1 ANALYSIS OF EMAIL PROCESSING STRATEGIES TO ENHANCE EFFICIENCY AND EFFECTIVENESS Robert Greve Oklahoma State University.

Slides:



Advertisements
Similar presentations
Operations Management
Advertisements

Scheduling communication to reduce Information Overload and Interruptions By Ashish Gupta Ramesh Sharda Robert Greve Manjunath Kamath Oh ! I dream.
IE 429, Parisay, January 2003 Review of Probability and Statistics: Experiment outcome: constant, random variable Random variable: discrete, continuous.
LESSONs NINE and TEN QUEUING MODELS.
Lab Assignment 1 COP 4600: Operating Systems Principles Dr. Sumi Helal Professor Computer & Information Science & Engineering Department University of.
QUEUING MODELS Based on slides for Hilier, Hiller, and Lieberman, Introduction to Management Science, Irwin McGraw-Hill.
All Hands Meeting, 2006 Title: Grid Workflow Scheduling in WOSE (Workflow Optimisation Services for e- Science Applications) Authors: Yash Patel, Andrew.
Queuing Analysis Based on noted from Appendix A of Stallings Operating System text 6/10/20151.
Previously Optimization Probability Review Inventory Models Markov Decision Processes.
Previously Optimization Probability Review Inventory Models Markov Decision Processes.
Queuing Systems Chapter 17.
Chapter 13 Queuing Theory
Improving Robustness in Distributed Systems Jeremy Russell Software Engineering Honours Project.
1 Performance Evaluation of Computer Networks Objectives  Introduction to Queuing Theory  Little’s Theorem  Standard Notation of Queuing Systems  Poisson.
Simulation with ArenaChapter 2 – Fundamental Simulation Concepts Discrete Event “Hand” Simulation of a GI/GI/1 Queue.
1 Queuing Theory 2 Queuing theory is the study of waiting in lines or queues. Server Pool of potential customers Rear of queue Front of queue Line (or.
Ant Colonies As Logistic Processes Optimizers
Management of Waiting Lines
Queuing Analysis Based on noted from Appendix A of Stallings Operating System text 6/28/20151.
Chapter 18 Management of Waiting Lines
Queuing Theory. Queuing theory is the study of waiting in lines or queues. Server Pool of potential customers Rear of queue Front of queue Line (or queue)
Lecture 4 Mathematical and Statistical Models in Simulation.
Lecture 14 – Queuing Systems
1 Chapter 7 Dynamic Job Shops Advantages/Disadvantages Planning, Control and Scheduling Open Queuing Network Model.

Location Models For Airline Hubs Behaving as M/D/C Queues By: Shuxing Cheng Yi-Chieh Han Emile White.
KNOWLEDGE WORKERS SPEND MORE TIME ON THAN ANY OTHER SINGLE TASK © 2015 Atrendia - The Leader in Lean Management 1 LeanMail More resources.
Spreadsheet Modeling & Decision Analysis
Introduction to Management Science
1. Facility size 2. Equipment procurement Long-term
Introduction to Discrete Event Simulation Customer population Service system Served customers Waiting line Priority rule Service facilities Figure C.1.
(C) 2009 J. M. Garrido1 Object Oriented Simulation with Java.
AN INTRODUCTION TO THE OPERATIONAL ANALYSIS OF QUEUING NETWORK MODELS Peter J. Denning, Jeffrey P. Buzen, The Operational Analysis of Queueing Network.
Management of Waiting Lines McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.
Introduction to Operations Research
1 [3] Jorge Martinez-Bauset, David Garcia-Roger, M a Jose Domenech- Benlloch and Vicent Pla, “ Maximizing the capacity of mobile cellular networks with.
Structure of a Waiting Line System Queuing theory is the study of waiting lines Four characteristics of a queuing system: –The manner in which customers.
Supplement C Waiting Line Models Operations Management by R. Dan Reid & Nada R. Sanders 4th Edition © Wiley 2010.
18 Management of Waiting Lines.
Queuing Theory Basic properties, Markovian models, Networks of queues, General service time distributions, Finite source models, Multiserver queues Chapter.
Queueing Theory What is a queue? Examples of queues: Grocery store checkout Fast food (McDonalds – vs- Wendy’s) Hospital Emergency rooms Machines waiting.
1 Queuing Models Dr. Mahmoud Alrefaei 2 Introduction Each one of us has spent a great deal of time waiting in lines. One example in the Cafeteria. Other.
Topics To Be Covered 1. Tasks of a Shop Control Manager.
1 Chapters 8 Overview of Queuing Analysis. Chapter 8 Overview of Queuing Analysis 2 Projected vs. Actual Response Time.
Modeling and Simulation Discrete-Event Simulation
Ch 10 - Risk Management Learning Objectives You should be able to: List and describe risk management processes, inputs, outputs, and tools List and describe.
Simulation Examples and General Principles
Chapter 10 Verification and Validation of Simulation Models
1 ISE 195 Introduction to Industrial & Systems Engineering.
Beyond Spam: OR/MS Modeling Opportunities for Response Management Ramesh Sharda Robert A. Greve, Ashish Gupta, Manjunath Kamath, Mohan R. Chinnaswamy.
CSCI1600: Embedded and Real Time Software Lecture 19: Queuing Theory Steven Reiss, Fall 2015.
Stevenson and Ozgur First Edition Introduction to Management Science with Spreadsheets McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies,
Structure of a Waiting Line System Queuing theory is the study of waiting lines Four characteristics of a queuing system: –The manner in which customers.
Dr. Anis Koubâa CS433 Modeling and Simulation
(C) J. M. Garrido1 Objects in a Simulation Model There are several objects in a simulation model The activate objects are instances of the classes that.
© 2015 McGraw-Hill Education. All rights reserved. Chapter 17 Queueing Theory.
Management of Waiting Lines Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent.
Queueing Theory. The study of queues – why they form, how they can be evaluated, and how they can be optimized. Building blocks – arrival process and.
Chapter 2 Simulation Examples. Simulation steps using Simulation Table 1.Determine the characteristics of each of the inputs to the simulation (probability.
McGraw-Hill/Irwin Copyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Chapter 18 Management of Waiting Lines.
WAITING LINES AND SIMULATION
Application of Queueing
Management of Waiting Lines
Chapter 10 Verification and Validation of Simulation Models
Demo on Queuing Concepts
Native simulation of different scheduling policies
Discrete Event “Hand” Simulation of a GI/GI/1 Queue
Waiting Line Models Waiting takes place in virtually every productive process or service. Since the time spent by people and things waiting in line is.
LECTURE 09 QUEUEING THEORY PART3
VIRTUE MARYLEE MUGURACHANI QUEING THEORY BIRTH and DEATH.
Presentation transcript:

1 ANALYSIS OF PROCESSING STRATEGIES TO ENHANCE EFFICIENCY AND EFFECTIVENESS Robert Greve Oklahoma State University

2 AGENDA ► INTRODUCTION ► OVERVIEW OF RESEARCH  MISSION, GOALS, STRATEGY, & OBJECTIVES ► MODELING STRATEGIES  QUEUING THEORY  STOCHASTIC PROGRAMMING  SIMULATTION ► SIMULATION STUDY ► FUTURE RESEARCH ► QUESTIONS & COMMENTS

3 INTRODUCTION ► ► "To make knowledge work productive will be the great management task of this century just as to make manual work productive was the great management task of the last century. The gap between knowledge work that is left unmanaged is probably a great deal wider than was the tremendous difference between manual work before and after the introduction of scientific management.“ (Peter Drucker, 1998)

4 INTRODUCTION ► “And then there’s your work flow during the day. An information worker gets lots of s as people want you to bid on something or respond to a problem. All these ‘events’ are coming in on your PC. Does the software help you know which of those you should ignore or pass along to somebody else, and how to prioritize them? No. We don’t do that yet.” (Bill Gates, 2003)

5 INTRODUCTION ► KNOWLEDGE WORKER  “True, knowledge workers are still a minority, but they are fast becoming the largest single group. And they have already become the major creator of wealth.” (Drucker, 2002) ► OVERLOAD  “More than 1 million messages pass through the Internet every hour. An estimated 2.7 trillion messages were sent in 1997.” And it was projected that nearly 7 trillion messages would be sent in 2000 (Overly, Foley & Lardner, 1999).  Intel (1999 Intel Employee Use Survey) ► 200: average number of s waiting in an employee’s inbox ► 2.5: average number of hours of each day employees spend managing ► 30: percentage of that is unnecessary

6 RESEARCH STREAMS ► MISSION  IMPROVEMENT OF KNOWLEDGE WORK ► GOALS  DECISION SUPPORT FOR KNOWLEDGE WORKERS ► STRATEGY  MODELING AND MANIPULATION OF PROCESSING SCHEMES ► OBJECTIVES  DISCOVERY OF HEURISTICS & CONTINGENCIES  VALIDATION OF HEURISTICS & CONTINGENCIES  IMPLEMENTATION ► DSS ► ES ► INTELLIGENT AGENTS

7 SCENARIO/POLICY TABLE EXAMPLE FREQUENCY OF UTILIZATION OF KNOWLEDGE WORKER NATURE OF OUTSIDE WORK... PERFORMANCE CRITERIA OPTIMAL POLICY INFREQUENTHIGHINFREQUENT RESPONSE TIME PRIORITIZE BY TYPE... RESOLUTION TIME PRIORITIZE BY ITERATION MINIMIZE DISTRACTIONS HOURS

