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

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Presentation on theme: "Yuri N. Sotskov 1, Omid Gholami 2, Frank Werner 3 1. United Institute of Informatics Problems, Minsk, Belarus, 2. Islamic."— Presentation transcript:

1 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,

2  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 3 Train road map in Belarus

4  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

5  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

6  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 *

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

8  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 *

9  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 *

10  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 *

11  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 *

12  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 *

13  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 *

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

15 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 16 Required time (Algorithm Ordinal-SCT) to schedule different job-shops: 10 ≤ n = m ≤ 60

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

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

19 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 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

21 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


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