1. The document discusses discrete event simulation and its components. Discrete event simulation models systems as they change states at discrete points in time. It involves events, state variables, event lists, and statistical counters.
2. The programming of discrete event simulation involves components like the system state, simulation clock, event list, statistical counters, initialization routine, timing routine, event routines, and report generator. The timing routine determines the next event and advances the clock, while event routines update the system state.
3. An example Fortran program for simulating a single-server queueing system is presented. It involves subroutines for initialization, timing, arrival and departure events, and reporting. Key variables include event time,
Materi yang ada pada slide ini berisi :
Penjelasan umum activity diagram
Notasi & semantic
Starting activity
Activity & action
Activity frame
Decisions & merge
Fork & join
Time event
Activity partition (swimlanes)
Subactivity
Objects
Signalconnector
Expansion regions
Interrupt
Ending activity
----------------------------------------------------------------------
Keep in touch with me in :
Twitter : https://twitter.com/rizkiadam_1991
Email : rizkiadamunikom@gmail.com
IG : @rizkiadamkurniawan
Materi yang ada pada slide ini berisi :
Penjelasan umum activity diagram
Notasi & semantic
Starting activity
Activity & action
Activity frame
Decisions & merge
Fork & join
Time event
Activity partition (swimlanes)
Subactivity
Objects
Signalconnector
Expansion regions
Interrupt
Ending activity
----------------------------------------------------------------------
Keep in touch with me in :
Twitter : https://twitter.com/rizkiadam_1991
Email : rizkiadamunikom@gmail.com
IG : @rizkiadamkurniawan
Berikut adalah contoh tugas besar mata kuliah pemodelan sistem, pada tugas besar ini dipaparkan langkah-langkah yang dibutuhkan untuk memodelkan suatu permasalahan yang dihadapi perusahaan ke dalam model matematis.
Mata Kuliah: Model dan Simulasi
Pertemuan: 1 sampai 4
Jurusan: Teknologi Informasi
Kampus: STMIK Swadharma
Sumber Gambar:
Huskmitnavn1 (2017), "3D Drawings.", dari https://huskmitnavn.dk/blogs/projects/3d-drawings, diakses 16/11/2018.
Itk Engineering (2018), "Make the Real World Manageable – with Models and Simulations", dari https://www.itk-engineering.de/en/development-partnership-competencies/modeling-simulation/, diakses 16/11/2018.
Wildstrom, Steve (2012), "In Praise of Old-fashioned PCs", dari https://techpinions.com/in-praise-of-old-fashioned-pcs/12039, diakses 16/11/2018.
____ (2018), "Trik Mengocok Kartu seperti Pesulap Profesional", dari https://www.youtube.com/watch?v=5jCInqwev_g, diakses 16/11/2018.
____ (2014), "Energi 6 Sisi Dadu", dari https://shellyashahab.wordpress.com/2014/06/18/energi-6-sisi-dadu/, diakses 16/11/2018.
Topik keenam perkuliahan Perancangan Sistem Kerja dan Ergonomi mengenai Pengukuran Waktu Kerja secara tidak langsung. Bagian pertama mengupas metode MTM
Berikut adalah contoh tugas besar mata kuliah pemodelan sistem, pada tugas besar ini dipaparkan langkah-langkah yang dibutuhkan untuk memodelkan suatu permasalahan yang dihadapi perusahaan ke dalam model matematis.
Mata Kuliah: Model dan Simulasi
Pertemuan: 1 sampai 4
Jurusan: Teknologi Informasi
Kampus: STMIK Swadharma
Sumber Gambar:
Huskmitnavn1 (2017), "3D Drawings.", dari https://huskmitnavn.dk/blogs/projects/3d-drawings, diakses 16/11/2018.
Itk Engineering (2018), "Make the Real World Manageable – with Models and Simulations", dari https://www.itk-engineering.de/en/development-partnership-competencies/modeling-simulation/, diakses 16/11/2018.
Wildstrom, Steve (2012), "In Praise of Old-fashioned PCs", dari https://techpinions.com/in-praise-of-old-fashioned-pcs/12039, diakses 16/11/2018.
