Snm Tauctv


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Fashion, apparel, textile, merchandising, garments

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Snm Tauctv

  1. 1. Scheduling by Applying Tabu Search : A Textile Case Prepared by: Ali Orhan AYDIN
  2. 2. Scope of the Presentation <ul><li>To present a study </li></ul><ul><li>which </li></ul><ul><li>explains job scheduling example </li></ul><ul><li>in a textile system </li></ul><ul><li>by applying Tabu Search </li></ul>
  3. 3. Introduction <ul><li>Systems are set of components which are related by some form of interaction, and which act together to achieve some objectives. </li></ul><ul><li>One of the most important man-made systems is production system . </li></ul><ul><li>Major aim of the most of the manufacturing and service systems is to make profit. </li></ul><ul><li>Therefore, they produce goods and services by using scarce resources. </li></ul><ul><li>To achieve this purpose efficiently use of resources becomes key succession factor. </li></ul><ul><li>Scheduling promises in helping production systems to pursue this goal. </li></ul>
  4. 4. Introduction <ul><li>Scheduling is a series of activities aim of which is to assign jobs and/or resources to men and/or machines in production systems . </li></ul><ul><li>By performing these activities, it is targeted to minimize production time and costs, by providing information to a production system on what to make, when to make, with which staff, and on which machine . </li></ul>
  5. 5. Introduction <ul><li>M any tools and approaches are proposed to achieve a good schedule . </li></ul><ul><li>Pioneer tool for scheduling and planning is Gantt Chart . </li></ul>
  6. 6. Introduction <ul><li>Most basic method for scheduling purposes is to use dispatching rules. </li></ul><ul><ul><li>SIRO: Service in Random Order </li></ul></ul><ul><ul><li>ERD: Earliest Release Date First </li></ul></ul><ul><ul><li>EDD: Earliest Due Date First </li></ul></ul><ul><ul><li>MS: Minimum Slack First </li></ul></ul><ul><ul><li>WSPT: Weighted Shortest Processing Time First </li></ul></ul><ul><ul><li>LPT: Longest Processing Time First </li></ul></ul><ul><ul><li>SST: Shortest Setup Time First </li></ul></ul><ul><ul><li>CP: Critical Path </li></ul></ul><ul><ul><li>LNS: Largest Number of Successors </li></ul></ul><ul><ul><li>SQNO: Shortest Queue at the Next Operation </li></ul></ul>
  7. 7. Introduction <ul><li>T here are also some composite dispatching rule s. </li></ul><ul><ul><li>ATC: Apparent Tardiness Cost is a rule that combines WSPT and MS . </li></ul></ul><ul><ul><li>ATCS: Apparent Tardiness Cost with Setups is a rule that combines WSPT, MS and SST . </li></ul></ul><ul><li>All of these dispatching rules prioritize all the jobs that are waiting for processing on a machine. </li></ul>
  8. 8. Introduction <ul><li>To find optimum schedule there are exact optimization methods. </li></ul><ul><li>If the scheduling problem is inherently easy linear programs can be used to solve them. </li></ul><ul><ul><li>I nteger program ming </li></ul></ul><ul><ul><li>B ranch-and-bound methods </li></ul></ul><ul><ul><li>C utting plane methods </li></ul></ul><ul><ul><li>S ome hybrid methods </li></ul></ul>
  9. 9. Introduction <ul><li>On the other hand, scheduling problems are usually Non-Deterministic Polynomial-Time Hard (NP-Hard) . </li></ul><ul><li>In some cases, solving NP-Hard problems may take enormous time. Usually, in real life that amount of computer time is not available. Therefore, finding optimal solution is nearly impossible . </li></ul><ul><li>M any methods proposed to find a good solution to scheduling problems of production systems in a relatively short time . </li></ul><ul><li>Those solutions are acceptable and feasible solutions that presumably is not far from optimal . </li></ul>
  10. 10. Introduction <ul><li>M any methods proposed to find a good solution to scheduling problems of production systems in a relatively short time . </li></ul><ul><li>In such cases, beam search, local search and global search heuristics can be applied . </li></ul>
  11. 11. Introduction <ul><li>Local Search Heuristics </li></ul><ul><ul><li>Hill-Climbing </li></ul></ul><ul><ul><li>Min-Conflicts </li></ul></ul><ul><ul><li>Min-Conflicts-Random-Walk </li></ul></ul><ul><ul><li>Steepest-Descent-Random-Walk </li></ul></ul><ul><ul><li>GSAT </li></ul></ul><ul><ul><li>WalkSat </li></ul></ul><ul><ul><li>Simulated Annealing </li></ul></ul><ul><ul><li>Tabu-Search. </li></ul></ul>
  12. 12. Introduction <ul><li>Global Search Heuristics </li></ul><ul><ul><li>Evolutionary algorithms </li></ul></ul><ul><ul><ul><li>Genetic algorithms </li></ul></ul></ul><ul><ul><ul><li>Memetic algorithms </li></ul></ul></ul><ul><ul><li>Population based algorithms. </li></ul></ul><ul><ul><ul><li>Particle swarm algorithms </li></ul></ul></ul><ul><ul><ul><li>Ant colony algorithms </li></ul></ul></ul><ul><ul><ul><li>Bees algorithms </li></ul></ul></ul>
  13. 13. Introduction <ul><li>All of these approaches have some advantages and disadvantages. </li></ul><ul><li>It is a fact that global search heuristics requires more time to achieve acceptable solution. </li></ul><ul><li>On the other hand, global and local search heuristics give nearly the same results. </li></ul><ul><li>Therefore, in this paper one of the local search heuristic tabu search is applied in scheduling problem of a textile system. </li></ul>
