An Assessment Of Queuing System At Polyclinic Community Ayer Keroh.
UNIVERSITI TEKNIKAL MALAYSIA MELAKA
BORANG PENGESAHAN STATUS LAPORAN PROJEK SARJANA MUDA
TAJUK: An Assessment of Queuing System at Polyclinic community at Ayer Keroh
SESI PENGAJIAN: 20010/11 Semester 2 Saya CHAN KIEN HOW
mengaku membenarkan Laporan PSM ini disimpan di Perpustakaan Universiti Teknikal Malaysia Melaka (UTeM) dengan syarat-syarat kegunaan seperti berikut: 1. Laporan PSM adalah hak milik Universiti Teknikal Malaysia Melaka dan penulis. 2. Perpustakaan Universiti Teknikal Malaysia Melaka dibenarkan membuat salinan
untuk tujuan pengajian sahaja dengan izin penulis.
3. Perpustakaan dibenarkan membuat salinan laporan PSM ini sebagai bahan pertukaran antara institusi pengajian tinggi.
4. **Sila tandakan (√)
SULIT
TERHAD
TIDAK TERHAD
(Mengandungi maklumat yang berdarjah keselamatan atau kepentingan Malaysia yang termaktub di dalam AKTA RAHSIA RASMI 1972)
(Mengandungi maklumat TERHAD yang telah ditentukan oleh organisasi/badan di mana penyelidikan dijalankan)
Alamat Tetap: 1913 JALAN SK 13/5 43300 SERI KEMBANGAN SELANGOR DARUL EHSAN
Tarikh: _________________________
Disahkan oleh:
PENYELIA PSM
Tarikh: _______________________
** Jika Laporan PSM ini SULIT atau TERHAD, sila lampirkan surat daripada pihak berkuasa/organisasi berkenaan dengan menyatakan sekali sebab dan tempoh laporan PSM ini perlu dikelaskan sebagai SULIT atau TERHAD.
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UNIVERSITI TEKNIKAL MALAYSIA MELAKA
AN ASSESMENT OF QUEUING SYSTEM AT POLYCLINIC
COMMUNITY AYER KEROH
This report submitted in accordance with requirement of the Universiti Teknikal Malaysia Melaka (UTeM) for the Bachelor Degree of Manufacturing Engineering
(Manufacturing Management)
by
CHAN KIEN HOW B050710178
FACULTY OF MANUFACTURING ENGINEERING 2011
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DECLARATION
I hereby declare that this report entitled “An Assessment of Queue System at Polyclinic Community Ayer Keroh” is the result of my own research except as cited in the references.
Signature :
Author’s Name : CHAN KIEN HOW
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APPROVAL
This report is submitted to the Faculty of Manufacturing Engineering of UTeM as a partial fulfillment of the requirements for the degree of Bachelor of Manufacturing Engineering (Manufacturing Management). The members of the supervisory committee are as follow:
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ABSTRACT
Queuing is daily practiced in our life. There is a lot of knowledge is inherent in the process of constructing a queue manner which capable to meet the incoming customer and yet without losing the server competiveness in the system. In this final year project, a queue system in Polyclinic is chosen as the assessment place to identify current service level system is it capable to meet the increasing patient’s capacity. The aim of this study is to adapt the Queue model and simulation method on the Polyclinic Ayer Keroh system flow to study the current service time provided by polyclinic is it meets the current arrival rate of the patient to the system. The data of the arrival rate and service time is collected will be analyzed and further translated into histogram and poses Goodness of Fit-Test by using Minitab Statistical Software for family distribution identification. With the analyzed result family distribution, it will be translated in the queuing theory and simulation. The relation of the system like time-average number in system (L), average time spent in system per customer (w), and server utilization (ρ) could be revealed. From the generated results and discussion of the system’s relation is compared with the result of the survey which is reflecting and indicating the patient desired service level provision by the polyclinic. Verification and validation of the model is needed to be done prior the model is compared with the standard. This can be concluded that the flow in system is stable and upgradeable. The implementation of the queue model and simulation is justified in this assessment. The potential improvement options are also suggested in order to further improve the quality of the service provided to the patient.