8 QUEUING THEORY ANALOGIES ► SERVER → KNOWLEDGE WORKER ► CUSTOMER → ► QUEUE → INBOX ► WAIT IN THE SYSTEM → RESPONSE TIME ► QUEUING DISCIPLINE → PROCESSING SCHEME

9 QUEUING THEORY

10 SINGLE SERVER QUEUE EXAMPLE A FACULTY MEMBER’S WEEKLY

11 SINGLE SERVER QUEUE EXAMPLE A FACULTY MEMBER’S ► ASSUMPTIONS  FIFO  EXPONENTIAL INTERARRIVAL AND PROCESSING TIMES ► RAQS (Kamath, et. al., 1999) ► UTILIZATION:  PERCEIVED INFORMATION OVERLOAD???

12 SINGLE SERVER QUEUE EXAMPLE A FACULTY MEMBER’S

13 SINGLE SERVER QUEUE EXAMPLE A FACULTY MEMBER’S

14 MULTI-SERVER QUEUES EXAMPLE A KNOWLEDGE NETWORK

15 MULTI-SERVER QUEUES EXAMPLE A KNOWLEDGE NETWORK ► ASSUMPTIONS  FIFO  POISON ARRIVALS  EXPONENTIAL PROCESSING TIME DISTRIBUTIONS ► UTILIZATIONS  REP 1: 0.80  REP 2: 0.86  REP 3: 0.81 ► AVERAGE TIME IN THE SYSTEM  DAYS

16 STOCHASTIC PROGRAMMING ► Objective: Maximizing the utility of processed  Utility of a processed may decrease with time.  Potential arrival of different types of in the future. ► Decision Variables - whether or not to process an in a given stage ► The stochastic parameters – arriving messages & processing time.

17 SIMULATION ► CONSIDERATIONS  Utilization  Categorization/Prioritization  Prioritization of Ongoing Message  Frequency & Duration of Interruptions  Frequency & Duration of Processing Requirements  Hours

18 MODELED SCENARIO ► PARAMETERS  ENVIRONMENT ► NATURE OF  FREQUENT, SHORT  INFREQUENT, LONG ► UTILIZATION  LOW (60%)  HIGH (80%)  EXTREME (90%) ► NATURE OF OUTSIDE WORK (INTERRUPTIONS)  FREQUENT, SHORT  INFREQUENT, LONG

19 MODELED SCENARIO ► PARAMETERS  POLICIES ► HOURS  NONE (CONTINUOUS)  MORNING  SPLIT ► PRIORITY SCHEME  1111, 1122, 1212, 2121, 1234  (PRIORITY GIVEN TO NEW TYPE 1 , ONGOING TYPE 1 , NEW TYPE 2 , AND ONGOING TYPE 2 , RESPECTIVELY)

20

21 GENERAL HYPOTHESES ► Higher utilization will cause slower response and resolution times. ► Priority given to type one messages will significantly reduce type one response and resolution times. ► Priority given to type one messages will significantly increase type two response and resolution times. ► Priority given to ongoing messages will significantly reduce resolution times.

22 GENERAL HYPOTHESES ► Infrequent, long duration interruptions will correlate with slower response times, compared to frequent, short duration interruptions. ► Infrequent, long duration processing requirements will cause slower response times, compared to frequent, short duration processing requirements. ► Morning hours will significantly increase response and resolution times, but to a lesser extent.

23 RESULTS OF INTEREST

24

25

26 RESULTS OF INTEREST

27 “ HOURS”

28 “SPLIT HOURS”

29 MANOVA RESULTS ► Utilization was a significant predictor of response and resolution times (.01 level). ► Priority schemes favoring type one messages significantly reduced type one response and resolution times (.01 level). ► Priority schemes favoring type one messages did significantly increase type two response and resolution times (.01 level). ► Priority given to ongoing messages did NOT significantly reduce resolution times(.01 level).

30 MANOVA RESULTS ► The frequency and duration of was a significant factor (.01 level). ► The frequency and duration of outside work interruptions was a significant factor (.01 level). ► Morning hours did significantly increase response and resolution times (.01 level). ► Split hours did significantly increase response and resolution times, but significantly less than morning hours (.01 level).

31 IMPLICATIONS OF RESULTS ► Strategy matters. ► Strategy will depend on timeliness of , and tolerance for interruptions. ► Analysis can provide a concrete basis for informed decisions.

32 FUTURE RESEARCH ► CONTINUED MODELING ► VALIDATION  CASE STUDY ► IMPLEMENTATION  DSS  ES  INTELLIGENT AGENTS ► BEHAVIORIAL ASPECTS  Perceived Information Overload

33 QUESTIONS & COMMENTS???