Network Techniques - II.
|
|
- Roland McDonald
- 5 years ago
- Views:
Transcription
1 erformance:lide1.doc Network Techniques - II. CM time - CM time+ : Overlapping, open network and non-broken activity keep being problematic ( chedules for roduction Management?! ) MM time : ( METRA otentials' Method ) Activity-on-node typed, deterministic project model with discrete variables and with abilities of handling manyfold and multiple relations GTM : ( General Model ) Event * -on-node typed deterministic project model with homogeneous relations * Deadline and/or milestone
2 erformance:lide2.doc METRA otentials' Method (MM time ) 196 : B. Roy, France, Appl: Nuclear ower tation ( originally: start potentials only ) Node : Activity ( with no break in performance) ( Event and milestone have duration of ) Edge : Technical-, technological-, or resource management-born quantified relation arameter (weight) : ag-time, duration and time potential ( deterministic variable ) Aim : chedules for production management, modelling technologies, monitoring progression, change management Handling ( time consequences of ) overlapping ( relative in time ), restrictions for resource allocation, technological and spatial limitations, etc.
3 erformance:lide3.doc "Activity-on-node" Activity identificator ( optional ) Early tart [A] Duration Early Finish ate tart Total Float ate Finish "Relation" (min) Deadline/milestone of predecessor (basic) activity ( here: Finish ) Deadline/milestone of successor (related) activity ( here: tart ) F p arameter (lag-time) Type or relation ( Finish-tart minimum ) Arrow indicates direction of relation, while solid line indicates that the parameter is a lower bound (minimum)
4 erformance:lide.doc "Relation" (max) Deadline/milestone of predecessor (base) activity ( here: Finish ) Deadline/milestone of successor (related) activity ( here: tart ) "minus" sign - F p arameter (lag-time) Type of relation ( Finish-tart maximum ) Reversed arrow indicates dirtection of relation, while broken line and "minus" sign indicate that the parameter is an upper bound (maximum) "Hammock/uspended Activity" [tart] [Finish] F
5 erformance:lide5.doc MM - Basic Relations rogression (completion) [%] 1 D p FFp 3 Fp 1 p 2 Fp Transforming Relations FFq Fq Fq q FFp q = p - q = p + D p q = p + D p - Fp q = p + q = p +D p + q = p + D p Fp q = p - D p q = p - D p - q = p - p q = p + - D p q = p - D p q = p +
6 erformance:lide6.doc ingle Relations Fp finish-start minimum p -Fp finish-start maximum p p start-start minimum p -p start-start maximum p FFp finish-finish minimum p -FFp finish-finish maximum p Fp start-finish minimum p -Fp start-finish maximum p uccessor (related) activity can be started at least "p" tu later than predecessor (base) activity is finished uccessor (related) activity should be started at most "p" tu later than predecessor (base) activity is finished uccessor (related) activity can be started at least "p" tu later than predecessor (base) activity is started uccessor (related) activity should be started at most "p" tu later than predecessor (base) activity is started uccessor (related) activity should be finished at least "p" tu later than predecessor (base) activity is finished uccessor (related) activity should be finished at most "p" tu later than predecessor (base) activity is finished uccessor (related) activity should be finished at least "p" tu later than predecessor (base) activity is started uccessor (related) activity should be finished at most "p" tu later than predecessor (base) activity is started Typically in case of strictly limited resources ( usually with parameter of, to assign consecutive processes ) Typically accompanying an Fp relation, in case of sensitive conditions or strict expectations on effective resource usage Typically in case of well synchronized parallel processes, in a large-scale schedule of a project Not typical relation. olely or together with an p relation it can be a useful tool for direct allocation ( time or resource ) Mostly a count-down typed relation for administrative, e.g. handover- or supervisory activities Not typical relation. olely or together with an FFp relation it can be a useful tool for direct allocation ( time or resource ) Theoretic relation, typically with negative parameter and for to replace a -Fp relation ( see: scheduling ) Theoretic relation, being mentioned to complete the list only. It can be used in case of complicated allocations
7 erformance:lide7.doc Most Frequently Used Combined Relations p FFp (min) critical succession -p -FFp } CRp (max) critical succession Fp -Fp strict/forced succession F -F } } immediate succession FF f(ds) f(dp) }-CRp } (min) critical approach (in progression) A lead-time of at least p tu should be provided between the predecessor (base) and successor (related) activity at any rate of completion A lead-time of at most p tu can be accepted between the predecessor (base) and successor (related) activity at any rate of completion uccessor (related) activity must be started exactly p tu after predecessor (base) activity is finished uccessor (related) activity must start immediately after predecessor (base) activity is finished Timing of base and of related activities should provide at least f(dp) tu lead-time between their starts and at least f(ds) tu lead-time between their finishes. ( arameters set as function of activity durations ) Typical relation of technological or resource management-born restrictions ( hardening, drying, consolidation, etc.) independent of durations of activities. Carefully applied it can be a useful tool in case of sensitive conditions. Be careful with it! Applications may result in a misguiding model! Typically used for direct allocation of succeeding activities. Typically used for expensive or most significant resources to provide their continuous usage, application or employment Typical tool for to provide room (manipulation/operation area) for succeeding activities. arameters are set in relation of intensity of performances. -FF f(ds) - f(dp) } (max) critical approach (in progression) chedule of base and related activities is acceptable if it results in at most f(dp) tu lead-time between their starts and at most f(ds) tu lead-time between their finishes. ( arameters set as function of activity durations ) Carefully applied it can be a useful tool for restricting area of manipulation/operation on the construction site. Be careful! Applications may result in a misguiding model!