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ABSTRAK
Beratur merupakan satu fenomena selalu berlaku dalam kehidupan seharian manusia. Dalam process penjanaan satu antrian bukan sahaja yang mampu menambung bilangan pesakit yang kian meningkat tetapi daya saing pelayan di system juga perlu dikekalkan dan diasahkan. Dalam Projeck ini, sistem antrian di Poliklinik dipilih sebagai tempat penilaian untuk mengenalpasti sistem perkhidmatan sekarang. Tujuan kajian ini adalah untuk menyesuaikan model Antrian dan kaedah simulasi pada aliran sistem Poliklinik Ayer Keroh untuk mengenalpasti masa perkhidmatan kini disediakan oleh poliklinik mampu memenuhi tahap kedatangan pesakit masuk sistem. Masa kedatangan dan perkhidmatan yang dikumpul akan dianalisis selanjutnya dan diterjemahkan ke dalam histogram dan menjalani Goodness of Fit-Test dengan menggunakan software statistik Minitab untuk pengenalan keluarga pengedaran. Dengan hasil analisis pengedaran keluarga, ia akan diterjemahkan dalam teori antrian dan simulasi. Hubungan sistem seperti jumlah rata-rata waktu dalam sistem (L), rata-rata waktu yang dihabiskan pada sistem pada pelanggan (w), dan penggunaan pelayan (ρ) dapat diungkapkan dalam perbincangan. Dari hasil perbincangan, hubungan sistem akan berbanding dengan hasil tinjauan yang mencerminkan dan menunjukkan tahap perkhidmatan yang diingini oleh pesakit terhadap poliklinik. Pengesahan dan validasi model perlu dilakukan sebelum model ini berbanding dengan piawai. Oleh itu, aliran dalam sistem boleh disimpulkan bahawa yang stabil. Penerapan model antrian dan simulasi dalam penilaian ini adalah dibenarkan. Cara dalam pembaikan sistem juga disarankan untuk meningkatkan kualiti perkhidmatan.
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ACKNOWLEDGEMENTS
I would like to extend my sincere thanks to my supervisor, Profesor Madya Dr Adi Saptari for his invaluable guidance and assistance throughout this project. I appreciate the knowledge and advise that was gained from my supervisor. He had given me valuable cooperation, assistance, support and suggestion during my project activities.
I deeply appreciate the Polyclinic Community Ayer Keroh for providing the opportunity to perform my research study in their treatment room area. I would like to express my gratitude to all of the patients in the polyclinic for giving full support when I was carried out the survey. Special thanks to Miss Gan, Senior of UTeM Thanks for her kindness and sincerity to help me and also their willingness to share their ideas and opinions in model developing.
Last but not least, I would like to thank my family and friends, who have supported me and motivated me to lead me from beginning of this project to the end of report submission.
