Instructor: Spyros Reveliotise-mail: spyros@isye.gatech.edu

homepage: www.isye.gatech.edu/~spyros

IE7201: Production & Service Systems EngineeringFall 2012

“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: 25% – Midterm Exam: 35%– Final Exam: 40%

• 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

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

Inputs Outputs• Materials• Capital• Labor• Manag. Res.

• Goods• Services

• The production system as a process network

Stage 5

Stage 4

Stage 3

Stage 2Stage 1

Suppliers Customers

The major functional units of a modern organization

Strategic Planning:defining the organization’s mission and

the required/perceived core competencies

Production/Operations:

product/servicecreation

Finance/Accounting:

monitoring of the organization

cash-flows

Marketing:demand

generationand

order taking

Fit Between Corporate and Functional Strategies (Chopra & Meindl)

Corporate Competitive Strategy

Supply Chainor Operations

Strategy

ProductDevelopment

Strategy

Marketingand SalesStrategy

Information Technology Strategy

Finance Strategy

Human Resources Strategy

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.

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.

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 whichthe company market share is attracted

The operations frontier, trade-offs, and the operational effectiveness

Differentiation

Cost Leadership

Responsiveness

The primary “drivers” for achieving strategic fit in Operations Strategy

(adapted from Chopra & Meindl)

Corporate Strategy

Operations Strategy

Efficiency Responsiveness

Facilities Inventory Transportation Information MarketSegmentation

The course perspective:Modeling, analyzing and controlling workflows

Some Key Performance measures• 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.

Some key issues to be addressed in this course

• How 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?

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

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

Analyzing a single workstation with deterministic inter-arrival and processing times

THB1 M1

Case I: ta = tp = 1.0

t

WIP

1

1 2 3 4 5

Arrival Departure

TH = 1 part / time unitExpected CT = tp

Analyzing a single workstation with deterministic inter-arrival and processing times

THB1 M1

Case II: tp = 1.0; ta = 1.5 > tp

t

WIP

1

1 2 3 4 5

Arrival Departure

TH = 2/3 part / time unitExpected CT = tp

Starvation!

Analyzing a single workstation with deterministic inter-arrival and processing times

THB1 M1

Case III: tp = 1.0; ta = 0.5

WIP

TH = 1 part / time unitExpected CT t

1

1 2 3 4 5

Arrival Departure

2

3

Congestion!

A single workstation with variable inter-arrival times

THB1 M1

Case I: tp=1; taN(1,0.12) (ca=a / ta = 0.1)

t

1

1 2 3 4 5

Arrival Departure

2

3

WIP

TH < 1 part / time unitExpected CT

A single workstation with variable inter-arrival times

THB1 M1

Case II: tp=1; taN(1,1.02) (ca=a / ta = 1.0)

TH < 1 part / time unitExpected CT

t

1

1 2 3 4 5

Arrival Departure

2

3

WIP

A single workstation with variable processing times

THB1 M1

Case I: ta=1; tpN(1,1.02)

Arrival Departure

TH < 1 part / time unitExpected CT

t

1

1 2 3 4 5

2

3

WIP

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.

The propagation of variability

B1 M1 THB2 M2

Case I: tp=1; taN(1,1.02) Case II: ta=1; tpN(1,1.02)

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

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.

Queueing Theory:A 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.

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.

The need for behavioral control

R3R2R1

J1 : R1 R2 R3 J2 : R3 R2 R1

Cluster Tools: An FMS-type of environment in

contemporary semiconductor manufacturing

Another example: Traffic Management in an AGV System

W1 W2

W3W4

DockingStation

Type - 1Deadlock

Type - 2Deadlock

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 = {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, ap[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

ResourceAllocation

System

BehavioralCorrectness Efficiency

An Event-Driven RAS Control Scheme

RAS Domain

Logi

cal

Cont

rol

Syst

em S

tate

Mod

el

Perfo

rman

ce C

ontr

ol

Configuration Data

FeasibleActions

AdmissibleActionsEvent Commanded

Action

Theoretical foundations

ControlTheory

“Theoretical” Computer Science

OperationsResearch

DiscreteEventSystems

Course Outline1. 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• (Gershwin’s Models for Transfer Line Analysis)

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 Kumar’s performance bounds and

stability theory

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