Instructor: Spyros Reveliotis homepage: IE7201: Production & Service Systems Engineering Fall.

Slides:



Advertisements
Similar presentations
Instructor: Spyros Reveliotis homepage: IE4803-C: Advanced Manufacturing Systems Modeling and.
Advertisements

OPSM 301 Operations Management Fall 2011 Zeynep Akşin.
Markov Reward Models By H. Momeni Supervisor: Dr. Abdollahi Azgomi.
FIN 685: Risk Management Topic 5: Simulation Larry Schrenk, Instructor.
A Workshop on Subject GRE / AGRE Maths in 9 Classes, II Hours each Day & Three mock tests for AGRE By: Satyadhar Joshi
ISM 206 Optimization Theory and Applications Fall 2005 Lecture 1: Introduction.
INDUSTRIAL & SYSTEMS ENGINEERING
Emmanuel Fernandez ECECS Dept. Univ. Cincinnati Emmanuel Fernandez Associate Professor
1 Petri Nets H Plan: –Introduce basics of Petri Net models –Define notation and terminology used –Show examples of Petri Net models u Calaway Park model.
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Course outline and schedule Introduction Event Algebra (Sec )
Page 0 Optimization Uncertainty Decision Analysis Systems Economics Masters of Engineering With Concentration in Systems Engineering A 30 hour graduate.
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Course outline and schedule Introduction (Sec )
OPERATIONS and LOGISTICS MANAGEMENT
Instructor: Dr. Radwan E. Abdel-Aal Office: Tel Web page:
Instructor: Spyros Reveliotis homepage: IE3104: Supply Chain Modeling: Manufacturing & Warehousing.
Chapter 2, Operations Strategy
Performance Evaluation of Computer Systems and Networks By Behzad Akbari Tarbiat Modares University Spring 2012 In the Name of the Most High.
Instructor: Spyros Reveliotis homepage: IE7201: Production & Service Systems Engineering Spring.
Operations Research Models
General information CSE : Probabilistic Analysis of Computer Systems
DEVELOPING STRATEGIES FOR COMPETITIVE ADVANTAGE Session 8 Diversification Strategy Session 8 Diversification Strategy 1.
Capacity analysis of complex materials handling systems.
Instructor: Spyros Reveliotis homepage: IE6650: Probabilistic Models Fall 2007.
Instructor: Spyros Reveliotis homepage: IE3104: Supply Chain Modeling: Manufacturing & Warehousing.
Chapter 11: Strategic Leadership Chapter 8 Production and operations management.
Course Summary. Characterization of the operations in contemporary SC’s and the area of SC management A SC chain consists of all the required stages for.
Instructor: Spyros Reveliotis homepage: GaTech / SJTU Dual MS: IE6201& IE6203 Summer 2008.
Instructor: Spyros Reveliotis homepage: IE7201: Production & Service Systems Engineering Fall.
1 Chapter 2 Operations Strategy and Competitiveness.
Software Engineering EKT 420 MOHAMED ELSHAIKH KKF 8A – room 4.
Capacity analysis of complex materials handling systems.
Chapter 3 System Performance and Models. 2 Systems and Models The concept of modeling in the study of the dynamic behavior of simple system is be able.
1 Network Coding and its Applications in Communication Networks Alex Sprintson Computer Engineering Group Department of Electrical and Computer Engineering.
GROUP MEMBERS AMARASENA R.G.C. (061004D) DE MEL W.R. (061013E) DOLAPIHILLA I.N.K. (061017U) KUMARAJITH R.M.E. (061031G)
Overview & Principles Production & Operations Management P.O.M. Overview & Principles Ken Homa.
ISyE 8803D Formal Methods in Operations Engineering Instructor Spyros Reveliotis Spring 2007.
Instructor: Spyros Reveliotis homepage: IE7201: Production & Service Systems Engineering Fall.
Chapter CHAPTER EIGHT OVERVIEW SECTION 8.1 – OPERATIONS MANAGEMENT Operations Management Fundamentals OM in Business IT’s Role in OM Competitive.
CS433 Modeling and Simulation Lecture 12 Queueing Theory Dr. Anis Koubâa 03 May 2008 Al-Imam Mohammad Ibn Saud University.
Why building models? n Cannot experience on the real system of interest n Cost n Danger n The real system does not exist Why using simulation? n Reduced.
© 2001 Six Sigma Academy© 2003 Six Sigma Academy1 Bank Exercise Champion Workshop.
Liveness-Enforcing Supervision of Sequential Resource Allocation Systems Spyros Reveliotis School of Industrial & Systems Eng. Georgia Institute of Technology.
Some Introductory Remarks on Operations Scheduling.
Instructor: Spyros Reveliotis Office: Room 316, ISyE Bldng tel #: (404) homepage: IE3104:
Design and Analysis of Algorithms (09 Credits / 5 hours per week) Sixth Semester: Computer Science & Engineering M.B.Chandak
DEPARTMENT/SEMESTER ME VII Sem COURSE NAME Operation Research Manav Rachna College of Engg.
Instructor: Spyros Reveliotis IE7201: Production & Service Systems Engineering Fall 2009 Closure.
Instructor: Spyros Reveliotis homepage: IE3104: Supply Chain Modeling: Manufacturing & Warehousing.
Courtesy Piggybacking: Supporting Differentiated Services in Multihop Mobile Ad Hoc Networks Wei LiuXiang Chen Yuguang Fang WING Dept. of ECE University.
1 Transport Bandwidth Allocation 3/29/2012. Admin. r Exam 1 m Max: 65 m Avg: 52 r Any questions on programming assignment 2 2.
Chapter 1 What is Simulation?. Fall 2001 IMSE643 Industrial Simulation What’s Simulation? Simulation – A broad collection of methods and applications.
Scott C. Johnson Lecturer Rochester Institute of Technology Spring 2016.
CPSC 531: System Modeling and Simulation
IE6201: Manufacturing Systems Spring 2006
Lecture 20 Revision class.
OPERATING SYSTEMS CS 3502 Fall 2017
Some Introductory Remarks on Operations Scheduling
IE4803-REV: Advanced Manufacturing Systems Modeling and Analysis Fall 2017 Instructor: Spyros Reveliotis homepage:
An introduction to Factory Physics
Modeling and Simulation (An Introduction)
The University of Jordan Mechatronics Engineering Department
IE7201: Production & Service Systems Engineering Spring 2017
Al-Imam Mohammad Ibn Saud University
Course Summary Organization: A process providing goods and services based on a set of inputs, including raw material, capital, labor and knowledge. The.
IE6201: Manufacturing Systems Summer 2011 Shanghai Jiaotong University
IE6201: Manufacturing Systems Spring 2007
World-Views of Simulation
Managerial Economics and Decision Sciences
Computer Systems Performance Evaluation
MSE 606A Engineering Operations Research
Presentation transcript:

Instructor: Spyros Reveliotis homepage: IE7201: Production & Service Systems Engineering Fall 2008

“Course Logistics” Office Hours: By appointment Course Prerequisites: ISYE 6650 (Familiarity with probability, stochastic modeling and basic queueing theory) and ISYE 6669 (Familiarity with optimization concepts and formulations, and basic Linear Programming theory) Grading policy: –Homework: 50% (more like take-home exams…) –Project: 25% –Class Participation: 25% Reading Materials: –Course Textbook: C. Cassandras and S. Lafortune, Introduction to Discrete Event Systems, 2nd Ed., Springer –Additional material will be distributed during the course development

Course Objectives Provide an understanding and appreciation of the different resource allocation and coordination problems that underlie the operation of production and service systems. Enhance the student ability to formally characterize and study these problems by referring them to pertinent analytical abstractions and modeling frameworks. Develop an appreciation of the inherent complexity of these problems and the resulting need of simplifying approximations. Systematize the notion and role of simulation in the considered problem contexts. Define a “research frontier” in the addressed areas.

Our basic view of the considered systems Production System: A transformation process (physical, locational, physiological, intellectual, etc.) Organization InputsOutputs Materials Capital Labor Manag. Res. Goods Services The production system as a process network Stage 5 Stage 4 Stage 3 Stage 2Stage 1 SuppliersCustomers

A strategic perspective on the operation of the considered systems Differentiation (Quality; Uniqueness; e.g., Luxury cars, Fashion Industry, Brand Name Drugs) Cost Leadership (Price; e.g., Wal-Mart, Southwest Airlines, Generic Drugs) Responsiveness (Reliability; Quickness; Flexibility; e.g., Dell, Overnight Delivery Services) Competitive Advantage through which the company market share is attracted

Some typical Performance Measures operationalizing the corporate strategy Production rate or throughput, i.e., the number of jobs produced per unit time Production capacity, i.e., the maximum sustainable production rate Expected cycle time, i.e., the average time that is spend by any job into the system (this quantity includes both, processing and waiting time). Average Work-In-Process (WIP) accumulated at different stations Expected utilization of the station servers. Remark: The above performance measures provide a link between the directly quantifiable and manageable aspects and attributes of the system and the primary strategic concerns of the company, especially those of responsiveness and cost efficiency.