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TABLE OF CONTENT
Declaration i
Approval ii
Abstract iii
Abstrak iv
Acknowledgement v
Table of Content vi
List of Figures x
List of Table xi
List of Abbreviations, Symbols, Nomenclatures xii
CHAPTER 1.INTRODUCTION 1
1.1 Background 1
1.2 Simulation 2
1.3 Polyclinic Community Ayer Keroh 3
1.4 Problem Statement 4
1.5 Objectives 4
1.6 Scope 4
1.7 Organization of Report 5
CHAPTER 2 LITERATURE REWIEW 7
2.1 Introduction 7
2.2 History of Queuing Theory 9
2.3 Queue Problem 10
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2.3.2 Variable Arrival Rate 11
2.3.3 Blocking 12
2.3.4 System Design 13
2.3.5 Bottleneck 13
2.4 Characteristic of Queuing System 13
2.5 Queuing Notation 16
2.6 Steady State Behavior of Infinite-Population Markovian Model 17
2.6.1 Single-Server Queues with Poison Arrivals & unlimited Capacity 18
2.6.2 Multi Server Queue 19
2.7 Simulation 20
2.7.1 The Power of Simulation 21
2.7.2 System 22
2.7.3 Model 23
2.7.4 Development of Simulation Software 25
2.8 Publication Queuing System in Service Industry 27
CHAPTER 3 METHODOLOGY 30
3.1 Introduction 30
3.2 Methodology Overview 32
3.2.1 Design Survey 32
3.2.2 Analysis of Distribution 32
3.2.3 Model Conceptualization 35
3.2.4 Model Translation 37
3.2.5 Verification and Validation 38
3.3 Results and Discussion 39
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CHAPTER 4: DATA COLLECTION AND MODEL DEVELOPMENT 40
4.1 Data Collection 40
4.1.1 Data Collection from Design Survey 40
4.1.2 Patient Arrival Time, Service Time Begin and End 42
4.2 Analysis of Distribution 43
4.2.1 Selecting Distribution Family 45
4.2.2 Morning Session 46
4.2.3 Afternoon Session 48
4.3 Model Conceptualization 49
4.3.1 Experiments Factors and Responses 49
4.3.2 Model Scope 50
4.3.3 Model Level of Detail 51
4.3.4 Assumption 51
4.3.5 Simplification 51
4.4 Model Translation 51
4.4.1 Queue Model (Queuing Theory) 51
4.4.1 Morning Session 52
4.4.2 Afternoon Session 52
4.4.2 Simulation model 53
4.5 Verification and Validation 55
4.5.1 Verification 55
4.5.2 Validation 56
CHAPTER 5: REULTS AND DISCUSSION 60
5.1 Results 60
5.1.1 Results of the collected data 61
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5.1.3 Results of the Simulated Model 62
5.2 Discussion 62
5.2.1 Discussion on the Collected Data 62
5.2.2 Discussion on the Queue Model 63
5.2.3 Discussion on the Simulation Model 63
5.2.4 Discussion of the Patient Satisfactory Level 63
5.2.5 Ways of Improvement in Polyclinic Performance 64
CHAPTER 6: CONCLUSION AND RECOMMENDATION 65
References 67
Appendix-1 FYP 1 Gantt Chart Appendix-2 FYP 2 Gantt Chart Appendix-3 Questionnaire
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LIST OF FIGURES
1.1 The Basic Queuing Process 2
2.1 Simple Queuing model 8
2.2 Sever center 2, with c = 3 parallel servers 16
2.3 Multiserver Queuing System 20
3.1 Flow Chart of the Project Plan 31
3.2 Methodology flow chart of steps, method and expected results gain in
this research 34
3.3 Sample negative Exponential Distribution 33
3.4 Sample Poison Distribution 33
3.5 Framework for Conceptual Modelling 35
3.6 Activity Cycle for Single Server Queue 36
3.7 Process Flow Diagram for Single Server Queue 38
4.1 Process Flow diagram of the patient in polyclinic 41 4.2 Logic Flow Diagram for a single queue server in Polyclinic 42 4.3 Histogram of Inter-arrival at Emergency Room 47 4.4 Histogram of Service Time at Emergency Room 47 4.5 Goodness of Fit-Test Service Time at Emergency Room 48 4.5 Polyclinic Outpatient Department Simulation model 54
4.6 Simulation Model for Morning Session 54
4.7 Simulation model for Afternoon session 55
4.8 Validated data for morning session 57
4.9 Invalidated data for Afternoon session 57
4.10 Power curve for morning session 58
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LIST OF TABLE
2.1 Queuing Notation for Parallel Server Systems 17
2.2 Steady-State Parameter of M/G/1 Queue 18
2.3 Steady-State Parameter of M/M/1 Queue 19
2.4 Steady-State Parameter of M/M/∞ Queue 20
2.5 Development of the Simulation Software 26
2.6 Summary of the Publication of Queuing Theory 27
3.1 Sample of the table for data collection 33
3.2 Component List 36
4.1 Sample data collected for Treatment Room A 43 4.2 Surveyed Result of � , μ , WQ, Wn in morning session 44 4.3 Surveyed Result of � , μ , Wn in Afternoon session 44 4.4 Surveyed Result of Mean Time of � , μ , wQ, WQ, Wn for both session 44 4.5 Distribution for Inter-arrival & Service Time in Morning Session 46 4.6 Distribution for Inter-arrival & Service Time in Afternoon Session 48