____ (2018), "Trik Mengocok Kartu seperti Pesulap Profesional", dari https://www.youtube.com/watch?v=5jCInqwev_g, diakses 16/11/2018.
____ (2014), "Energi 6 Sisi Dadu", dari https://shellyashahab.wordpress.com/2014/06/18/energi-6-sisi-dadu/, diakses 16/11/2018.
Topik keenam perkuliahan Perancangan Sistem Kerja dan Ergonomi mengenai Pengukuran Waktu Kerja secara tidak langsung. Bagian pertama mengupas metode MTM
Java application monitoring with Dropwizard Metrics and graphite Roberto Franchini
Java application monitoring with Dropwizard Metrics and graphite.
How to correlate system monitoring and application monitoring using graphite as backend for Collectd and application metrics.
[EUC2016] FFWD: latency-aware event stream processing via domain-specific loa...Matteo Ferroni
Tools and applications for event stream processing and real-time analytics are getting a huge hype these days on a wide range of application scenarios, from the smallest Internet of Things (IoT) embedded sensor to the most popular Social Network feed. Unfortunately, dealing with this kind of input rises some issues that can easily mine the real-time analysis requirement due to an unexpected overload of the system; this happens as the processing time may strongly depend on the single event content, while the event arrival rate may vary unpredictably over time. In this work, we propose Fast Forward With Degradation (FFWD), a latency-aware load shedding framework that exploits performance degradation techniques to adapt the throughput of the application to the size of the input, allowing the system to have a fast and reliable response time in case of overloading. Moreover, we show how different domain-specific policies can guarantee a reasonable accuracy of the aggregated output metrics.
Full paper: http://ieeexplore.ieee.org/document/7982234/
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
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Dr. Sean Tan, Head of Data Science, Changi Airport Group
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LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
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During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
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What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
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• Communication Mining Overview
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• How can it help today’s business and the benefits
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https://arxiv.org/abs/2306.08302
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https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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2. Simulasi sistem terdiri dari :
1. Simulasi Sistem Diskrit, bila hanya didefinisikan pd titik-titik
waktu tertentu :
a. Kejadian Diskrit (Event Based Simulation)
b. Pendekatan Aktivitas (Activity Based Simulation)
c. Process Interaction Approach
d. Three Phase Approach
2. Simulasi Sistem Kontinyu, merupakan fungsi kontinyu dari
waktu :
a. Model Sistem Kontinyu :
- Sistem Statis state variables independen terhadap waktu
- Sistem Dinamis state variables merupakan fungsi waktu
b. Sistem umpan balik
c. Metode analog
3. Software Simulasi
Secara kasar software tools utk membangun discrete event
simulation dapat dikategorikan kedlm empat kategori :
1. General purpose languages
– C, Pascal, FORTRAN, C++, ADA, Java, dll.
2. Event Scheduled Simulation Languages
– SLAM, SIMAN, SIMPAS, SIM++, JAVASIM, dll,
3. Process Oriented Simulation Languages
– CSIM, EZSIM, GPSS, SIMAN, SLAM, GASP, JAVASIM dll.
4. Application Oriented Simulators
– Opnet, Comnet III, Tangram II, ns-2, Qualnet, Jade, dll.