8 erformance:lide8.doc roviding Technological Break 1 rogression [%] D p FFp CRp p rogression [%] D p FFp 1 CRp p
9 erformance:lide9.doc roviding Manipulation Area rogression [m] D p FF l l l l D p rogr.[m] D p FF l l l l D p
10 erformance:lide1.doc ensitive Conditions rogression [%] rogression [%] D p -FFp D p -FFp 1 1 -CRp -CRp -p -p Restricting Manipulation Area rogression [m] D p -FF l l rogr.[m] -FF l D p l l - l D p l - l D p
11 erformance:lide11.doc Restrictions on Duration rogression [%] -D max 1 j j i D min Virtual Deceleration / aradox / rogression [unit] D n = D - d n 1 d
12 erformance:lide12.doc MM Network roblem: eft Abutment eft ier Right ier Right Abutment ite reparation iling F -F F F F F Foundations F F5 F5 F F5 F5 F Walls & iers F F2 F2 F2 F2 F2 F2 Beams & labs F F F F F F F Road tructures + Finishes
13 erformance:lide13.doc Behavior/Type of (Critical) Activities A B + C 7 7 F F A 1 A 2 B 1 B 2 C 1 C A B /F C A 1 A 2 B 1 B 2 C 1 C A B - C FF A 1 A 2 B 1 B 2 C 1 C
14 erformance:lide1.doc General Model (GTM) 1997 : Hungary, Z. A. Vattai, Multi-project management (MÁV) Node : Event ( a specific moment in time ) ( start, finish, milestone, deadline ) Edge : Relation/comparision/restriction assignment ( process, activity, idle-time, lag-time, etc. as technical interpretation ) arameter (weight) : Restriction parameter, lower bound value, time-potential ( deterministic variable ) Aim : Releasing restrictions of traditional scheduling techniques (ERT,CM,MM), to develop flexible technological models and stabil logical structures for projects of typified (logical/technological) elements
15 erformance:lide15.doc Homogenizing relations p i i t ij p j - p i t ij p j j p i i p j - p i t ij / ( 1) τ ij π i π j - t ij p j j p j - p i = t ij τ ij π j π i τ ij p i i t ij - t ij p j j
16 erformance:lide16.doc MM time fi GTM roblem A B C FF F D E F F F2 F1 FF A A B1 B2 C1 C D D2 3 5 E E2 F1 F A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 F2 A A2-7 9 B B C1 2 C2-2 2 D1 3 D E1 5 E2-9 1 F F2-1 2 π max π min
17 erformance:lide17.doc FOUR "MAGIC QUETION" from network techniques 1., Duration of an activity having no float in an activity-on-arrow typed project model get increased by δ. What will be its effect on the overall execution time of the project? 2., Duration of an activity having no float in an activity-on-arrow typed project model get decreased by δ. What will be its effect on the overall execution time of the project? 3., Duration of an activity having no float in an activity-on-node typed project model get increased by δ. What will be its effect on the overall execution time of the project?., Can emerge any situation when an activity having no float simultaneously behaves as "positive-", "negative", "start-", and "endcritical"?