4.7 Model Scope 49
4.8 Model Level of Detail 50
4.9 Analysis of M/M/1 in morning session 52
4.10 Analysis of M/M/1 in Afternoon session 52
4.11 Summary Process Input 53
4.12 Total Time spent in the system of 10 replications 56 4.13 Total Time Spent in the System from the collected data 57 5.1 Result of Survey of Patients Satisfactory Level 61 5.2 Results of μ, wQ, Wn from collected data 61 5.3 Results of wQ, and LQ from Queuing Model 61 5.4 Results of wQ, LQ, μ and Wn from Queuing Model 62
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LIST OF ABBREVIATIONS, SYMBOLS,
NOMENCLATURES
A/B/c/N/K - Notational system for parallel server systems A - Represents inter-arrival time distribution An - Inter-arrival time between customer n-1 and n
B - Represents the service-time distribution c - Represents the number of parallel servers D - Constant or deterministic
DES - Discrete Event Simulation Ek - Erlang of order k
FIFO - First-in-First-Out FYP - Final Year Project
G - Arbitrary or general
GI - General independent
H - Hyperexponential
K - Represents the size of the calling population LIFO - Last-In-Last-Out
M - Exponential or Markov
N - Represents the system capability OPD - Outpatient Department
PH - phase-type
Pn - Steady-state probability of having n customers in system
Pn(t) - Probability of n customers in system at time t
PR - Priority
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λe - Effective arrival rate μ - Service rate of one server ρ - Server utilization
Sn - service time of the nth arriving customer
Wn - Total time spent in the system by the nth arriving customer
WQ
n- Total time spent waiting in queue by customer n
L(t) - The number of customers in system at time t LQ(t)- The number of customers in queue at time t
L - Long-run time-average number of customers in system LQ - Long-run time-average number of customers in queue
W - Long-run average time spent in system per customer wQ - Long-run average time spent in queue per customer
� - Mean
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CHAPTER 1
INTRODUCTION
1.1 Background
Queuing is a daily practices in human life. Queue existence usually when peoples wait to get services provided from the server. There are several characteristic or types of queue, which is in virtual or physical form. Virtual queuing is usually providing a waiting area or room with seat, whereby the person in queue is required to remember his place in the queue system, or take a ticket with a number from a machine. These types of queue typically are found at hospital, government department and etc. A queue may long or short. A long waiting queue is a wastages and non-value-added phenomenon. Therefore, many researches are interested in queue behavior study to overcome or minimize this unpleasant circumstance. One of the ways to understand the queue behavior is via queue model.
A simple queuing model consist 3 main related fields which are population of potential customer, waiting line customer and server. The waiting line of customers where are came from a calling potential population of potential customers being entertained or served by server. In a simple expression, customers from time to time and join in a queue, are eventually served and finally leave the system. The term of customers refers to any type of entity that can be viewed as requesting service from a system. Therefore, many service facilities, production system, repair and maintenance facilities, communication and computer systems and transport and material-handling system can be view as queuing systems. In the queuing system it may congested with the buffer of material or waiting customer in restaurant at the bottleneck area or at the counters.
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There are several approaches to solve or ease queuing system which is in terms of mathematically or simulation model. In illustrating the queuing system behavior by using queuing model, there is several criteria need to take consideration before implement it. The consisting criteria are input sources, type of queue, queue discipline practice in the system, service mechanism, service time, system capacity and the queuing terminology & notation. Once these criteria are obtained, long run measure performance of queuing systems could be carried out by using the formulae. The relation of the system like time-average number in system (L), average time spent in system per customer (w), and server utilization (ρ) could be revealed. Figure 1.1 shows the illustration of basic queuing process.