Software tools utk membangun kontinyu simulation dapat
dikategorikan kedlm : VENSIM, SEESIM, Dynamo dll
4. Bagaimana Simulasi Diskrit Berjalan……..
(Programming Simulation)
– Time-driven: simulasi berjalan pd interval waktu
tertentu/fixed (mis. state ditentukan pada saat t, t + ∆t,
t + 2 ∆t, …)
Time-based simulation
– Event-driven: simulasi berjalan dari event-ke-event
(mis. state ditentukan pd titik waktu dari event
berikutnya)
Event-based simulation
1. Simulasi Diskrit
5. Contoh :
Mensimulasikan Discrete Event System (Event Approach)
pendekatan kejadian yakni pengembangan model simulasi didasarkan
pd adanya kejadian yg terjadi pd sistem
• States
Kumpulan variabel-variabel yg diperlukan utk karakterisasi sistem pada
sembarang titik waktu
• Entities
Objek-objek yg diproses dalam simulasi – mis. packet atau panggilan
telepon
• Attributes
Karakteristik dari entities (mis., panjang paket, tipe dan tujuan)
• Resources
Substansi/items dimana entities menduduki atau menggunakan (mis., buffer
space pd router, tokens pd FDDI network, bandwidth pd suatu link)
• Activities
Durasi waktu dimana panjangnya diketahui saat dimulai. Misalnya, waktu
transmisi dari suatu paket pd suatu link
• Delay
Durasi waktu dg panjang yg tdk terspesifikasi yg tdk diketahui sebelum
selesai. misalnya – waktu perjalanan suatu paket dari node A ke node B dlm
suatu jaringan
6. • Utk mengimplementasikan suatu event scheduling simulasi
perlu membangun suatu event list, secara tdk langsung perlu
membangun suatu deretan events
• Perhatikan contoh antrian Single Server (M/M1) – misalnya
pada proses permesinan.
• Ada EVENT bila ada job Datang dan Pergi (selesai di proses)
• Ada ACTIVITY bila job tsb di Proses`
A M
Job in servise
Job AntriArrive
Departure
Batas Sistem
Mesin/Proses
D
• Model
– IID Exponential interarrival time dg mean 1/λ
– IID Exponential service time dg mean 1/µ
• Ingin mendapatkan mean job delay dlm antrian
– Solusi analitis mean waiting time = ρ/ (µ - λ)
7. Adapun representasi komputasinya sbb :
Misal data waktu kedatangan (A) dan waktu permesinan (M) :
A1=55 A2=32 A3=24 A4=40 A5=12 A6=29 …… dstnya
S1=43 S2=36 S3=34 …..dstnya
Catatan :
data2 tsb diperoleh dari hasil Pembangkitan Bilangan Random
(Random Number Generator) berdasarkan distribusi kedatangan
dan proses (pelayanan) Minggu depan akan di jelaskan !!!
0 t1 t2 t3C1 C2 time
s1 s2 s3s0 s4 s5
A1 A2 A3
S1 S2
Illustrasi sistem antrian M/M/1 dengan pendekatan event time
8. Keterangan :
ti : wkt kedatangan customer ke i (to = 0)
Ai = ti – ti-1 = antar wkt kedatangan antara (i-1) dan customer ke I
Si : time server actually spends serving customer ke i (termasuk customer
delay pd antrian)
Di : delay in queue customer ke I
Ci : ti + Di + Si = wkt selesai melayani customer ke i dan pergi dari sistem
A1 datang pd wkt simulasi menit ke 55
A2 datang pd wkt simulasi menit ke 32 kemudian atau menit ke 87 (55 + 32) dst
A1 dilayani selama 43 menit (S1), selesai pd menit 98 (55 + 43) sehingga A2 yg
datang pd menit ke 87 menunggu hingga menit 98 (selama 11 menit) atau
menunggu hingga A1 selesai dilayani
A2 mulai dilayani pada menit ke 98 selama 36 menit (S2) dan selesai pd menit ke
134 (98 + 36) …..dstnya
9. Time = 0
System
System State
Status Number in Time of Last
Queue Event
Time of Arrival
A
D
Clock Event List
Statistical Counters
Number Total Area
delay delay under Q(t)
0
55
0 (8.00)
0 0 0
0 0 0
S
Time = 55
System
System State
