IE7201: Production & Service Systems Engineering Fall 2012

1. IE7201: Production & Service Systems EngineeringFall 2012 Instructor: Spyros Reveliotis e-mail: spyros@isye.gatech.edu homepage: www.isye.gatech.edu/~spyros

2. “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

3. 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.

4. Inputs Outputs • Materials • Capital • Labor • Manag. Res. • Goods • Services Organization Stage 1 Stage 2 Stage 4 Stage 3 Stage 5 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

5. The major functional units of a modern organization Strategic 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

6. Fit Between Corporate and Functional Strategies (Chopra & Meindl) Corporate Competitive Strategy Product Development Strategy Supply Chain or Operations Strategy Marketing and Sales Strategy Information Technology Strategy Finance Strategy Human Resources Strategy

7. 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.

8. 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.

9. 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)

10. The operations frontier, trade-offs, and the operational effectiveness Responsiveness Cost Leadership Differentiation

11. 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

12. 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.

13. 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?

14. 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

15. 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

16. TH B1 M1 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

17. TH B1 M1 Analyzing a single workstation with deterministic inter-arrival and processing times Case II: tp = 1.0; ta = 1.5 > tp WIP Starvation! 1 TH = 2/3 part / time unit Expected CT = tp t 1 2 4 5 3 Arrival Departure

18. TH B1 M1 3 2 1 t 1 2 3 4 5 Arrival Departure 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  

19. TH B1 M1 A single workstation with variable inter-arrival times Case I: tp=1; taN(1,0.12) (ca=a / ta = 0.1) WIP 3 2 TH < 1 part / time unit Expected CT   1 t 1 2 3 4 5 Arrival Departure

20. TH B1 M1 WIP 3 2 1 t 1 2 3 4 5 Arrival Departure A single workstation with variable inter-arrival times Case II: tp=1; taN(1,1.02) (ca=a / ta = 1.0) TH < 1 part / time unit Expected CT  

21. TH B1 M1 WIP 3 2 1 t 1 2 3 4 5 A single workstation with variable processing times Case I: ta=1; tpN(1,1.02) TH < 1 part / time unit Expected CT   Arrival Departure

22. 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.

23. TH B1 M1 B2 M2 WIP 3 2 1 t 1 2 3 4 5 The propagation of variability W1 W2 Case I: tp=1; taN(1,1.02) Case II: ta=1; tpN(1,1.02) WIP 3 2 1 t 1 2 3 4 5 W1 arrivals W1 departures W2 arrivals

24. 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.

25. 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.

26. 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.

27. R1 R2 R3 J1 :R1®R2®R3 J2 :R3®R2®R1 The need for behavioral control

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

29. Another example: Traffic Management in an AGV System

30. A more “realistic” example:A typical fab layout

31. An example taken from the area of public transportation

32. A more avant-garde example:Computerized workflow management

33. 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}. • Aresource 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)

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

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

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

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

38. 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

39. 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