Figure 1.1The Basic Queuing Process
1.2 Simulation
Simulation is a powerful and useful tool for designing and evaluating the performance of queuing systems. Typical measurement of system performance including server utilization (% of time of a server is busy), length of waiting line, client in the waiting lines, and delays of the customer. There are two aspects of consideration when attempting to improve a simulation which is analyst trading offs between server utilization and customer satisfaction in terms of line lengths and delay. In a high competency era, most of the sector is facing the challenge of quickly designing and implementing complex production and service system that are capable of meeting the growing demands for quality, delivery, affordability and service. With recent advances in computing and software technology, simulation is a powerful
Queue Service Mechanism Input
Source
Customers
Served Customers Queuing System
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tools and technology for systems study and improvement. Simulation is an animation of the system study by imitating actual system characteristic that exhibit event which takes place over time.
Application of simulation is very vast. Simulation is being used to study systems in the design stage, before such systems are built. Simulation modeling consist two main usage which act as an analysis tool for predicting the effect of changes to existing systems and as a design tool to predict the performance of new systems under varying sets of circumstances. In some Instances, a model can be developed which is simple enough to be “tackle” by mathematical methods or other mathematic techniques. The solution usually consist one or more numerical parameters which are called measures of performance of the system. It is because of Simulation is capable predict the performance accurately so it is being widely applied in manufacturing industries, wafer fabrication, construction engineering & project management, business processing, military, logistics, hospital or health system, transportation & distribution, and etc.
1.3 Polyclinic Community Ayer Keroh
This project will study about polyclinic service at Malacca. There are two policlinics community services in Malacca which are policlinic community Peringgit and polyclinic community Ayer Keroh. The area of the study is at polyclinic community Ayer Keroh, Melaka. Polyclinic community Ayer Keroh was launch on 8th of July 2003 by the Chief Ministry of Malacca, YB Datuk Wira Mohd Ali bin Mohd Rustam. The Director of the polyclinic is Dr Hj. Jamal bin Ali Johari. This policlinic is supervision by Dr Ismail Saleh who is health officer of Daerah Melaka Tengah Bukit Baru. Currently, the patient numbers at polyclinic has increased. The ratio of the patient to the doctor is typically high. Each of the doctors needs to serve approximate 50 patients each day. It decreases the efficiency of the service which causes patient wait too long for getting service.
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1.4 Problem Statement
In recent year the demand for health care services all over the world has risen. This phenomenon occurred due to ageing population has increase, this kind of situation is expected continue into the future. This phenomenon also happened in Malacca area. Meanwhile, the number of resources such as nurse, doctor, dentist, bed, space and etc is very limited to be fulfilled the need of current market. Therefore, this situation increase server workload so that bottleneck situation might occur. There is a need to analyse the level of service in polyclinic so as to find mechanisms by which improvements in service efficiency and cost effectiveness, without reducing patient care. This is suit with government’s effort to upgrade the healthcare industry in encouraging us to do research and development (R&D).
1.5 Objectives
1. To identify the customer service level at Polyclinic Community Ayer Keroh. 2. Model and simulate the Queues System in Polyclinic Community Ayer
Keroh.
3. Propose improvement of queue system based on the ideal time of patient willing to experience in waiting for service provided in Polyclinic Community Ayer Keroh.
1.6 Scope
Since the service provided in the polyclinic is very wide. In this study will explore the outpatient department. Each of the department is provided with the waiting area and space for the patient. The study will concern the patient who desired to get the consultancy service in the outpatient department (OPD). A set of interviews to the patients in determining the customer services level at polyclinic will be done in this research. Moreover, the ideal time for the patient wait in the system will be determined.
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1.7 Organization of Report
Basically this report consists of 5 chapters which included Introduction, Literature Review, Methodology, Results and Discussion, Last chapter is Conclusion which adds on with suggestion or recommendation for future study. The summary of each chapter contents is briefed as below:
Chapter 1: Introduction
This Chapter briefly explained about the background of the study, background information of the hospital in concern, problem statement of the study, objective of this project, scope which is covered in this report and the structure of organization report.