Status Number in Time of Last
Queue Event
Time of Arrival
A
D
Clock Event List
Statistical Counters
Number Total Area
delay delay under Q(t)
Time = 87 System State
Status Number in Time of Last
Queue Event
Time of Arrival
A
D
Clock Event List
Statistical Counters
Number Total Area
delay delay under Q(t)
S
S
1
1
0
1
55
87
55
1
87
1
0
0
0
0
98
87
98
111
87
1
customer
2
customer
1
10. Time = 98
System
System State
Status Number in Time of Last
Queue Event
Time of Arrival
A
D
Clock Event List
Statistical Counters
Number Total Area
delay delay under Q(t)
98
111
134
2 11 11
1 0 98
S
Time = 111 System State
Status Number in Time of Last
Queue Event
Time of Arrival
A
D
Clock Event List
Statistical Counters
Number Total Area
delay delay under Q(t)
Time = 134
System
System State
Status Number in Time of Last
Queue Event
Time of Arrival
A
D
Clock Event List
Statistical Counters
Number Total Area
delay delay under Q(t)
S
S
1
1
1
0
11
1
134
11
1
2
134
3
11
34
11
34
111 134
151
168
151
2
customer
3
customer
2
customer
3
11. Time =
System
System State
Status Number in Time of Last
Queue Event
Time of Arrival
A
D
Clock Event List
Statistical Counters
Number Total Area
delay delay under Q(t)
S
Time = System State
Status Number in Time of Last
Queue Event
Time of Arrival
A
D
Clock Event List
Statistical Counters
Number Total Area
delay delay under Q(t)
Time =
System
System State
Status Number in Time of Last
Queue Event
Time of Arrival
A
D
Clock Event List
Statistical Counters
Number Total Area
delay delay under Q(t)
S
S
customer
customer
customer
12. Pemrograman Simulasi Diskrit
Komponen & Pengorganisasian Model Pemrograman
• System State
menggambarkan keadaan sistem pd suatu waktu tertentu
• Simulation Clock
Jam simulasi yang menentukan waktu terjadinya event
• Even List
Daftar yg memuat kejadian berikutnya
• Statistical Counter
variabel2 yg dipakai utk menghitung statistik (memberikan informasi
statistik tentang performance sistem)
• Initialization Routine
sub-rutin untuk memulai/menolkan waktu simulasi
• Timing Routine
sub-rutin utk menentukan event berikutnya dan melanjutkan jam
simulasi untuk event berikutnya
• Event Routine
sub-rutin utk meng-update state (keadaan) sistem ketika suatu
event terjadi
13. • Report Generator
sub-rutin estimasi perhitungan (dari statistical counter) dan mencetak
laporan
• Main Program
sub-program untuk memanggil timing routin untuk menentukan event
berikutnya dan meng-update keadaan sistem
Hubungan logical (flow control) dari masing-masing
komponen tersebut sebagai berikut :
14. Hubungan Logical (Flow Chart)
1. Set simulation clock = 0
2. Initialize system state and
statistical counters
3. Initialize event list
1. Call the timing routine
2. Call event routine i
1. Update system state
2. Update statistical counters
3. Generate future events and
add to the event list
1. Compute estimates of interest
2. Print Report
1. Determine the next
event type, misal i
2. Advance the
simulation clock
Is simulation
over ?
No
Yes
Initialization
Routine
Main
Program
Event
Routine i
Report
Generator
Timing Routine
Generate random
variates
Library Routine
15. Pemrograman (Bahasa Program FORTRAN) :
Beberapa asumsi tentang single-server queueing system yaitu waktu antar
kedatangan konsumen berdistribusi exponential, misal rata-rata 1 menit &
waktu pelayanan berdistribusi eksponential dgn rata-rata 0,5 menit. Simulasi
berhenti jika jumlah konsumen yg dilayani n = 1000 orang.
