Yuri N. Sotskov 1, Omid Gholami 2, Frank Werner 3 1. United Institute of Informatics Problems, Minsk, Belarus, 2. Islamic.

Slides:



Advertisements
Similar presentations
TWO STEP EQUATIONS 1. SOLVE FOR X 2. DO THE ADDITION STEP FIRST
Advertisements

You have been given a mission and a code. Use the code to complete the mission and you will save the world from obliteration…
Constraint Satisfaction Problems
Chapter 1 The Study of Body Function Image PowerPoint
Copyright © 2011, Elsevier Inc. All rights reserved. Chapter 5 Author: Julia Richards and R. Scott Hawley.
1 Copyright © 2010, Elsevier Inc. All rights Reserved Fig 2.1 Chapter 2.
By D. Fisher Geometric Transformations. Reflection, Rotation, or Translation 1.
Business Transaction Management Software for Application Coordination 1 Business Processes and Coordination.
and 6.855J Cycle Canceling Algorithm. 2 A minimum cost flow problem , $4 20, $1 20, $2 25, $2 25, $5 20, $6 30, $
and 6.855J Spanning Tree Algorithms. 2 The Greedy Algorithm in Action
Jeopardy Q 1 Q 6 Q 11 Q 16 Q 21 Q 2 Q 7 Q 12 Q 17 Q 22 Q 3 Q 8 Q 13
Jeopardy Q 1 Q 6 Q 11 Q 16 Q 21 Q 2 Q 7 Q 12 Q 17 Q 22 Q 3 Q 8 Q 13
Title Subtitle.
0 - 0.
DIVIDING INTEGERS 1. IF THE SIGNS ARE THE SAME THE ANSWER IS POSITIVE 2. IF THE SIGNS ARE DIFFERENT THE ANSWER IS NEGATIVE.
MULTIPLYING MONOMIALS TIMES POLYNOMIALS (DISTRIBUTIVE PROPERTY)
ADDING INTEGERS 1. POS. + POS. = POS. 2. NEG. + NEG. = NEG. 3. POS. + NEG. OR NEG. + POS. SUBTRACT TAKE SIGN OF BIGGER ABSOLUTE VALUE.
SUBTRACTING INTEGERS 1. CHANGE THE SUBTRACTION SIGN TO ADDITION
MULT. INTEGERS 1. IF THE SIGNS ARE THE SAME THE ANSWER IS POSITIVE 2. IF THE SIGNS ARE DIFFERENT THE ANSWER IS NEGATIVE.
Addition Facts
Year 6 mental test 5 second questions
MALT©2006 Maths/Fractions Slide Show : Lesson 2
How fractions, decimals and percentages work together.
2010 fotografiert von Jürgen Roßberg © Fr 1 Sa 2 So 3 Mo 4 Di 5 Mi 6 Do 7 Fr 8 Sa 9 So 10 Mo 11 Di 12 Mi 13 Do 14 Fr 15 Sa 16 So 17 Mo 18 Di 19.
ZMQS ZMQS
REVIEW: Arthropod ID. 1. Name the subphylum. 2. Name the subphylum. 3. Name the order.
BT Wholesale October Creating your own telephone network WHOLESALE CALLS LINE ASSOCIATED.
§1 Greedy Algorithms ALGORITHM DESIGN TECHNIQUES
ABC Technology Project
2 |SharePoint Saturday New York City
© S Haughton more than 3?
© Charles van Marrewijk, An Introduction to Geographical Economics Brakman, Garretsen, and Van Marrewijk.
© Charles van Marrewijk, An Introduction to Geographical Economics Brakman, Garretsen, and Van Marrewijk.
VOORBLAD.
15. Oktober Oktober Oktober 2012.
Name Convolutional codes Tomashevich Victor. Name- 2 - Introduction Convolutional codes map information to code bits sequentially by convolving a sequence.
1 Breadth First Search s s Undiscovered Discovered Finished Queue: s Top of queue 2 1 Shortest path from s.
Vienna, Hofburg the Imperial Tableware and the Sissy exhibition
Factor P 16 8(8-5ab) 4(d² + 4) 3rs(2r – s) 15cd(1 + 2cd) 8(4a² + 3b²)
Squares and Square Root WALK. Solve each problem REVIEW:
1..
© 2012 National Heart Foundation of Australia. Slide 2.
Lets play bingo!!. Calculate: MEAN Calculate: MEDIAN
Past Tense Probe. Past Tense Probe Past Tense Probe – Practice 1.
Multiplication (RN) 12 X ⅓ = 12 groups of ⅓ or 12/3 = 4 ⅔ x ¾ = ? ¼ x ½ = ?
Understanding Generalist Practice, 5e, Kirst-Ashman/Hull
Chapter 5 Test Review Sections 5-1 through 5-4.
GG Consulting, LLC I-SUITE. Source: TEA SHARS Frequently asked questions 2.
1 First EMRAS II Technical Meeting IAEA Headquarters, Vienna, 19–23 January 2009.
Addition 1’s to 20.
25 seconds left…...
Test B, 100 Subtraction Facts
Januar MDMDFSSMDMDFSSS
Week 1.
We will resume in: 25 Minutes.
©Brooks/Cole, 2001 Chapter 12 Derived Types-- Enumerated, Structure and Union.
Bottoms Up Factoring. Start with the X-box 3-9 Product Sum
1 Unit 1 Kinematics Chapter 1 Day
PSSA Preparation.
Ismael R. de Farias, Jr. 1 Joint work with Ernee Kozyreff 1 and Ming Zhao 2 1 Texas Tech 2 SAS Integer Programming with Complementarity Constraints.
How Cells Obtain Energy from Food
By Rasmussen College. 1. What majors or programs do you offer? 2. What is the average length of your programs? 3. What percentage of your students graduate?
1 Graphs with Maximal Induced Matchings of the Same Size Ph. Baptiste 1, M. Kovalyov 2, Yu. Orlovich 3, F. Werner 4, I. Zverovich 3 1 Ecole Polytechnique,
Solving a job-shop scheduling problem by an adaptive algorithm based on learning Yuri N. Sotskov 1, Omid Gholami 2, Frank Werner 3 1. United Institute.
Introduction to Job Shop Scheduling Problem Qianjun Xu Oct. 30, 2001.
Presentation transcript:

Yuri N. Sotskov 1, Omid Gholami 2, Frank Werner 3 1. United Institute of Informatics Problems, Minsk, Belarus, 2. Islamic Azad university - Mahmudabad Branch, Mahmudabad, Iran, 3. Faculty of Mathematics, Otto-von-Guericke-University, Magdeburg, Germany, OPTIMA 2012, Costa da Caparica, Portugal September 23-30,

 Introduction  Literature Review for Single-Track Railway Systems  Problem Setting in Terms of a Job-Shop  Mixed (Disjunctive) Graph Formulation of a Job-Shop Scheduling Problem  Heuristic Algorithms  Computational Results 2

3 Train road map in Belarus

 Szpigel (1973): B&B algorithm, results for 5 sections and 10 trains  Cai and Goh (1994): greedy algorithm  Carey and Lockwood (1995): binary mixed integer programming model  Mladenovic and Cangalovic (2007): constraint programming approach  Zhou and Zhong (2007): B&B algorithm, resource-constrained project scheduling problem  Liu and Kozan (2011): no-wait condition for prioritized trains, recursive procedure  Sotskov and Gholami (2012): shifting bottleneck procedure 4

 set of railroad sections (machines) ◦ M ={ M 1, M 2, …, M m }  set of trains (jobs) ◦ J ={ J 1, J 2, …, J n }  the sequence of the job operations on the corresponding machines is given for any job J i : ◦ O i = (O i 1, O i 2, …, O i ni ) 5

 G = (Q, C, D) -> G= (Q, C  D i, Ø) | | 4 9 | 2 2 | 2 8 | | 3 4 | | 9 8 | 2 1 | 6 8 | 2 Mixed graph G=(Q, C, D) for a job-shop problem with three jobs (trains) and seven machines (railroad sections) 0 *