Chapter 2: Literature Review
All the theory applied in this research will be covered in this chapter. Such as Queuing theory and Simulation theory will be discuss further in this chapter. Moreover, the journal or article of current application of Queuing Theory and Simulation Theory in service industry will be discussed in this chapter as well.
Chapter 3: Methodology
This Chapter is concerned about the methods and techniques which will be carried out in this project for the process of conducting the study. The methods and technique used are explained in this chapter.
Chapter 4: Model Development and Data Collection
In this chapter, the conceptual model will be developed. Assumptions regarding in this simulation project also presented in this chapter. The steps to develop queuing model via calculation and simulation model via WITHNESS software are shown in this chapter. Moreover, the Fitness-Test for the data collected will be illustrated in this chapter via manual calculation and MiniTab software.
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Chapter 5: Results and Discussion
All the quantitative and qualitative findings of the study in this project are recorded in this chapter. The data from results parts, finding obtained from the results are evaluated in this part. Discussion also includes the performances of the current and proposed improved model.
Chapter 6: Conclusion and Recommendation
This chapter is final part of the report. Summarizes of important findings of the study will be stated in this section. Recommendations for future work in this area are also included in this chapter.
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CHAPTER 2
LITERATURE REVIEW
2.1 Introduction
Federick S.H and Gerald J.L, stated, “Queuing Theory is the study of waiting in all these various guises. The queuing model is to represent the various types of queuing systems that arise in practice. Formulas for each model indicate how the corresponding queuing system should perform, including the average amount of waiting that will occur, under a variety of circumstances.” From the analysis of Jerry Banker et al “Queuing model, whether solved mathematically or analyzed through simulation, provide the analyst with a powerful tool for designing and evaluating the performance of queuing systems. Typical measures of system performance include server utilization (percentage of time a server is busy), length of waiting lines, and delays of customers. Quite often, when designing or attempting to improve a queuing system, the analyst (or decision maker) is involved in tradeoffs between server utilization and customer satisfaction in term of lines length and delays. Queuing theory and simulation analysis are used to predict these measures of system performance as a function of the input parameters. The input parameters include the arrival rate of customers, the service demands of customers, the rate at which a server works, and the number and arrangement of servers.” Figure 2.1 illustrate the simple queuing model.
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Figure 2.1 Simple queuing model
A considerable body of research has shown that queuing theory can be useful in real- world healthcare situations, and some reviews of this work have appeared. McClain (1976) reviews research on models for evaluating the impact of bed assignment policies on utilization, waiting time, and the probability of turning away patients. Nosek and Wilson (2001) review the use of queuing theory in pharmacy applications with particular attention to improving customer satisfaction. Customer satisfaction is improved by predicting and reducing waiting times and adjusting staffing. Preater (2002) presents a brief history of the use of queuing theory in healthcare and points to an extensive bibliography of the research that lists many papers (however, it provides no description of the applications or results). Green (2006a) presents the theory of queuing as applied in healthcare. She discusses the relationship amongst delays, utilization and the number of servers; the basic M/M/s model, its assumptions and extensions; and the applications of the theory to determine the required number of servers.
Queuing models and simulation models each have their advantages. It is clear that queuing models are simpler, require less data, and provide more generic results than simulation However, discrete-event simulation permits modelling the details of complex patient flows. Jacobson et al. (2006) present a list of steps that must be done carefully to model each healthcare scenario successfully using simulation and warn about the slim margins of tolerable error and the effects of such errors in lost lives. Tucker et al. (1999) and Kao and Tung (1981) use simulation to validate, refine or otherwise complement the results obtained by queuing theory.
Calling population of potential customers
Waiting line of
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2.2 History of Queuing Theory
From the research of Encyclopedia of American Industries, below is the summary of the history queuing theory: “The first to develop a viable queuing theory was the French mathematician S.D. Poisson (1781-1840). Poisson created a distribution function to describe the probability of a prescribed outcome after repeated iterations of independent trials. Because Poisson used a statistical approach, the distributions he used could be applied to any situation where excessive demands are made on a limited resource.