a. Event Deskripsi :
- Kedatangan / Arrival (A) = 1
- Kepergian / Departure (D) = 2
b. Sub – Routine :
Sub - Program Purpose
INIT
TIMING
ARRIVE
DEPART
REPORT
EXPON (RMEAN)
Initialisasi routine
Timing routine
Event routine (1)
Event routine (2)
Laporan simulasi (dipanggil ketika simulasi
selesai n = 1000)
Distribusi eksponential dgn rata-rata RMEAN
(λ = 1 dan μ = 0,5)
16. Definisi
MARRVT
MSERVT
TOTCUS
Waktu rata-rata antar kedatangan
Waktu pelayanan rata-rata
Jumlah total n yang selesai dilayani
c. Input Parameter :
d. Modeling variables :
Definisi
ANIQ
DELAY
NEVNTS
NEXT
NIQ
NUMCUS
RMEAN
RMIN
STATUS
TARRVL(1)
TIME
TLEVNT
TNE(1)
TOTDEL
U
Fungsi dari grafik jumlah dalam antrian
Lamanya customer dalam antrian
Jumlah event dalam simulasi (2 event yakni datang & pergi)
Jenis event yg terjadi berikutnya
Jumlah customer dalam antrian
Jumlah customer yg telah menyelesaikan waktu tunggunya
Rata-rata dari distribusi eksponential yg digunakan
Utk menyatakan event yg akan terjadi utk jangka yg masih lama
Status = 0 jika server idle dan staus = 1 jika sibuk
Waktu kedatangan customer ke I
Jam simulasi
Waktu kejadian terakhir
Wkt perubahan dari waktu sekarang ke waktu berikutnya
Total customer yg mengalami antrian
Variabel random berdistribusi uniform antara 0 dan 1 (utk
pembangkitan bilangan random)
17. Definisi
AVGDEL
AVGNIQ
Waktu antrian rata-rata
Panjang antrian rata-rata
e. Output Variables :
PEMROGRAMAN FORTRAN :
1. SUB-ROUTINE PROGRAM UTAMA
*** MAIN PROGRAM
INTEGER NEVNTS, NEXT, NIQ, NUMCUS, STATUS, TOTCUS
REAL ANIQ, MARRVT, MSERVT, TARRVL(100), TIME, TNE(2), TOTDEL
COMMON /MODEL/ ANIQ, MARVT, MSERVT, NEVNTS, NEXT, NIQ,
NUMCUS, STATUS, TIME, TLEVNT, TNE, TOTCUS, TOTDEL
*** SPECIFY THE NUMBER OF EVENT TYPES FOR THE TIMING ROUTINE
NEVNTS=2
*** READ INPUT PARAMETERS
REALD 10, MARRVT, MSERVT
10 FORMAT (2F, 10.0)
READ 20, TOTCUS
20 FORMAT (I 10)
Deklarasi
18. *** INITIALIZE THE SIMULATION
CALL INIT
*** DETERMINE THE NEXT EVENT
30 CALL TIMING
*** CALL THE APPROPRIATE EVENT ROUTINE
GO TO (40, 50), NEXT
40 CALL ARRIVE
GO TO 60
50 CALL DEPART
*** IF THE SIMULATION IS OVER, CALL THE REPORT GENERATOR AND END THE
*** SIMUALTION, IF NOT, CONTINUE THE SIMULATION
60 IF(NUMCUS.LT. TOTCUS) GO TO 30
CALL REPORT
STOP
END
19. 2. SUB-ROUTINE INITIALISATION :
SUBROUTINE INIT
INTEGER NEVNTS, NEXT, NIQ, NUMCUS, STATUS, TOTCUS
REAL ANIQ, MARRVT, MSERVT, TARRVL(1000), TIME, TLEVNT, TNE(2), TOTDEL
COMMON /MODEL/ ANIQ, MARRVT, MSERVT, NEVNTS, NEXT, NIQ, NUMCUS, STATUS,
1TARRVL, TIME, TLEVNT, TNE, TOTCUS, TOTDEL
*** INITIALIZE THE SIMULATION CLOCK
TIME=0
.
*** INITIALIZE THE ATATE VARIABLES
STATUS=0
NIQ=0
TLEVNT=0
*** INITIALIZE THE STATISTICAL COUNTERS
NUMCUS=0
TOTDEL=0
ANIQ=0
*** INITIALIZE THE EVENT LIST. SINCE NO CUSTOMERS ARE PRESENT, THE TIME OF THE
*** NEXT DEPARTURE (SERVICE COMPLETION) IS SET TO ‘INFINITY.’
TNE(1)=TIME+EXPON(MARRVT)
TNE(2)=1.E+30
RETURN
END
20. 3. SUB-ROUTINE TIMING :
SUBROUTINE TIMING
INTEGER NEVNTS, NEXT, NIQ, NUMCUS, STATUS, TOTCUS
REAL ANIQ, MARRVT, MSERVT, TARRVL(1000), TIME, TLEVNT, TNE(2), TOTDEL
COMMON /MODEL/ ANIQ, MARRVT, MSERVT, NEVNTS, NEXT, NIQ, NUMCUS, STATUS,
1TARRVL, TIME, TLEVNT, TNE, TOTCUS, TOTDEL
RMIN=1.E+29
NEXT=0
*** DETERMINE THE EVENT TYPE OF THE NEXT EVENT TO OCCUR
DO 10 I=1.NEVNTS
IF (TNE(I).E.RMIN) GO TO 10
RMIN=TNE(I)
NEXT=I
10 CONTINUE
.
