8: Type 1 Type 2 Passengers model

2. Building the Module:

Figure 9: Creating and assigning for Type 1 Type 2 Passengers

During the verification process, it was discovered that there were really two types of passengers. The first passenger type arrives according to an exponential interarrival distribution with mean 2.4 minutes and has a service time (in minutes) following a gamma distribution with parameters b = 0.42 and a = 10.4 . The second type of passenger arrives according to an exponential distribution with mean 4.4 minutes and has a service time (in minutes) following 3 plus an Erlang distribution with parameters ExpMean = 0.54 and k = 15 . This is shown in the above figure.

UMD October 2016/MNE 530

Page 10

PART C RESULT

•

Collected Statics of Type 1 and Type 2 Passenger:

Average time of a passenger in the system Number of Passenger completing check-in Average length of a Check-in queue

UMD October 2016/MNE 530

Type 1 Passenger 0.2877

Type 2 Passenger 0.2902

390

204

3.3504

3.3504

Page 11

PART D ARENA SIMULATION MODEL 1. Flowchart of the Arena model

Figure 10: Flowchart of Separating stormed passengers

2. Building the Module:

Here, DECIDE module is used to separate the stormed passenger from the arriving all travellers. The Passengers going to stormed place have to wait in another queue. The storm causes a delay and if a passenger’s destination is stormed, they do not get served. The chance of storm is 2% in a given day and the probability of an arriving customer to be the stormed city (if any storm occurs) is 20%.

UMD October 2016/MNE 530

Page 12

PART D RESULT

•

Collected statics from the report. Average time of a passenger in the system Number of Passenger completing check-in Average length of a Check-in queue

0.2681 544 2.5442



UMD October 2016/MNE 530

Page 13