The most important application of queuing theory occurred during the late 1800s, when telephone companies were faced with the problem of how many operators to place on duty at a given time. At the time, all calls were switched manually by an operator who physically connected a wire to a switchboard. Each customer required the operator only for the few seconds it took to relay directions and have the plug inserted and the time recorded. After the call was set up, the operator was free to accept another call. The problem for an early telephone traffic engineer was how many switchboards should be set up in an area.
Beyond that, supervisors were faced with the problem of how many operators to keep on the boards. Too many, and most operators would remain idle for minutes at a time. Too few, and operators would be overwhelmed by service requests, perhaps never catching up until additional help was added.
Often, callers who were unable to gain an operator's attention simply hung up in frustration and, suspecting it was a busy time for the operators, would wait several minutes before trying again. Others stayed on the line, waiting their turn to talk to the operator. Yet others would call repeatedly, hoping the operator would be sufficiently annoyed by repeated calls to serve them next.
These behavioral discrepancies caused problems for traffic engineers because they affected the level of demand for service from an operator. A call turned away was lost, not to come back until much later, and was effectively out of the system. Callers
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4 1.4 Problem Statement
In recent year the demand for health care services all over the world has risen. This phenomenon occurred due to ageing population has increase, this kind of situation is expected continue into the future. This phenomenon also happened in Malacca area. Meanwhile, the number of resources such as nurse, doctor, dentist, bed, space and etc is very limited to be fulfilled the need of current market. Therefore, this situation increase server workload so that bottleneck situation might occur. There is a need to analyse the level of service in polyclinic so as to find mechanisms by which improvements in service efficiency and cost effectiveness, without reducing patient care. This is suit with government’s effort to upgrade the healthcare industry in encouraging us to do research and development (R&D).
1.5 Objectives
1. To identify the customer service level at Polyclinic Community Ayer Keroh.
2. Model and simulate the Queues System in Polyclinic Community Ayer
Keroh.
3. Propose improvement of queue system based on the ideal time of patient willing to experience in waiting for service provided in Polyclinic Community Ayer Keroh.
1.6 Scope
Since the service provided in the polyclinic is very wide. In this study will explore the outpatient department. Each of the department is provided with the waiting area and space for the patient. The study will concern the patient who desired to get the consultancy service in the outpatient department (OPD). A set of interviews to the patients in determining the customer services level at polyclinic will be done in this research. Moreover, the ideal time for the patient wait in the system will be determined.
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5 1.7 Organization of Report
Basically this report consists of 5 chapters which included Introduction, Literature Review, Methodology, Results and Discussion, Last chapter is Conclusion which adds on with suggestion or recommendation for future study. The summary of each chapter contents is briefed as below:
Chapter 1: Introduction
This Chapter briefly explained about the background of the study, background information of the hospital in concern, problem statement of the study, objective of this project, scope which is covered in this report and the structure of organization report.
Chapter 2: Literature Review
All the theory applied in this research will be covered in this chapter. Such as Queuing theory and Simulation theory will be discuss further in this chapter. Moreover, the journal or article of current application of Queuing Theory and Simulation Theory in service industry will be discussed in this chapter as well.
Chapter 3: Methodology
This Chapter is concerned about the methods and techniques which will be carried out in this project for the process of conducting the study. The methods and technique used are explained in this chapter.
Chapter 4: Model Development and Data Collection
In this chapter, the conceptual model will be developed. Assumptions regarding in this simulation project also presented in this chapter. The steps to develop queuing model via calculation and simulation model via WITHNESS software are shown in this chapter. Moreover, the Fitness-Test for the data collected will be illustrated in this chapter via manual calculation and MiniTab software.
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Chapter 5: Results and Discussion
All the quantitative and qualitative findings of the study in this project are recorded in this chapter. The data from results parts, finding obtained from the results are evaluated in this part. Discussion also includes the performances of the current and proposed improved model.
Chapter 6: Conclusion and Recommendation
This chapter is final part of the report. Summarizes of important findings of the study will be stated in this section. Recommendations for future work in this area are also included in this chapter.
