Wil van der Aalst Eindhoven University of Technology

Slides:



Advertisements
Similar presentations
Techniques to analyze workflows (design-time)
Advertisements

1 Analysis of workflows : Verification, validation, and performance analysis. Wil van der Aalst Eindhoven University of Technology Faculty of Technology.
IE 429, Parisay, January 2003 Review of Probability and Statistics: Experiment outcome: constant, random variable Random variable: discrete, continuous.
Lab Assignment 1 COP 4600: Operating Systems Principles Dr. Sumi Helal Professor Computer & Information Science & Engineering Department University of.
Workflow Management Kap. 4. Analyzing Workflows Wil van der Aalst has copyrights to almost all figures in the following slideshow made by Lars Frank.
1 Chapter 8 Queueing models. 2 Delay and Queueing Main source of delay Transmission (e.g., n/R) Propagation (e.g., d/c) Retransmission (e.g., in ARQ)
Simulation of multiple server queuing systems
Queuing Analysis Based on noted from Appendix A of Stallings Operating System text 6/10/20151.
Queuing Systems Chapter 17.
1 Analysis of workflows a-priori and a-posteriori analysis Wil van der Aalst Eindhoven University of Technology Faculty of Technology Management Department.
1 Modeling workflows : The organizational dimension and alternative notations. Wil van der Aalst Eindhoven University of Technology Faculty of Technology.
To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 14-1 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Chapter 14.
Data Communication and Networks Lecture 13 Performance December 9, 2004 Joseph Conron Computer Science Department New York University
ELEN 602 Lecture 9 Review of last lecture Little’s Formula M/M/1 Queue
Simulating Single server queuing models. Consider the following sequence of activities that each customer undergoes: 1.Customer arrives 2.Customer waits.
Queuing Analysis Based on noted from Appendix A of Stallings Operating System text 6/28/20151.
Queuing. Elements of Waiting Lines  Population –Source of customers Infinite or finite.
To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 14-1 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Chapter 14.
Chapter 9: Queuing Models
Queuing Theory (Waiting Line Models)
Spreadsheet Modeling & Decision Analysis
Marlon Dumas marlon.dumas ät ut . ee
4/11: Queuing Models Collect homework, roll call Queuing Theory, Situations Single-Channel Waiting Line System –Distribution of arrivals –Distribution.
Capacity analysis of complex materials handling systems.
1 Queuing Analysis Overview What is queuing analysis? - to study how people behave in waiting in line so that we could provide a solution with minimizing.
Structure of a Waiting Line System Queuing theory is the study of waiting lines Four characteristics of a queuing system: –The manner in which customers.
1 QUEUES. 2 Definition A queue is a linear list in which data can only be inserted at one end, called the rear, and deleted from the other end, called.
Queuing Queues are a part of life and waiting to be served is never really pleasant. The longer people wait the less likely they are to want to come back.
1 Queuing Systems (2). Queueing Models (Henry C. Co)2 Queuing Analysis Cost of service capacity Cost of customers waiting Cost Service capacity Total.
1 Chapters 8 Overview of Queuing Analysis. Chapter 8 Overview of Queuing Analysis 2 Projected vs. Actual Response Time.
1 Analysis of workflows : Verification, validation, and performance analysis. Wil van der Aalst Eindhoven University of Technology Faculty of Technology.
CSCI1600: Embedded and Real Time Software Lecture 19: Queuing Theory Steven Reiss, Fall 2015.
Waiting Lines and Queuing Theory Models
Structure of a Waiting Line System Queuing theory is the study of waiting lines Four characteristics of a queuing system: –The manner in which customers.
Queuing Models.
Mohammad Khalily Islamic Azad University.  Usually buffer size is finite  Interarrival time and service times are independent  State of the system.
1 BIS 3106: Business Process Management (BPM) Lecture Nine: Quantitative Process Analysis (2) Makerere University School of Computing and Informatics Technology.
MTAT Business Process Management Lecture 6 – Quantitative Process Analysis II Marlon Dumas marlon.dumas ät ut. ee 1.
Simulation of single server queuing systems
Managerial Decision Making Chapter 13 Queuing Models.
Marlon Dumas marlon.dumas ät ut . ee
Queueing Theory What is a queue? Examples of queues:
Queuing Theory Non-Markov Systems
Introduction to Extend
Chapter 9: Queuing Models
B.Ramamurthy Appendix A
Birth-Death Process Birth – arrival of a customer to the system
Queuing Systems Don Sutton.
Introduction Notation Little’s Law aka Little’s Result
Wil van der Aalst Eindhoven University of Technology
Solutions Hwk Que3 1 The port of Miami has 3 docking berths for loading and unloading ships but is considering adding a 4th berth.
Wil van der Aalst Eindhoven University of Technology
System Performance: Queuing
MATS Quantitative Methods Dr Huw Owens
The M/G/1 Queue and others.
Workflow Management Systems: Functions, architecture, and products.
Wil van der Aalst Eindhoven University of Technology
Variability 8/24/04 Paul A. Jensen
Simulation Continuous Variables Simulation Continuous Vars
مهندسی مجدد فرآیندهای تجاری
Wil van der Aalst Eindhoven University of Technology
Queuing Theory By: Brian Murphy.
Queuing Analysis Two analytical techniques can be employed to study queuing processes: Shock wave analysis Demand-capacity process is deterministic Suited.
Workflow Management Systems: Functions, architecture, and products.
Solutions Hwk Que3 1 The port of Miami has 3 docking berths for loading and unloading ships but is considering adding a 4th berth.
24 February 2019 Model Antrian M/D/1.
Model Antrian M/M/s.
CSE 550 Computer Network Design
Waiting Line Models Waiting takes place in virtually every productive process or service. Since the time spent by people and things waiting in line is.
Queuing Models J. Mercy Arokia Rani Assistant Professor
Presentation transcript:

Analysis of workflows: Verification, validation, and performance analysis. Wil van der Aalst Eindhoven University of Technology Faculty of Technology Management Department of Information and Technology P.O. Box 513 5600 MB Eindhoven The Netherlands w.m.p.v.d.aalst@tm.tue.nl

نکات مهم فصل چهارم

نکات مهم فصل چهارم

نکات مهم فصل چهارم

نکات مهم فصل چهارم

نکات مهم فصل چهارم

نکات مهم فصل چهارم

نکات مهم فصل چهارم

نکات مهم فصل چهارم

نکات مهم فصل چهارم

نکات مهم فصل چهارم

نکات مهم فصل چهارم

نکات مهم فصل چهارم

نکات مهم فصل چهارم

نکات مهم فصل چهارم

Queuing models service waiting arrivals l c m Basic characteristics: average number of arrivals per time unit: l (mean arrival rate) average number that can be handled by one server per time unit: m (mean service rate) number of servers: c

W (S) = average time in queue (system) Queuing models (2) l c m W,Lq S,L W (S) = average time in queue (system) Lq (L) = average number in queue (system) Basic relationships: average time between arrivals: 1/l average service time: 1/m occupation rate: r = l/(c*m) average number being served: r = l/m L = Lq + r S = W + 1/m Lq = l * W L = l * S (Little’s formula)

Also formulas for M/Er/1, M/G/1, M/M/c, ... ! M/M/1 queue l 1 m Assumptions: time between arrivals and service time follow a negative expontential distribution 1 server (c = 1) FIFO Lq = (l* l)/(m * (m-l)) L = l/(m-l) = r/(1-r) W = r/(m-l) S = 1/(m-l) Also formulas for M/Er/1, M/G/1, M/M/c, ... !

Exercise Calculate: Increase the occupation rate until 90%: 1 resource, average service time of 8 minutes difficult cases 1 resource, average 6 difficult service time of 2 c21 cases per hour minutes task1a c1 c23 task2 c3 18 easy cases task1b c22 per hour easy cases 1 resource, average service time of 2.66 minutes Calculate: occupation rates, average waiting time, average throughput time, average number in system. Increase the occupation rate until 90%: average waiting time, average throughput time, average number in system.

Simulation Random walk through the reachability graph Computer experiment pseudo random numbers random generator Validation Statistical aspects start run subruns Animation Flexible No proof!