4. Network Calculations
Construction Project Management (CE 110401346) 4. Network Calculations Dr. Khaled Hyari Department of Civil Engineering Hashemite University Calculations On a Precedence Network I Early Activity Start
More informationNetwork Techniques - I. t 5 4
Performance:Slide.doc Network Techniques - I. t s v u PRT time/cost : ( Program valuation & Review Technique ) vent-on-node typed project-model with probabilistic (stochastic) data set as weights (inp)
More informationSAMPLE STUDY MATERIAL. GATE, IES & PSUs Civil Engineering
SAMPLE STUDY MATERIAL Postal Correspondence Course GATE, IES & PSUs Civil Engineering CPM & CONSTRUCTION EQUIPMENT C O N T E N T 1. CPM AND PERT... 03-16 2. CRASHING OF NETWORK... 17-20 3. ENGINEERING
More informationPROGRAMMING CPM. Network Analysis. Goal:
PROGRAMMING CPM 5/21/13 Network Analysis Goal: Calculate completion time Calculate start and finish date for each activity Identify critical activities Identify requirements and flows of resources (materials,
More information. Introduction to CPM / PERT Techniques. Applications of CPM / PERT. Basic Steps in PERT / CPM. Frame work of PERT/CPM. Network Diagram Representation. Rules for Drawing Network Diagrams. Common Errors
More informationEmbedded Systems 15. REVIEW: Aperiodic scheduling. C i J i 0 a i s i f i d i
Embedded Systems 15-1 - REVIEW: Aperiodic scheduling C i J i 0 a i s i f i d i Given: A set of non-periodic tasks {J 1,, J n } with arrival times a i, deadlines d i, computation times C i precedence constraints
More informationTime Planning and Control
Time Planning and ontrol Precedence iagram G44 NGINRING MNGMNT 1 Precedence iagramming S ctivity I LS T L n important extension to the original activity-on-node concept appeared around 14. The sole relationship
More informationDecomposition Method for Project Scheduling with Spatial Resources
Decomposition Method for Project Scheduling with Spatial Resources J.L. Hurink, A.L. Kok and J.J. Paulus University of Twente, PO box 217, 7500AE Enschede, The Netherlands, j.j.paulus@ewi.utwente.nl Project
More informationA comparison of sequencing formulations in a constraint generation procedure for avionics scheduling
A comparison of sequencing formulations in a constraint generation procedure for avionics scheduling Department of Mathematics, Linköping University Jessika Boberg LiTH-MAT-EX 2017/18 SE Credits: Level:
More informationOnline Scheduling Switch for Maintaining Data Freshness in Flexible Real-Time Systems
Online Scheduling Switch for Maintaining Data Freshness in Flexible Real-Time Systems Song Han 1 Deji Chen 2 Ming Xiong 3 Aloysius K. Mok 1 1 The University of Texas at Austin 2 Emerson Process Management
More informationA scheme developed by Du Pont to figure out
CPM Project Management scheme. A scheme developed by Du Pont to figure out Length of a normal project schedule given task durations and their precedence in a network type layout (or Gantt chart) Two examples
More informationAlgorithm Design and Analysis
Algorithm Design and Analysis LECTURE 6 Greedy Algorithms Interval Scheduling Interval Partitioning Scheduling to Minimize Lateness Sofya Raskhodnikova S. Raskhodnikova; based on slides by E. Demaine,
More informationTime allowed: 1.5 hours
Student name College of ngineering Civil ngineering Department G 404: NGINRING MNGMNT Second Semester 1438/1439H (MODL NSWR) FIRST MIDTRM XM Monday 24/6/1439H (12/3/2018G) from 7:00 to 8:30 PM Time allowed:
More informationCSE 421 Greedy Algorithms / Interval Scheduling
CSE 421 Greedy Algorithms / Interval Scheduling Yin Tat Lee 1 Interval Scheduling Job j starts at s(j) and finishes at f(j). Two jobs compatible if they don t overlap. Goal: find maximum subset of mutually
More informationChapter 4. Greedy Algorithms. Slides by Kevin Wayne. Copyright 2005 Pearson-Addison Wesley. All rights reserved.
Chapter 4 Greedy Algorithms Slides by Kevin Wayne. Copyright 2005 Pearson-Addison Wesley. All rights reserved. 1 4.1 Interval Scheduling Interval Scheduling Interval scheduling. Job j starts at s j and
More informationEmbedded Systems - FS 2018
Institut für Technische Informatik und Kommunikationsnetze Prof. L. Thiele Embedded Systems - FS 2018 Sample solution to Exercise 3 Discussion Date: 11.4.2018 Aperiodic Scheduling Task 1: Earliest Deadline
More informationCSE 417. Chapter 4: Greedy Algorithms. Many Slides by Kevin Wayne. Copyright 2005 Pearson-Addison Wesley. All rights reserved.