The need for behavioral control R3R3 R2R2 R1R1 J 1 : R 1  R 2  R 3 J 2 : R 3  R 2  R 1

Cluster Tools: An FMS-type of environment in contemporary semiconductor manufacturing

Another example: Traffic Management in an AGV System

A more “realistic” example: A typical fab layout

An example taken from the area of public transportation

A more avant-garde example: Computerized workflow management

A modeling abstraction: Sequential Resource Allocation Systems A set of (re-usable) resource types R = {R i, i = 1,...,m}. Finite capacity C i for each resource type R i. a set of job types J = {J j, j = 1,...,n}. An (partially) ordered set of job stages for each job type, {p jk, k = 1,..., j }. A resource requirements vector for each job stage p, a p [i], i = 1,...,m. A distribution characterizing the processing time requirement of each processing stage. Protocols characterizing the job behavior (e.g., typically jobs will release their currently held resources only upon allocation of the resources requested for their next stage)

Behavioral or Logical vs Performance Control of Sequential RAS Resource Allocation System Behavioral Correctness Efficiency

An Event-Driven RAS Control Scheme RAS Domain Logical Control System State Model Performance Control Configuration Data Feasible Actions Admissible Actions EventCommanded Action

Theoretical foundations Control Theory “Theoretical” Computer Science Operations Research Discrete Event Systems

Course Outline 1. Qualitative / Behavioral modeling of the considered RAS Languages and (Finite State) Automata An introduction to (Ramadge & Wonham) Supervisory Control theory Petri nets Application of the above frameworks to the liveness-enforcing supervision of RAS 2, Timed models: towards performance analysis of the considered RAS Timed Automata and Petri nets Dioid / (max, +) Algebras Applications in Queueing Systems and in the development of efficient cyclical schedules for contemporary cluster tools 3. Stochastic Timed Automata Stochastic Clock Structures and Stochastic Timed Automata Generalized Semi-Markov Process (GSMP) and its connection to Simulation Markov and Semi-Markov Processes as specializations of the GSMP model Generalized Stochastic Petri nets (GSPN)

Course Outline (cont.) 4. The stochastic scheduling problem in the considered RAS Formulation of the scheduling problem for RAS with Markovian Dynamics in the GSPN modeling framework Approximation of non-Markovian dynamics through the method of stages Introduction to Markov Decision Process (MDP) theory and Dynamic Programming (DP) Approximate DP: current trends and their potential application in the RAS scheduling problem 5. Queueing-theoretic-based analysis and control of the considered RAS Introduction to the theory of Open and Closed Markovian Queueing networks BCMP networks Performance evaluation and stability analysis of multi-class queueing networks An introduction to fluid-based modeling, analysis and control of queuing systems 6. Application of MDP theory in inventory control The optimality of (S,s) policies Introduction to the theory of multi-echelon inventory systems Hedging point strategies

Reading Assignment Please, read Chapter 1 in your textbook.

An informal introduction to the RAS logical control problem

A motivational example: Deadlock Avoidance in an FMS R3R3 R2R2 R1R1 J 1 : R 1  R 2  R 3 J 2 : R 3  R 2  R 1

q 0 q J 21 J q J 22 J q 1 11 J q 2 21 J q 3 12 J q 4 22 J q J 21 J q J 12 J 21 J q J 21 J 22 J Modeling the underlying RAS behavior

q 0 q J 21 J q J 22 J q 1 11 J q 2 21 J q 3 12 J q 4 22 J q J 21 J q J 12 J 21 J q J 21 J 22 J Finite State Automata-based modeling: Safe vs. Unsafe Region

The Optimal Logical Control Policy q 0 q J 21 J q J 22 J q 1 11 J q 2 21 J q 3 12 J q 4 22 J q J 21 J q J 12 J 21 J q J 21 J 22 J q 6 13 J q 5 11 J 12 J q 7 23 J q 8 21 J 22 J q 9 11 J 13 J q J 13 J q 11 J 12 J 13 J q J 23 J q J 23 J q J 22 J 23 J

Complexity and other complicating effects State Safety is an NP-complete problem in sequential RAS (by reduction of the SAT problem) State Transition Diagram (STD) size: Uncontrollable and/or unobservable events where: C = max resource capacity Q = max number of stages supported by a resource m = number of resource types

The “nature” of the available results Special RAS structure admitting an optimal logical control policy of polynomial complexity w.r.t the RAS size Polynomial-Kernel (PK-) RAS logical control policies: Sub- optimal policies that are of polynomial complexity –RUN (Resource Upstream Neighborhood) –RO (Resource Ordering) –Banker’s algorithm –etc. An analytical framework for –interpreting the correctness of the above policies, and –enabling the “automatic” validation and synthesis of new members from this class of policies