  14. 14. Literature Review: Greedies <ul><li>Among many heuristic algorithms local search algorithms are reviewed in this section. </li></ul><ul><li>In local search, an initial configuration (valuation of variables) is generated and the algorithm moves from the current configuration to a neighborhood configurations until a solution (decision problems) or a good solution (optimization problems) has been found or the resources available are exhausted. </li></ul><ul><li>Usually, local search algorithms are called as greedy algorithms; since, they try to reach global maximum or minimum by searching the space locally. </li></ul><ul><li>Local search algorithms move from solution to solution in the space of candidate solutions (the search space) until a solution deemed optimal is found or a time bound is elapsed. </li></ul>
  15. 15. Literature Review: Greedies <ul><li>General Psuedo Code of local-search algorithms </li></ul>
  16. 16. Literature Review: Greedies <ul><li>Hill-climbing is probably the most known algorithm of local search. </li></ul><ul><li>Algorithm of hill-climbing </li></ul>
  17. 17. Literature Review: Greedies <ul><li>Psuedo Code of Min-Conflicts </li></ul>
  18. 18. Literature Review: Greedies <ul><li>Min-Conflicts-Random-Walk algorithm </li></ul>
  19. 19. Literature Review: Greedies <ul><li>Steepest-Descent-Random-Walk algorithm </li></ul>
  20. 20. Literature Review: Greedies <ul><li>Tabu Search algorithm </li></ul>
  21. 21. Literature Review: Greedies <ul><li>Tabu search is applied in many resource allocation problems like: </li></ul><ul><ul><li>cell formation problem </li></ul></ul><ul><ul><li>designing manufacturing cells </li></ul></ul><ul><ul><li>optimization of Process Plans </li></ul></ul><ul><ul><li>Parallel Flowshop Scheduling </li></ul></ul><ul><ul><li>project scheduling </li></ul></ul><ul><ul><li>flow-shop scheduling </li></ul></ul><ul><ul><li>job shop scheduling </li></ul></ul>
  22. 22. Literature Review: Greedies <ul><li>There are also hybrid algorithms which are developed by combining tabu search and other heuristics to find better solutions to the problems. </li></ul><ul><ul><li>Tabu Search and Simulated Annealing is combined by Zolfaghari and Liang </li></ul></ul><ul><ul><li>Tabu Search and Genetic Algorithm is hybridized by Ombuki and Ventresca </li></ul></ul><ul><ul><li>Tabu and Scatter Search is combined by Blazewicz, Glover, and Kasprzak </li></ul></ul>
  23. 23. Literature Review: Greedies <ul><li>Moreover, like it is aimed in this paper, tabu search is used in scheduling jobs in textile manufacturing systems and proved to be efficient. </li></ul><ul><li>In their paper, Tucci and Rinaldi [31] describes a typical fabric production system and applies tabu search. </li></ul>
  24. 24. Applying Tabu Search: A Textile Case <ul><li>Aim of this paper is to apply tabu search in a textile manufacturing industry. </li></ul><ul><li>Specifically, fabric production is taken under consideration. In such systems, to produce fabric first raw strings are dyed. Afterwards, they are tied to conics and sent to fabric department to be weaved. Final stage is quality control and while controlling fabric, they are rolled on cylinders. </li></ul>
  25. 25. Applying Tabu Search: A Textile Case <ul><li>This paper focuses on scheduling jobs in weaving process while trying to minimize maximum lateness on single machine. </li></ul>
  26. 26. Applying Tabu Search: A Textile Case <ul><li>Bill-of-Material of such fabric product </li></ul>
  27. 27. Applying Tabu Search: A Textile Case <ul><li>As an example, here 20 jobs are described . </li></ul>
  28. 28. Applying Tabu Search: A Textile Case <ul><li>Length of ordered fabrics and total processing time of each job. </li></ul>
  29. 29. Applying Tabu Search: A Textile Case <ul><li>Due dates of jobs. </li></ul>
  30. 30. Applying Tabu Search: A Textile Case <ul><li>In the frame of the given information, initial solution for the problem is obtained by applying dispatching rule Earliest Due Date. </li></ul>
  31. 31. Applying Tabu Search: A Textile Case <ul><li>Afterwards, Tabu Search is applied. 20 iterations are performed by using OpenTS open source code in JAVA environment. </li></ul>
  32. 32. Conclusion <ul><li>Tabu Search is an effective algorithm to achieve job scheduling objectives like maximum lateness/tardiness minimization. </li></ul><ul><li>It requires less processing time than global search heuristics. </li></ul><ul><li>As the job size increase, time requirement also increases to generate a schedule. </li></ul><ul><li>Therefore, Tabu Search can be suggested to mass production systems; since, they deal with many jobs. </li></ul>
  33. 33. Conclusion <ul><li>The study just tried to simply explain the Tabu Search concept. </li></ul><ul><li>The problem provided is oversimplified. </li></ul><ul><li>Actual textiles systems deal with much more higher amount of jobs. </li></ul><ul><li>Because, it is aimed to show how Tabu Search can be used in minimize maximum tardiness problem. </li></ul><ul><li>In this manner, a good feasible solution but not optimal is found. </li></ul>
  34. 34. <ul><li>THANK YOU </li></ul><ul><li>FOR </li></ul><ul><li>LISTENING </li></ul>