CSE 417 Chapter 4: Greedy Algorithms Many Slides by Kevin Wayne. Copyright 2005 Pearson-Addison Wesley. All rights reserved. 1 Greed is good. Greed is right. Greed works. Greed clarifies, cuts through,
More informationALG 5.4. Lexicographical Search: Applied to Scheduling Theory. Professor John Reif
Algorithms Professor John Reif ALG 54 input assume problem Scheduling Problems Set of n tasks in precidence relation on tasks Also, given m processors Unit time cost to process task using single processor
More informationGreedy Algorithms. Kleinberg and Tardos, Chapter 4
Greedy Algorithms Kleinberg and Tardos, Chapter 4 1 Selecting breakpoints Road trip from Fort Collins to Durango on a given route. Fuel capacity = C. Goal: makes as few refueling stops as possible. Greedy
More informationContents college 5 and 6 Branch and Bound; Beam Search (Chapter , book)! general introduction
Contents college 5 and 6 Branch and Bound; Beam Search (Chapter 3.4-3.5, book)! general introduction Job Shop Scheduling (Chapter 5.1-5.3, book) ffl branch and bound (5.2) ffl shifting bottleneck heuristic
More informationExam Spring Embedded Systems. Prof. L. Thiele
Exam Spring 20 Embedded Systems Prof. L. Thiele NOTE: The given solution is only a proposal. For correctness, completeness, or understandability no responsibility is taken. Sommer 20 Eingebettete Systeme
More informationChapter 16: Program Evaluation and Review Techniques (PERT)
Chapter 16: Program Evaluation and Review Techniques (PERT) PERT PERT is a method for determining the length of a construction project and the probability of project completion by a specified date PERT
More informationScheduling Lecture 1: Scheduling on One Machine
Scheduling Lecture 1: Scheduling on One Machine Loris Marchal October 16, 2012 1 Generalities 1.1 Definition of scheduling allocation of limited resources to activities over time activities: tasks in computer
More informationTransfer Line Balancing Problem
Transfer Line Balancing Problem Transfer Line Balancing Problem (TLBP) was introduced in [3] In comparison with the well-known assembly line balancing problems, TLBP has many new assumptions reflecting
More informationTime Planning and Control. Time-Scaled Network
Time Planning and Control Time-Scaled Network Processes of Time Planning and Control 1.Visualize and define the activities..sequence the activities (Job Logic). 3.stimate the activity duration. 4.Schedule
More informationOn the Soft Real-Time Optimality of Global EDF on Multiprocessors: From Identical to Uniform Heterogeneous
On the Soft Real-Time Optimality of Global EDF on Multiprocessors: From Identical to Uniform Heterogeneous Kecheng Yang and James H. Anderson Department of Computer Science, University of North Carolina
More informationPlanning and Scheduling of batch processes. Prof. Cesar de Prada ISA-UVA
Planning and Scheduling of batch processes Prof. Cesar de Prada ISA-UVA prada@autom.uva.es Outline Batch processes and batch plants Basic concepts of scheduling How to formulate scheduling problems Solution
More informationA Critical Path Problem in Neutrosophic Environment
A Critical Path Problem in Neutrosophic Environment Excerpt from NEUTROSOPHIC OPERATIONAL RESEARCH, Volume I. Editors: Prof. Florentin Smarandache, Dr. Mohamed Abdel-Basset, Dr. Yongquan Zhou. Foreword
More informationLaxity Release Optimization for Simulink Models
Preprints of the 19th World Congress The International Federation of Automatic Control Laxity Release Optimization for imulink Models Kaijie Qin, Guiming Luo, Xibin Zhao chool of oftware, Tsinghua University,
More informationChapter 4. Greedy Algorithms. Slides by Kevin Wayne. Copyright 2005 Pearson-Addison Wesley. All rights reserved.
Chapter 4 Greedy Algorithms Slides by Kevin Wayne. Copyright 2005 Pearson-Addison Wesley. All rights reserved. 1 4.1 Interval Scheduling Interval Scheduling Interval scheduling. Job j starts at s j and
More informationReal Time Operating Systems
Real Time Operating ystems Luca Abeni luca.abeni@unitn.it Interacting Tasks Until now, only independent tasks... A job never blocks or suspends A task only blocks on job termination In real world, jobs
More informationThe Language of Motion
The Language of Motion PowerPoint 8.1 How would you describe the motion in each of the images below? Motion of the something floating through space Motion of the pucks Motion of the cars 1 Size does matter,
More informationScheduling Lecture 1: Scheduling on One Machine
Scheduling Lecture 1: Scheduling on One Machine Loris Marchal 1 Generalities 1.1 Definition of scheduling allocation of limited resources to activities over time activities: tasks in computer environment,
More informationCSE101: Design and Analysis of Algorithms. Ragesh Jaiswal, CSE, UCSD
Course Overview Material that will be covered in the course: Basic graph algorithms Algorithm Design Techniques Greedy Algorithms Divide and Conquer Dynamic Programming Network Flows Computational intractability
More informationA Note on Modeling Self-Suspending Time as Blocking Time in Real-Time Systems
A Note on Modeling Self-Suspending Time as Blocking Time in Real-Time Systems Jian-Jia Chen 1, Wen-Hung Huang 1, and Geoffrey Nelissen 2 1 TU Dortmund University, Germany Email: jian-jia.chen@tu-dortmund.de,
More informationA Simplified Approach for Testing Real-Time Systems Based on Action Refinement
