Decision Support System


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DSS for Planning and Scheduling

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Decision Support System

  1. 1. DECISION SUPPORT SYSTEMS for planning and scheduling in practice
  2. 2. DECISION SUPPORT SYSTEMS for planning and scheduling in practice I. Application Areas, Infrastructures, General Architectural Issues II. System Requirements III. Planning and Scheduling Techniques IV. System Implementations Commercial Packages
  3. 3. Part I. Application Areas, Infrastructures, General Architectural Issues
  4. 4. <ul><li>Application Areas </li></ul><ul><ul><li>Planning and Scheduling in Manufacturing and Services </li></ul></ul><ul><li>Infrastructures </li></ul><ul><ul><li>In Manufacturing </li></ul></ul><ul><ul><li>In Supply Chain Management </li></ul></ul><ul><ul><li>In Services </li></ul></ul><ul><li>General Issues regarding </li></ul><ul><ul><li>Systems Architecture </li></ul></ul><ul><ul><li>For Production Scheduling </li></ul></ul><ul><ul><li>For Workforce Scheduling </li></ul></ul>
  5. 5. APPLICATION AREAS OF PLANNING AND SCHEDULING <ul><li>Manufacturing </li></ul><ul><ul><li>Process </li></ul></ul><ul><ul><li>Discrete </li></ul></ul><ul><ul><li>Automotive </li></ul></ul><ul><ul><li>Food and Snacks </li></ul></ul><ul><li>Services: </li></ul><ul><ul><li>Crew Scheduling (Airlines) </li></ul></ul><ul><ul><li>Workforce Scheduling (Call Centers) </li></ul></ul><ul><ul><li>Reservation Systems and Yield Management </li></ul></ul>
  6. 6. INFORMATION SYSTEM INFRASTRUCTURE IN MANUFACTURING ENVIRONMENTS <ul><li>Interfaces with Forecasting, Medium Term, and Long Term Planning </li></ul><ul><li>Interfaces with Product Design and Facility Layout </li></ul>
  7. 7. <ul><li>Workforce Scheduling in </li></ul><ul><ul><li>Call Centers </li></ul></ul><ul><ul><li>Hospitals </li></ul></ul><ul><li>Reservation Systems in </li></ul><ul><ul><li>Airlines </li></ul></ul><ul><ul><li>Hotels </li></ul></ul><ul><ul><li>Car Rentals </li></ul></ul>INFORMATION SYSTEM INFRASTRUCTURE IN SERVICE ENVIRONMENTS
  8. 8. Part II. Important Issues in Design of Decision Support Systems
  9. 9. IMPORTANT ISSUES IN DESIGN OF DECISION SUPPORT SYSTEMS <ul><li>Module Design and Interfacing </li></ul><ul><li>GUI Design </li></ul><ul><li>Design of Link Between GUI and Algorithm Library </li></ul><ul><li>Internal Reoptimization </li></ul><ul><li>External Reoptimization </li></ul>
  10. 10. MODULAR (OBJECT-ORIENTED) DESIGN <ul><li>Standardization of Data Transfers Between Modules. </li></ul><ul><li>Data Concerning: </li></ul><ul><ul><li>Jobs (Operations) </li></ul></ul><ul><ul><li>Work Centers (Machines) </li></ul></ul><ul><ul><li>Schedules </li></ul></ul><ul><li>Have to be Properly Organized in order to make Transfer of Data Easy. </li></ul><ul><li>EXAMPLE : Plugging in New Algorithm in Existing System should be Easy. </li></ul>
  11. 11. GUI’S SHOULD ALLOW: <ul><li>Interactive Optimization </li></ul><ul><ul><li>Freezing Jobs and Reoptimize </li></ul></ul><ul><ul><li>Creating New Schedules by Combining Different Parts from Different Schedules </li></ul></ul><ul><li>Cascading and Propagation Effects </li></ul><ul><ul><li>After a Change or Mutation by the User, the System </li></ul></ul><ul><ul><li>does Feasibility Analysis </li></ul></ul><ul><ul><li>takes care of Cascading and Propagation Effects, </li></ul></ul><ul><ul><li>does Internal Reoptimization </li></ul></ul>
  12. 12. GRAPHICS USER INTERFACES FOR SCHEDULING PRODUCTION PROCESSES <ul><li>Gantt Chart Interface </li></ul><ul><li>Dispatch List Interface </li></ul><ul><li>Time Buckets </li></ul><ul><li>Throughput Diagrams </li></ul>
  13. 13. IMPORTANT OBJECTIVES TO BE DISPLAYED <ul><li>Due Date Related </li></ul><ul><ul><li>Number of Late Jobs </li></ul></ul><ul><ul><li>Maximum Lateness </li></ul></ul><ul><ul><li>Average Lateness </li></ul></ul><ul><li>Productivity and Inventory Related </li></ul><ul><ul><li>Total Setup Time </li></ul></ul><ul><ul><li>Total Machine Idle Time </li></ul></ul><ul><ul><li>Average Time Jobs Remain in System </li></ul></ul>
  14. 14. Part III. Planning and Scheduling Optimization Techniques