*** IF THE EVENT LIST IS EMPTY (1.E., NEXT=0), STOP THE SIMULATION
*** OTHERWISE, ADVANCE THE SIMULATION CLOCK
IF (NEXT, GT.0) GO TO 30
PRINT 20
20 FORMAT (1h1, 5X, ‘EVENT LIST EMPTY’)
STOP
30 TIME=TNE(NEXT)
RETURN
END
21. FLOWCHART SUBROUTINE ARRIVE
Subroutine
ARRIVE
Schedule the next arrival
event
Set DELAY = 0 for this
customer and gather statistics
Add 1 to the number of
customers delayed
Make server busy
Schedule a departure
event for this customer
Return
Update area under the
number in queue function
Add 1 to the number in
queue
Store the time of arrival
of this customer
Return
22. 4. SUB-ROUTINE ARRIVE :
SUBROUTINE ARRIVE
INTEGER NEVNTS, NEXT, NIQ, NUMCUS, STATUS, TOTCUS
REAL ANIQ, MARRVT, MSERVT, TARRVL(1000), TIME, TLEVNT, TNE(2), TOTDEL
COMMON /MODEL/ ANIQ, MARRVT, MSERVT, NEVNTS, NEXT, NIQ, NUMCUS, STATUS,
1TARRVL, TIME, TLEVNT, TNE, TOTCUS, TOTDEL
***
23. FLOWCHART SUBROUTINE DEPART
Subroutine
DEPART
Update area under the
number in queue function
Subtract 1 from the number in
queue
Compute delay of
customer entering service
and gather statistics
Add 1 to the number of
customers delayed
Schedule a departure
event for this customer
Return
Make server busy
Set the time of the next
departure to infinity
Return
24. 5. SUB-ROUTINE DEPART :
SUBROUTINE DEPART
INTEGER NEVNTS, NEXT, NIQ, NUMCUS, STATUS, TOTCUS
REAL ANIQ, MARRVT, MSERVT, TARRVL(1000), TIME, TLEVNT, TNE(2), TOTDEL
COMMON /MODEL/ ANIQ, MARRVT, MSERVT, NEVNTS, NEXT, NIQ, NUMCUS, STATUS,
1TARRVL, TIME, TLEVNT, TNE, TOTCUS, TOTDEL
***
25. 6. SUB-ROUTINE REPORT :
SUBROUTINE REPORT
INTEGER NEVNTS, NEXT, NIQ, NUMCUS, STATUS, TOTCUS
REAL ANIQ, MARRVT, MSERVT, TARRVL(1000), TIME, TLEVNT, TNE(2), TOTDEL
COMMON /MODEL/ ANIQ, MARRVT, MSERVT, NEVNTS, NEXT, NIQ, NUMCUS, STATUS,
1TARRVL, TIME, TLEVNT, TNE, TOTCUS, TOTDEL
***
26. 7. SUB-ROUTINE FUNGSI EXPONENTIAL :
FUNCTION EXPON(RMEAN)
REAL RMEAN, U
*** GENERATE A U(0,1) RANDOM VARIABLE, THE FORM OF THIS STATEMENT DEPENDS
*** ON THE COMPUTER USED
U=RANUN (Z)
*** GENERATE AN EXPONENTIAL RANDOM VARIABLE WITH MEAN RMEAN
EXPON=-RMEAN*ALOG (U)
RETURN
END
8 . OUTPUT HASIL SIMULASI :
-------------------------------------------------------------------------
SINGLE-SERVER QUEUEING SYSTEM
REAL RMEAN, U
MEAN INTERARRIVAL TIME : 1.000 MINUTES
MEAN SERVICE TIME : .500 MINUTES
NUMBER OF CUSTOMERS : 1000
AVERAGE DELAY IN QUEUE : .497 MINUTES
AVERAGE NUMBER IN QUEUE : .500