A Simplified Approach for Testing Real-Time Systems Based on Action Refinement Saddek Bensalem, Moez Krichen, Lotfi Majdoub, Riadh Robbana, Stavros Tripakis Verimag Laboratory, Centre Equation 2, avenue
More informationST. JOSEPH S COLLEGE OF ARTS & SCIENCE (AUTONOMOUS) CUDDALORE-1
ST. JOSEPH S COLLEGE OF ARTS & SCIENCE (AUTONOMOUS) CUDDALORE-1 SUB:OPERATION RESEARCH CLASS: III B.SC SUB CODE:EMT617S SUB INCHARGE:S.JOHNSON SAVARIMUTHU 2 MARKS QUESTIONS 1. Write the general model of
More informationT U M I N S T I T U T F Ü R I N F O R M A T I K. Binary Search on Two-Dimensional Data. Riko Jacob
T U M I N S T I T U T F Ü R I N F O R M A T I K Binary Search on Two-Dimensional Data Riko Jacob ABCDE FGHIJ KLMNO TUM-I08 21 Juni 08 T E C H N I S C H E U N I V E R S I T Ä T M Ü N C H E N TUM-INFO-06-I0821-0/1.-FI
More informationRecoverable Robustness in Scheduling Problems
Master Thesis Computing Science Recoverable Robustness in Scheduling Problems Author: J.M.J. Stoef (3470997) J.M.J.Stoef@uu.nl Supervisors: dr. J.A. Hoogeveen J.A.Hoogeveen@uu.nl dr. ir. J.M. van den Akker
More informationMeasurement of Particle Size Distributions
Measurement of Particle Size Distributions Static Laser Scattering perfect for Particle Size Measurement in the Range of 0.01 2000 µm Particle size measurement with state of the art laser technology: simple
More informationCPU scheduling. CPU Scheduling
EECS 3221 Operating System Fundamentals No.4 CPU scheduling Prof. Hui Jiang Dept of Electrical Engineering and Computer Science, York University CPU Scheduling CPU scheduling is the basis of multiprogramming
More informationScheduling Periodic Real-Time Tasks on Uniprocessor Systems. LS 12, TU Dortmund
Scheduling Periodic Real-Time Tasks on Uniprocessor Systems Prof. Dr. Jian-Jia Chen LS 12, TU Dortmund 08, Dec., 2015 Prof. Dr. Jian-Jia Chen (LS 12, TU Dortmund) 1 / 38 Periodic Control System Pseudo-code
More informationAperiodic Task Scheduling
Aperiodic Task Scheduling Jian-Jia Chen (slides are based on Peter Marwedel) TU Dortmund, Informatik 12 Germany Springer, 2010 2017 年 11 月 29 日 These slides use Microsoft clip arts. Microsoft copyright
More informationTitle: Department: Previous Version(s): Replaces:
Title: Department: Pediatric Massive Transfusion Protocol (MTP) Trauma Services Effective Date: 09/2014 Reviewed: Policy and Protocol Previous Version(s): Replaces: **The reader is cautioned to refer to
More informationPrinciples of AI Planning
Principles of AI Planning 5. Planning as search: progression and regression Albert-Ludwigs-Universität Freiburg Bernhard Nebel and Robert Mattmüller October 30th, 2013 Introduction Classification Planning
More informationNetwork analysis. A project is a temporary endeavor undertaken to create a "unique" product or service
Network analysis Introduction Network analysis is the general name given to certain specific techniques which can be used for the planning, management and control of projects. One definition of a project
More informationA New Sufficient Feasibility Test for Asynchronous Real-Time Periodic Task Sets
A New Sufficient Feasibility Test for Asynchronous Real-Time Periodic Task Sets Abstract The problem of feasibility analysis for asynchronous periodic task sets (ie where tasks can have an initial offset
More informationOn max-algebraic models for transportation networks
K.U.Leuven Department of Electrical Engineering (ESAT) SISTA Technical report 98-00 On max-algebraic models for transportation networks R. de Vries, B. De Schutter, and B. De Moor If you want to cite this
More informationProject Management Prof. Raghunandan Sengupta Department of Industrial and Management Engineering Indian Institute of Technology Kanpur
Project Management Prof. Raghunandan Sengupta Department of Industrial and Management Engineering Indian Institute of Technology Kanpur Module No # 07 Lecture No # 35 Introduction to Graphical Evaluation
More informationProportional Share Resource Allocation Outline. Proportional Share Resource Allocation Concept
Proportional Share Resource Allocation Outline Fluid-flow resource allocation models» Packet scheduling in a network Proportional share resource allocation models» CPU scheduling in an operating system
More informationSolving Fuzzy PERT Using Gradual Real Numbers
Solving Fuzzy PERT Using Gradual Real Numbers Jérôme FORTIN a, Didier DUBOIS a, a IRIT/UPS 8 route de Narbonne, 3062, Toulouse, cedex 4, France, e-mail: {fortin, dubois}@irit.fr Abstract. From a set of
More informationAverage-Case Performance Analysis of Online Non-clairvoyant Scheduling of Parallel Tasks with Precedence Constraints
Average-Case Performance Analysis of Online Non-clairvoyant Scheduling of Parallel Tasks with Precedence Constraints Keqin Li Department of Computer Science State University of New York New Paltz, New
More informationSingle-part-type, multiple stage systems
MIT 2.853/2.854 Introduction to Manufacturing Systems Single-part-type, multiple stage systems Stanley B. Gershwin Laboratory for Manufacturing and Productivity Massachusetts Institute of Technology Single-stage,
More informationA Critical Path Problem Using Triangular Neutrosophic Number
A Critical Path Problem Using Triangular Neutrosophic Number Excerpt from NEUTROSOPHIC OPERATIONAL RESEARCH, Volume I. Editors: Prof. Florentin Smarandache, Dr. Mohamed Abdel-Basset, Dr. Yongquan Zhou.