Algorithms:  Ordinal-algorithm  MaxPT-algorithm  MinPT-algorithm Priority rules for comparing conflict jobs:  Release time  Completion time  Due date 7

 The algorithm considers subsequently the first requests of all jobs, the second requests of all jobs, etc. It compares the operation O i j currently considered with the other operations O k l to be processed on the same machine. Based on the chosen priority rule, a direct arc is created | | 4 9 | 2 2 | 2 8 | | 3 4 | | 9 8 | 2 1 | 6 8 | 2 0 *

 The algorithm considers subsequently the first requests of all jobs, the second requests of all jobs, etc. It compares the operation O i j currently considered with the other operations O k l to be processed on the same machine. Based on the chosen priority rule, a direct arc is created | | 4 9 | 2 2 | 2 8 | | 3 4 | | 9 8 | 2 1 | 6 8 | 2 0 *

 Sort the jobs in non-increasing order of their total processing times and consider all operations of a job subsequently. Then it compares the operation O i j currently considered with the other operations O k l to be processed on the same machine. Based on the chosen priority rule, a direct arc is created | | 4 9 | 2 2 | 2 8 | | 3 4 | | 9 8 | 2 1 | 6 8 | 2 0 *

 Sort the jobs in non-increasing order of their total processing times and consider all operations of a job subsequently. Then it compares the operation O i j currently considered with the other operations O k l to be processed on the same machine. Based on the chosen priority rule, a direct arc is created | | 4 9 | 2 2 | 2 8 | | 3 4 | | 9 8 | 2 1 | 6 8 | 2 0 *

 Sort the jobs in non-decreasing order of their total processing times and consider all operations of a job subsequently. Then it compares the operation O i j currently considered with the other operations O k l to be processed on the same machine. Based on the chosen priority rule, a direct arc is created | | 4 9 | 2 2 | 2 8 | | 3 4 | | 9 8 | 2 1 | 6 8 | 2 0 *

 Sort the jobs in non-decreasing order of their total processing times and consider all operations of a job subsequently. Then it compares the operation O i j currently considered with the other operations O k l to be processed on the same machine. Based on the chosen priority rule, a direct arc is created | | 4 9 | 2 2 | 2 8 | | 3 4 | | 9 8 | 2 1 | 6 8 | 2 0 *

Digraph (Q, C  D i, Ø) defining a solution of the job-shop problem 0 *

15 Objective function values of the obtained schedules for the job-shop problems with the criterion ∑T i SRT (Shortest Release Time) SCT (Shortest Completion Time) SDD (Shortest Due-Date)

16 Required time (Algorithm Ordinal-SCT) to schedule different job-shops: 10 ≤ n = m ≤ 60

17 Required time (Algorithm Ordinal-SCT) to schedule different job-shops: m = 20 and 10 ≤ n ≤ 110

18 Required time (Algorithm Ordinal-SCT) to schedule different job-shops: n = 20 and 10 ≤ m ≤ 110

19 Best constructive algorithm for the train scheduling problem among the tested ones is the Ordinal-SCT (Shortest Completion Time) algorithm. Intel Core 2 Due CPU, 2.00 GHz, Ram 2 GB, Windows 7 Ultimate, Borland Delphi programming language.

20 BenchmarkOrdinal-SCTShifting Bottleneck EDDFCFS MT 6 (6×6) MT 10 (10×10) Job-shop 10 (10×10) Job-shop 18 (18×5) Comparison of different algorithms for the makespan criterion on some benchmark instances

Heuristic Algorithms for a Job-Shop Problem with Minimizing Total Job Tardiness Yuri N. Sotskov 1, Omid Gholami 2, Frank Werner 3 1. United Institute of Informatics Problems, Minsk, Belarus, 2. Islamic Azad university - Mahmudabad Branch, Mahmudabad, Iran, 3. Faculty of Mathematics, Otto-von-Guericke-University, Magdeburg, Germany, 21