Instructor: Spyros Reveliotis

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IE7201 Production Service Systems
EngineeringFall 2010

• Instructor Spyros Reveliotis
• e-mail spyros_at_isye.gatech.edu
• homepage www.isye.gatech.edu/spyros

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Course Logistics

• Office Hours By appointment
• Course Prerequisites
• ISYE 6761 (Familiarity with basic probability
  concepts and Discrete Time Markov Chain theory)
• ISYE 6669 (Familiarity with optimization concepts
  and formulations, and basic Linear Programming
  theory)
• Grading policy
• Homework 40
• Project 20
• Final Exam 35
• Class Participation 5
• Reading Materials
• Course Textbook C. Cassandras and S. Lafortune,
  Introduction to Discrete Event Systems, 2nd Ed.,
  Springer (recommended reading)
• Additional material will be distributed during
  the course development

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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.

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Our basic view of the considered systems

• Production System A transformation process
  (physical, locational, physiological,
  intellectual, etc.)

• The production system as a process network

Suppliers
Customers
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The major functional units of a modern
organization
Strategic Planning defining the organizations
mission and the required/perceived core
competencies
Production/ Operations product/service creation
Finance/ Accounting monitoring of the
organization cash-flows
Marketing demand generation and order taking
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Fit Between Corporate and Functional Strategies
(Chopra Meindl)
Corporate Competitive Strategy
Supply Chain or Operations Strategy
Product Development Strategy
Marketing and Sales Strategy
Information Technology Strategy
Finance Strategy
Human Resources Strategy
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Corporate Mission

• The mission of the organization
• defines its purpose, i.e., what it contributes to
  society
• states the rationale for its existence
• provides boundaries and focus
• defines the concept(s) around which the company
  can rally
• Functional areas and business processes define
  their missions such that they support the overall
  corporate mission in a cooperative and
  synergistic manner.

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Corporate Mission Examples

• Merck The mission of Merck is to provide society
  with superior products and services-innovations
  and solutions that improve the quality of life
  and satisfy customer needs-to provide employees
  with meaningful work and advancement
  opportunities and investors with a superior rate
  of return.
• FedEx FedEx is committed to our
  People-Service-Profit philosophy. We will produce
  outstanding financial returns by providing
  totally reliable, competitively superior, global
  air-ground transportation of high-priority goods
  and documents that require rapid, time-certain
  delivery. Equally important, positive control of
  each package will be maintained utilizing real
  time electronic tracking and tracing systems. A
  complete record of each shipment and delivery
  will be presented with our request for payment.
  We will be helpful, courteous, and professional
  for each other, and the public. We will strive to
  have a completely satisfied customer at the end
  of each transaction.

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A strategic perspective on the operation of the
considered systems
Responsiveness (Reliability Quickness
Flexibility e.g., Dell, Overnight Delivery
Services)
Competitive Advantage through which the company
market share is attracted
Cost Leadership (Price e.g., Wal-Mart,
Southwest Airlines, Generic Drugs)
Differentiation (Quality Uniqueness e.g.,
Luxury cars, Fashion Industry, Brand Name Drugs)
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The operations frontier, trade-offs, and the
operational effectiveness
Responsiveness
Cost Leadership
Differentiation
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The primary drivers for achieving strategic fit
in Operations Strategy(adapted from Chopra
Meindl)
Corporate Strategy
Operations Strategy
Efficiency
Responsiveness
Market Segmentation
Facilities
Inventory
Transportation
Information
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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.

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Queueing TheoryA plausible modeling framework

• Quoting from Wikipedia
• Queueing theory (also commonly spelled queuing
  theory) is the mathematical study of waiting
  lines (or queues).
• The theory enables mathematical analysis of
  several related processes, including arriving at
  the (back of the) queue, waiting in the queue
  (essentially a storage process), and being served
  by the server(s) at the front of the queue.
• The theory permits the derivation and
  calculation of several performance measures
  including the average waiting time in the queue
  or the system, the expected number waiting or
  receiving service and the probability of
  encountering the system in certain states, such
  as empty, full, having an available server or
  having to wait a certain time to be served.

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The traditional approach

• Traditionally, the problems pertaining to the
  design and control of the material flow taking
  place in production systems have been addressed
  through deterministic modeling e.g.,
• MRP and MRP-related approaches
• Flow Analysis in Systematic Layout Planning
• (Rough-Cut) Capacity Planning
• (even) shop-floor scheduling

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The underlying variability

• But the actual operation of the system is
  characterized by high variability due to a large
  host of operational detractors e.g.,
• machine failures
• employee absenteeism
• lack of parts or consumables
• defects and rework
• planned and unplanned maintenance
• set-up times and batch-based operations

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Analyzing a single workstation with deterministic
inter-arrival and processing times
Case I ta tp 1.0
WIP
1
TH 1 part / time unit Expected CT tp
t
1
2
3
4
5
Arrival
Departure
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Analyzing a single workstation with deterministic
inter-arrival and processing times
Case II tp 1.0 ta 1.5 gt tp
WIP
Starvation!
1
TH 2/3 part / time unit Expected CT tp
t
1
2
4
5
3
Arrival
Departure
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Analyzing a single workstation with deterministic
inter-arrival and processing times
Case III tp 1.0 ta 0.5
WIP
Congestion!
TH 1 part / time unit Expected CT ? ?
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A single workstation with variable inter-arrival
times
Case I tp1 ta?N(1,0.12) (ca?a / ta 0.1)
WIP
3
2
TH lt 1 part / time unit Expected CT ? ?
1
t
1
2
3
4
5
Arrival
Departure
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A single workstation with variable inter-arrival
times
Case II tp1 ta?N(1,1.02) (ca?a / ta 1.0)
TH lt 1 part / time unit Expected CT ? ?
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A single workstation with variable processing
times
Case I ta1 tp?N(1,1.02)
TH lt 1 part / time unit Expected CT ? ?
Arrival
Departure
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Remarks