More informationMATHEMATICAL ANALYSIS OF LINEAR SCHEDULES Gunnar Lucko 1
MATHEMATICAL ANALYSIS OF LINEAR SCHEDULES Gunnar Lucko ABSTRACT The interplay of various managerial dimensions of construction projects requires careful control. Some criticisms have been raised regarding
More informationTDDB68 Concurrent programming and operating systems. Lecture: CPU Scheduling II
TDDB68 Concurrent programming and operating systems Lecture: CPU Scheduling II Mikael Asplund, Senior Lecturer Real-time Systems Laboratory Department of Computer and Information Science Copyright Notice:
More informationScheduling I. Today Introduction to scheduling Classical algorithms. Next Time Advanced topics on scheduling
Scheduling I Today Introduction to scheduling Classical algorithms Next Time Advanced topics on scheduling Scheduling out there You are the manager of a supermarket (ok, things don t always turn out the
More informationReal-Time Scheduling
1 Real-Time Scheduling Formal Model [Some parts of this lecture are based on a real-time systems course of Colin Perkins http://csperkins.org/teaching/rtes/index.html] Real-Time Scheduling Formal Model
More informationTime Synchronization
Massachusetts Institute of Technology Lecture 7 6.895: Advanced Distributed Algorithms March 6, 2006 Professor Nancy Lynch Time Synchronization Readings: Fan, Lynch. Gradient clock synchronization Attiya,
More informationCMSC 451: Lecture 7 Greedy Algorithms for Scheduling Tuesday, Sep 19, 2017
CMSC CMSC : Lecture Greedy Algorithms for Scheduling Tuesday, Sep 9, 0 Reading: Sects.. and. of KT. (Not covered in DPV.) Interval Scheduling: We continue our discussion of greedy algorithms with a number
More informationINSTITUT FÜR WIRTSCHAFTSTHEORIE UND OPERATIONS RESEARCH UNIVERSITÄT KARLSRUHE
INSTITUT FÜR WIRTSCHAFTSTHEORIE UND OPERATIONS RESEARCH UNIVERSITÄT KARLSRUHE Proects with Minimal and Maximal Time Lags: Construction of Activity-on-Node Networks and Applications Klaus Neumann Christoph
More informationEfficient Workplan Management in Maintenance Tasks
Efficient Workplan Management in Maintenance Tasks Michel Wilson a Nico Roos b Bob Huisman c Cees Witteveen a a Delft University of Technology, Dept of Software Technology b Maastricht University, Dept
More informationCOVER SHEET: Problem#: Points
EEL 4712 Midterm 3 Spring 2017 VERSION 1 Name: UFID: Sign here to give permission for your test to be returned in class, where others might see your score: IMPORTANT: Please be neat and write (or draw)
More informationEECS 571 Principles of Real-Time Embedded Systems. Lecture Note #7: More on Uniprocessor Scheduling
EECS 571 Principles of Real-Time Embedded Systems Lecture Note #7: More on Uniprocessor Scheduling Kang G. Shin EECS Department University of Michigan Precedence and Exclusion Constraints Thus far, we
More informationSchedule Table Generation for Time-Triggered Mixed Criticality Systems
Schedule Table Generation for Time-Triggered Mixed Criticality Systems Jens Theis and Gerhard Fohler Technische Universität Kaiserslautern, Germany Sanjoy Baruah The University of North Carolina, Chapel
More informationPrinciples of AI Planning
Principles of 5. Planning as search: progression and regression Malte Helmert and Bernhard Nebel Albert-Ludwigs-Universität Freiburg May 4th, 2010 Planning as (classical) search Introduction Classification
More informationA Multi-Periodic Synchronous Data-Flow Language
Julien Forget 1 Frédéric Boniol 1 David Lesens 2 Claire Pagetti 1 firstname.lastname@onera.fr 1 ONERA - Toulouse, FRANCE 2 EADS Astrium Space Transportation - Les Mureaux, FRANCE November 19, 2008 1 /
More information57:022 Principles of Design II Midterm Exam #2 Solutions
57:022 Principles of Design II Midterm Exam #2 Solutions Part: I II III IV V Total Possible Pts: 20 15 12 16 12 75 PART ONE Indicate "+" if True and "O" if False: _+_a. If a component's lifetime has exponential
More informationEnergy-efficient Mapping of Big Data Workflows under Deadline Constraints
Energy-efficient Mapping of Big Data Workflows under Deadline Constraints Presenter: Tong Shu Authors: Tong Shu and Prof. Chase Q. Wu Big Data Center Department of Computer Science New Jersey Institute
