email spyrosisyegatechedu homepage wwwisyegatechedu spyros IE7201 Production amp Service Systems Engineering Spring 2020 Course Logistics Office Hours By appointment ID: 783835
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Slide1
Instructor: Spyros Reveliotise-mail: spyros@isye.gatech.eduhomepage: www.isye.gatech.edu/~spyros
IE7201: Production & Service Systems Engineering
Spring 2020
Slide2“Course Logistics”Office Hours: By appointmentCourse 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: 0%
Two Midterm Exams: 30% each
Final Exam: 40%
Reading Materials:
Course Textbook:
Fundamentals of Queueing Theory (5
th
edition)
, by J. G.
Shortle
, J. M. Thompson, D. Gross and C. M. Harris, J. Wiley & Sons, Inc., 2018.
Additional material will be distributed during the course development
Slide3Course ObjectivesProvide 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 for simplifying approximations.Systematize the notion and role of simulation in the considered problem contexts.Define a “research frontier” in the addressed areas.
Slide4Our basic view of the considered systemsProduction System: A transformation process (physical, locational, physiological, intellectual, etc.)
Organization
Inputs
Outputs
Materials
Capital
Labor
Manag. Res.
Goods
Services
The production system as a
process network
Stage 5
Stage 4
Stage 3
Stage 2
Stage 1
Suppliers
Customers
Slide5The major functional units of a modern organizationStrategic Planning:defining the organization’s 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
Slide6Corporate MissionThe mission of the organizationdefines its purpose, i.e., what it contributes to societystates the rationale for its existenceprovides boundaries and focusdefines the concept(s) around which the company can rallyFunctional areas and business processes
define their missions such that they support the overall corporate mission in a cooperative and synergistic manner.
Slide7Corporate Mission ExamplesMerck: 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.
Slide8A 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
Slide9The primary “drivers” for achieving strategic fit in Operations Strategy(adapted from Chopra & Meindl)
Corporate Strategy
Operations Strategy
Efficiency
Responsiveness
Facilities
Inventory
Transportation
Information
Market
Segmentation
Slide10The course perspective:Modeling, analyzing and controlling workflowsSome Key Performance measuresProduction 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
.
Slide11The underlying variabilityBut the actual operation of the system is characterized by high variability due to a large host of operational detractors; e.g.,machine failuresemployee absenteeismlack of parts or consumablesdefects and reworkplanned and unplanned maintenanceset-up times and batch-based operations
Slide12Some key issues to be addressed in this courseHow do I get good / accurate estimates 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?
Slide13Queueing Theory:A plausible modeling frameworkQuoting 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.
Slide14Factory 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.
Slide15Automation and the need for behavioral control
R
3
R
2
R
1
J
1
:
R
1
®
R
2
®
R
3
J
2
:
R
3
®
R
2
®
R
1
Slide16Another example: Traffic Management in an AGV System
Slide17Behavioral or Logical vs Performance Control of Sequential RASResource
Allocation
System
Behavioral
Correctness
Efficiency
Slide18An Event-Driven RAS Control Scheme
RAS Domain
Logical Control
System State Model
Performance Control
Configuration Data
Feasible
Actions
Admissible
Actions
Event
Commanded
Action
Slide19Theoretical foundations
Control
Theory
“Theoretical”
Computer
Science
Operations
Research
D
iscrete
E
vent
S
ystems
Slide20Course Outline1. Introduction: Course Objectives, Context, and OutlineContemporary organizations and the role of Operations Management (OM)Corporate strategy and its connection to operationsThe 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
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
The Exponential Distribution and the Poisson Process
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
Slide21Course Outline (cont.)3. Accommodating non-Markovian behaviorPhase-type distributions and their role as approximating distributions The M/G/1 queuePriority QueuesThe 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 Kumar’s performance bounds and stability theory
Slide22Course Outline (cont.)5. Behavioral Control of Production and Service SystemsBehavioral modeling and analysis of Production and Service SystemsResource 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
Slide23Analyzing a single workstation with deterministic inter-arrival and processing times
TH
B1
M1
Case I: t
a
= t
p
= 1.0
t
WIP
1
1
2
3
4
5
Arrival
Departure
TH = 1 part / time unit
Expected CT = t
p
Slide24Analyzing a single workstation with deterministic inter-arrival and processing times
TH
B1
M1
Case II: t
p
= 1.0; t
a
= 1.5 > t
p
t
WIP
1
1
2
3
4
5
Arrival
Departure
TH =
2/3
part / time unit
Expected CT = t
p
Starvation!
Slide25Analyzing a single workstation with deterministic inter-arrival and processing times
TH
B1
M1
Case III: t
p
= 1.0; t
a
= 0.5
WIP
TH = 1 part / time unit
Expected CT
t
1
1
2
3
4
5
Arrival
Departure
2
3
Congestion!
Slide26A single workstation with variable inter-arrival times
TH
B1
M1
Case I: t
p
=1; t
a
N(1,0.1
2
) (c
a
=
a
/ t
a
= 0.1)
t
1
1
2
3
4
5
Arrival
Departure
2
3
WIP
TH
<
1 part / time unit
Expected CT
Slide27A single workstation with variable inter-arrival times
TH
B1
M1
Case II: t
p
=1; t
a
N(1,1.0
2
) (c
a
=
a
/ t
a
= 1.0)
TH
<
1 part / time unit
Expected CT
t
1
1
2
3
4
5
Arrival
Departure
2
3
WIP
Slide28A single workstation with variable processing times
TH
B1
M1
Case I: t
a
=1; t
p
N(1,1.0
2
)
Arrival
Departure
TH
<
1 part / time unit
Expected CT
t
1
1
2
3
4
5
2
3
WIP
Slide29RemarksSynchronization 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.
Slide30The propagation of variability
B1
M1
TH
B2
M2
Case I: t
p
=1; t
a
N(1,1.0
2
)
Case II: t
a
=1; t
p
N(1,1.0
2
)
t
1
1
2
3
4
5
2
3
WIP
t
1
1
2
3
4
5
2
3
WIP
W1
W2
W1 arrivals
W1 departures
W2 arrivals
Slide31RemarksThe 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.