• Synchronization of job arrivals and completions
  maximizes throughput and minimizes experienced
  cycle times.
• Variability in job inter-arrival or processing
  times causes starvation and congestion, which
  respectively reduce the station throughput and
  increase the job cycle times.
• In general, the higher the variability in the
  inter-arrival and/or processing times, the more
  intense its disruptive effects on the performance
  of the station.
• The coefficient of variation (CV) defines a
  natural measure of the variability in a certain
  random variable.

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The propagation of variability
W1
W2
Case I tp1 ta?N(1,1.02)
Case II ta1 tp?N(1,1.02)
WIP
3
2
1
t
1
2
3
4
5
W1 arrivals
W1 departures
W2 arrivals
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Remarks

• The variability experienced at a certain station
  propagates to the downstream part of the line due
  to the fact that the arrivals at a downstream
  station are determined by the departures of its
  neighboring upstream station.
• The intensity of the propagated variability is
  modulated by the utilization of the station under
  consideration.
• In general, a highly utilized station propagates
  the variability experienced in the job processing
  times, but attenuates the variability experienced
  in the job inter-arrival times.
• A station with very low utilization has the
  opposite effects.

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Problem (Re-)Statement

• How do I get a (more) accurate estimate of the
  performance of a certain system configuration?
• How do I design and control a system to support
  certain target performance?
• What are the attributes that determine these
  performance measures?
• What are the corresponding dependencies?
• Are there inter-dependencies between these
  performance measures and of what type?
• What target performances are feasible?

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Factory Physics(a term coined by W. Hopp M.
Spearman)

• The employment of fundamental concepts and
  techniques coming from the area of queueing
  theory in order to characterize, analyze and
  understand the dynamics of (most) contemporary
  production systems.

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The need for behavioral control
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Cluster Tools An FMS-type of environment in
contemporary semiconductor manufacturing
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Another example Traffic Management in an AGV
System
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A more realistic exampleA typical fab layout
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An example taken from the area of public
transportation
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A more avant-garde exampleComputerized workflow
management
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A modeling abstractionSequential Resource
Allocation Systems

• A set of (re-usable) resource types R Ri, i
  1,...,m.
• Finite capacity Ci for each resource type Ri.
• a set of job types J Jj, j 1,...,n.
• An (partially) ordered set of job stages for each
  job type, pjk, k 1,...,lj.
• A resource requirements vector for each job stage
  p, api, 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)

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Behavioral or Logical vs Performance Control of
Sequential RAS
Resource Allocation System
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An Event-Driven RAS Control Scheme
Event
Commanded Action
Configuration Data
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Theoretical foundations
Control Theory
Theoretical Computer Science
Discrete Event Systems
Operations Research
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Course Outline

• 1. Introduction Course Objectives, Context, and
  Outline
• Contemporary organizations and the role of
  Operations Management (OM)
• Corporate strategy and its connection to
  operations
• The organization as a resource allocation system
  (RAS)
• The underlying RAS management problems and the
  need for understanding the impact of the
  underlying stochasticity
• The basic course structure
• 2. Modeling and Analysis of Production and
  Service Systems as Continuous-Time Markov Chains
• (A brief overview of the key results of the
  theory of Discrete-Time Markov Chains
• Bucket Brigades
• Poisson Processes and Continuous-Time Markov
  Chains (CT-MC)
• Birth-Death Processes and the M/M/1 Queue
• Transient Analysis
• Steady State Analysis
• Modeling more complex behavior through CT-MCs
• Single station systems with multi-stage
  processing, finite resources and/or blocking
  effects
• Open (Jackson) and Closed (Gordon-Newell)
  Queueing networks
• (Gershwins Models for Transfer Line Analysis)

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Course Outline (cont.)

• 3. Accommodating non-Markovian behavior
• Phase-type distributions and their role as
  approximating distributions
• The M/G/1 queue
• The G/M/1 queue
• The G/G/1 queue
• The essence of Factory Physics
• (Reversibility and BCMP networks)
• 4. Performance Control of Production and Service
  systems
• Controlling the event rates of the underlying
  CT-MC model (an informal introduction of the dual
  Linear Programming formulation in standard MDP
  theory)
• A brief introduction of the theory of Markov
  Decision Processes (MDPs) and of Dynamic
  Programming (DP)
• An introduction to Approximate DP
• An introduction to dispatching rules and
  classical scheduling theory
• Buffer-based priority scheduling policies, Meyn
  and Kumars performance bounds and stability
  theory

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Course Outline (cont.)

• 5. Behavioral Control of Production and Service
  Systems
• Behavioral modeling and analysis of Production
  and Service Systems
• Resource allocation deadlock and the need for
  liveness-enforcing supervision (LES)
• Petri nets as a modeling and analysis tool
• A brief introduction to the behavioral control of
  Production and Service Systems