More informationIndeterminate Analysis Force Method 1
Indeterminate Analysis Force Method 1 The force (flexibility) method expresses the relationships between displacements and forces that exist in a structure. Primary objective of the force method is to
More informationOverview: Synchronous Computations
Overview: Synchronous Computations barriers: linear, tree-based and butterfly degrees of synchronization synchronous example 1: Jacobi Iterations serial and parallel code, performance analysis synchronous
More informationMinimizing total weighted tardiness on a single machine with release dates and equal-length jobs
Minimizing total weighted tardiness on a single machine with release dates and equal-length jobs G. Diepen J.M. van den Akker J.A. Hoogeveen institute of information and computing sciences, utrecht university
More information2. Project management
2. Project management In what follows, we consider production processes where only a single item of a specific product is produced in the planning horizon In this case specific instruments for planning
More informationTask Models and Scheduling
Task Models and Scheduling Jan Reineke Saarland University June 27 th, 2013 With thanks to Jian-Jia Chen at KIT! Jan Reineke Task Models and Scheduling June 27 th, 2013 1 / 36 Task Models and Scheduling
More informationChlorine disinfectant in the water industry
Chlorine disinfectant in the water industry Based on its chemical characteristics and its reactivity response, chlorine is very well suited for disinfection of water and to prevent contamination with bacteria
More informationMultiprocessor Scheduling II: Global Scheduling. LS 12, TU Dortmund
Multiprocessor Scheduling II: Global Scheduling Prof. Dr. Jian-Jia Chen LS 12, TU Dortmund 28, June, 2016 Prof. Dr. Jian-Jia Chen (LS 12, TU Dortmund) 1 / 42 Global Scheduling We will only focus on identical
More informationA BASE SYSTEM FOR MICRO TRAFFIC SIMULATION USING THE GEOGRAPHICAL INFORMATION DATABASE
A BASE SYSTEM FOR MICRO TRAFFIC SIMULATION USING THE GEOGRAPHICAL INFORMATION DATABASE Yan LI Ritsumeikan Asia Pacific University E-mail: yanli@apu.ac.jp 1 INTRODUCTION In the recent years, with the rapid
More informationQi Jian-xun,Sun Dedong
Physics Procedia 24 (2012) 1530 1540 2012 International Conference on Applied Physics and Industrial Engineering One Improvement Method of Reducing Duration Directly to Solve Time-Cost Tradeoff Problem
More informationEE382V: System-on-a-Chip (SoC) Design
EE82V: SystemonChip (SoC) Design Lecture EE82V: SystemonaChip (SoC) Design Lecture Operation Scheduling Source: G. De Micheli, Integrated Systems Center, EPFL Synthesis and Optimization of Digital Circuits,
More informationNon-Work-Conserving Non-Preemptive Scheduling: Motivations, Challenges, and Potential Solutions
Non-Work-Conserving Non-Preemptive Scheduling: Motivations, Challenges, and Potential Solutions Mitra Nasri Chair of Real-time Systems, Technische Universität Kaiserslautern, Germany nasri@eit.uni-kl.de
More informationNo.5 Node Grouping in System-Level Fault Diagnosis 475 identified under above strategy is precise in the sense that all nodes in F are truly faulty an
Vol.16 No.5 J. Comput. Sci. & Technol. Sept. 2001 Node Grouping in System-Level Fault Diagnosis ZHANG Dafang (± ) 1, XIE Gaogang (ΞΛ ) 1 and MIN Yinghua ( ΠΦ) 2 1 Department of Computer Science, Hunan
More informationAn Effective Chromosome Representation for Evolving Flexible Job Shop Schedules
An Effective Chromosome Representation for Evolving Flexible Job Shop Schedules Joc Cing Tay and Djoko Wibowo Intelligent Systems Lab Nanyang Technological University asjctay@ntuedusg Abstract As the Flexible
More informationEE382V-ICS: System-on-a-Chip (SoC) Design
EE8VICS: SystemonChip (SoC) EE8VICS: SystemonaChip (SoC) Scheduling Source: G. De Micheli, Integrated Systems Center, EPFL Synthesis and Optimization of Digital Circuits, McGraw Hill, 00. Additional sources:
More informationImmediate Detection of Predicates in Pervasive Environments
Immediate Detection of redicates in ervasive Environments Ajay Kshemkalyani University of Illinois at Chicago November 30, 2010 A. Kshemkalyani (U Illinois at Chicago) Immediate Detection of redicates......