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CHAPTER 2
LITERATURE REVIEW
2.1 Introduction
Federick S.H and Gerald J.L, stated, “Queuing Theory is the study of waiting in all these various guises. The queuing model is to represent the various types of queuing systems that arise in practice. Formulas for each model indicate how the corresponding queuing system should perform, including the average amount of waiting that will occur, under a variety of circumstances.” From the analysis of Jerry
Banker et al “Queuing model, whether solved mathematically or analyzed through
simulation, provide the analyst with a powerful tool for designing and evaluating the performance of queuing systems. Typical measures of system performance include server utilization (percentage of time a server is busy), length of waiting lines, and delays of customers. Quite often, when designing or attempting to improve a queuing system, the analyst (or decision maker) is involved in tradeoffs between server utilization and customer satisfaction in term of lines length and delays. Queuing theory and simulation analysis are used to predict these measures of system performance as a function of the input parameters. The input parameters include the arrival rate of customers, the service demands of customers, the rate at which a server works, and the number and arrangement of servers.” Figure 2.1 illustrate the simple queuing model.
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Figure 2.1 Simple queuing model
A considerable body of research has shown that queuing theory can be useful in real- world healthcare situations, and some reviews of this work have appeared. McClain (1976) reviews research on models for evaluating the impact of bed assignment policies on utilization, waiting time, and the probability of turning away patients. Nosek and Wilson (2001) review the use of queuing theory in pharmacy applications with particular attention to improving customer satisfaction. Customer satisfaction is improved by predicting and reducing waiting times and adjusting staffing. Preater (2002) presents a brief history of the use of queuing theory in healthcare and points to an extensive bibliography of the research that lists many papers (however, it provides no description of the applications or results). Green (2006a) presents the theory of queuing as applied in healthcare. She discusses the relationship amongst delays, utilization and the number of servers; the basic M/M/s model, its assumptions and extensions; and the applications of the theory to determine the required number of servers.
Queuing models and simulation models each have their advantages. It is clear that queuing models are simpler, require less data, and provide more generic results than simulation However, discrete-event simulation permits modelling the details of complex patient flows. Jacobson et al. (2006) present a list of steps that must be done carefully to model each healthcare scenario successfully using simulation and warn about the slim margins of tolerable error and the effects of such errors in lost lives. Tucker et al. (1999) and Kao and Tung (1981) use simulation to validate, refine or otherwise complement the results obtained by queuing theory.
Calling population of potential customers
Waiting line of
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9 2.2 History of Queuing Theory
From the research of Encyclopedia of American Industries, below is the summary of the history queuing theory: “The first to develop a viable queuing theory was the French mathematician S.D. Poisson (1781-1840). Poisson created a distribution function to describe the probability of a prescribed outcome after repeated iterations of independent trials. Because Poisson used a statistical approach, the distributions he used could be applied to any situation where excessive demands are made on a limited resource.
The most important application of queuing theory occurred during the late 1800s, when telephone companies were faced with the problem of how many operators to place on duty at a given time. At the time, all calls were switched manually by an operator who physically connected a wire to a switchboard. Each customer required the operator only for the few seconds it took to relay directions and have the plug inserted and the time recorded. After the call was set up, the operator was free to accept another call. The problem for an early telephone traffic engineer was how many switchboards should be set up in an area.
Beyond that, supervisors were faced with the problem of how many operators to keep on the boards. Too many, and most operators would remain idle for minutes at a time. Too few, and operators would be overwhelmed by service requests, perhaps never catching up until additional help was added.
Often, callers who were unable to gain an operator's attention simply hung up in frustration and, suspecting it was a busy time for the operators, would wait several minutes before trying again. Others stayed on the line, waiting their turn to talk to the operator. Yet others would call repeatedly, hoping the operator would be sufficiently annoyed by repeated calls to serve them next.
These behavioral discrepancies caused problems for traffic engineers because they affected the level of demand for service from an operator. A call turned away was lost, not to come back until much later, and was effectively out of the system. Callers