More informationA Critical Path Problem Using Triangular Neutrosophic Number
A ritical Path Problem Using Triangular Neutrosophic Number Mai Mohamed 1 Department of Operations Research Faculty of omputers and Informatics Zagazig University, Sharqiyah, Egypt Yongquan Zhou 2 ollege
More informationBounding the End-to-End Response Times of Tasks in a Distributed. Real-Time System Using the Direct Synchronization Protocol.
Bounding the End-to-End Response imes of asks in a Distributed Real-ime System Using the Direct Synchronization Protocol Jun Sun Jane Liu Abstract In a distributed real-time system, a task may consist
More informationAndrew Morton University of Waterloo Canada
EDF Feasibility and Hardware Accelerators Andrew Morton University of Waterloo Canada Outline 1) Introduction and motivation 2) Review of EDF and feasibility analysis 3) Hardware accelerators and scheduling
More informationAS computer hardware technology advances, both
1 Best-Harmonically-Fit Periodic Task Assignment Algorithm on Multiple Periodic Resources Chunhui Guo, Student Member, IEEE, Xiayu Hua, Student Member, IEEE, Hao Wu, Student Member, IEEE, Douglas Lautner,
More informationScheduling Stochastically-Executing Soft Real-Time Tasks: A Multiprocessor Approach Without Worst-Case Execution Times
Scheduling Stochastically-Executing Soft Real-Time Tasks: A Multiprocessor Approach Without Worst-Case Execution Times Alex F. Mills Department of Statistics and Operations Research University of North
More informationAUTOMATED TEMPLATE MATCHING METHOD FOR NMIS AT THE Y-12 NATIONAL SECURITY COMPLEX
AUTOMATED TEMPLATE MATCHING METHOD FOR NMIS AT THE Y-1 NATIONAL SECURITY COMPLEX J. A. Mullens, J. K. Mattingly, L. G. Chiang, R. B. Oberer, J. T. Mihalczo ABSTRACT This paper describes a template matching
More informationAllocation of multiple processors to lazy boolean function trees justification of the magic number 2/3
Allocation of multiple processors to lazy boolean function trees justification of the magic number 2/3 Alan Dix Computer Science Department University of York, York, YO1 5DD, U.K. alan@uk.ac.york.minster
More informationChap 4. Software Reliability
Chap 4. Software Reliability 4.2 Reliability Growth 1. Introduction 2. Reliability Growth Models 3. The Basic Execution Model 4. Calendar Time Computation 5. Reliability Demonstration Testing 1. Introduction
More informationEnergy-Efficient Real-Time Task Scheduling in Multiprocessor DVS Systems
Energy-Efficient Real-Time Task Scheduling in Multiprocessor DVS Systems Jian-Jia Chen *, Chuan Yue Yang, Tei-Wei Kuo, and Chi-Sheng Shih Embedded Systems and Wireless Networking Lab. Department of Computer
More informationWork in Progress: Reachability Analysis for Time-triggered Hybrid Systems, The Platoon Benchmark
Work in Progress: Reachability Analysis for Time-triggered Hybrid Systems, The Platoon Benchmark François Bidet LIX, École polytechnique, CNRS Université Paris-Saclay 91128 Palaiseau, France francois.bidet@polytechnique.edu
More informationTardiness Bounds under Global EDF Scheduling on a Multiprocessor
Tardiness ounds under Global EDF Scheduling on a Multiprocessor UmaMaheswari C. Devi and James H. Anderson Department of Computer Science The University of North Carolina at Chapel Hill Abstract This paper
More informationModule 5: CPU Scheduling
Module 5: CPU Scheduling Basic Concepts Scheduling Criteria Scheduling Algorithms Multiple-Processor Scheduling Real-Time Scheduling Algorithm Evaluation 5.1 Basic Concepts Maximum CPU utilization obtained
More informationAnnouncements. Project #1 grades were returned on Monday. Midterm #1. Project #2. Requests for re-grades due by Tuesday
Announcements Project #1 grades were returned on Monday Requests for re-grades due by Tuesday Midterm #1 Re-grade requests due by Monday Project #2 Due 10 AM Monday 1 Page State (hardware view) Page frame
More informationChapter 6: CPU Scheduling
Chapter 6: CPU Scheduling Basic Concepts Scheduling Criteria Scheduling Algorithms Multiple-Processor Scheduling Real-Time Scheduling Algorithm Evaluation 6.1 Basic Concepts Maximum CPU utilization obtained
More informationA lower bound on deterministic online algorithms for scheduling on related machines without preemption
Theory of Computing Systems manuscript No. (will be inserted by the editor) A lower bound on deterministic online algorithms for scheduling on related machines without preemption Tomáš Ebenlendr Jiří Sgall
More informationEmbedded Systems 14. Overview of embedded systems design
Embedded Systems 14-1 - Overview of embedded systems design - 2-1 Point of departure: Scheduling general IT systems In general IT systems, not much is known about the computational processes a priori The
More information