  15. 15. PLANNING AND SCHEDULING OPTIMIZATION TECHNIQUES <ul><li>Dispatching Rules </li></ul><ul><li>Composite Dispatching Rules </li></ul><ul><li>Dynamic Programming </li></ul><ul><li>Integer Programming </li></ul><ul><li>Column Generation </li></ul><ul><li>Branch and Bound </li></ul><ul><li>Beam Search </li></ul>
  16. 16. <ul><li>Local Search </li></ul><ul><li>Decomposition Techniques </li></ul><ul><ul><li>Temporal </li></ul></ul><ul><ul><li>Machine (Shifting Bottleneck) </li></ul></ul><ul><li>Drum-Buffer-Rope </li></ul><ul><li>Hybrid Methods </li></ul>PLANNING AND SCHEDULING OPTIMIZATION TECHNIQUES (continued)
  17. 17. IMPORTANT CHARACTERISTICS OF OPTIMIZATION TECHNIQUES <ul><li>Quality of Solutions Obtained (How Close to Optimal?) </li></ul><ul><li>Amount of CPU-Time Needed (Real-Time on a PC?) </li></ul><ul><li>Ease of Development and Implementation (How much time needed to code, test, adjust and modify) </li></ul>
  18. 18. Local Search Value Objective Function Dispatching Rules Beam Search Branch and Bound CPU - Time
  19. 19. COMPOSITE PRIORITY RULE THAT IS MIXTURE OF THREE BASIC PRIORITY RULES: <ul><li>Weighted Shortest Processing Time First </li></ul><ul><li>Earliest Due Date First </li></ul><ul><li>Shortest Setup Time First </li></ul>
  20. 20. DYNAMIC PROGRAMMING Characterizing Equations: <ul><li>(i) Initial Conditions </li></ul><ul><li>(ii) Recursive Relation </li></ul><ul><li>(iii) Optimal Value Function </li></ul><ul><li>Example: Consider a Single Machine and </li></ul><ul><li>Objective Function </li></ul>
  21. 21. INTEGER PROGRAMMING FORMULATIONS <ul><li>Hard Problems can often be Formulated as I.P.s. </li></ul><ul><li>These I.P.s are often Solved via Branch and Bound </li></ul><ul><li>Many Applications of I.P. Formulations in </li></ul><ul><ul><li>Workforce scheduling </li></ul></ul><ul><ul><li>Crew Scheduling </li></ul></ul>
  22. 22. DISJUNCTIVE PROGRAMMING FORMULATIONS <ul><li>Hard Problems can often be Formulated as Disjunctive Programs </li></ul><ul><li>These Programs are often Solved via Branch and Bound </li></ul><ul><li>Many Applications of Disjunctive Programs in Job Shop Scheduling </li></ul>
  23. 23. LOCAL (NEIGHBORHOOD) SEARCH METHODS <ul><li>Simulated Annealing (Probabilistic Method) </li></ul><ul><li>Tabu-Search (Deterministic Method) </li></ul><ul><li>Genetic Algorithms </li></ul>
  24. 24. IMPORTANT CHARACTERISTICS OF LOCAL SEARCH PROCEDURES <ul><li>Schedule Representation Needed for Procedure </li></ul><ul><li>The Neighborhood Design </li></ul><ul><li>The Search Process within the Neighborhood </li></ul><ul><li>The Acceptance-Rejection Criterion </li></ul>
  25. 25. DECOMPOSITION TECHNIQUES <ul><li>Machine Decomposition (Shifting Bottleneck Techniques) </li></ul><ul><li>Temporal Decomposition </li></ul><ul><li>IMPORTANT CHARACTERISTICS OF DECOMPOSITION TECHNIQUES </li></ul><ul><li>Select as the next Subproblem to Solve always the one that Appears the Hardest (“Follow the Path of the Most Resistance”) </li></ul><ul><li>After the Completion of Each Step, Re optimize all the Steps that were Done Before </li></ul>
  26. 26. HYBRID METHODS <ul><li>Scheduling techniques can be Combined in Series </li></ul><ul><ul><ul><li>E.G., FIRST USE A DISPATCHING RULE, THEN FOLLOW UP WITH A LOCAL SEARCH </li></ul></ul></ul><ul><li>Scheduling Techniques can be Combined in an Integrated Manner </li></ul><ul><ul><ul><li>E.G., A DISPATCHING RULE CAN BE USED WITHIN A BRANCH AND BOUND TO OBTAIN UPPER BOUNDS. </li></ul></ul></ul><ul><ul><ul><li>DYNAMIC PROGRAMMING ROUTINE CAN BE USED FOR A SINGLE MACHINE SUBPROBLEM WITHIN A MACHINE DECOMPOSITION TECHNIQUE </li></ul></ul></ul>
  27. 27. Part IV. System Implementation Issues Commercial Packages
  28. 28. <ul><li>ERP-SYSTEMS </li></ul><ul><li>SAP, Baan, JD Edwards, People Soft </li></ul><ul><li>GENERAL OPTIMIZATION </li></ul><ul><li>Ilog, Dash </li></ul><ul><li>GENERAL SCHEDULING </li></ul><ul><li>I2, Cybertec, AutoSimulation, IDS Professor Scheer </li></ul><ul><li>SCHEDULING OIL AND PROCESS INDUSTRIES </li></ul><ul><li>Haverly Systems, Chesapeake, Finity </li></ul><ul><li>SCHEDULING CONSUMER PRODUCTS </li></ul><ul><li>Manugistics, Numetrix </li></ul><ul><li>SCHEDULING WORKFORCE IN CALL CENTERS </li></ul><ul><li>AIX, TCS, Siebel </li></ul>
  29. 29. RULES TO FOLLOW IN ORDER TO GENERATE ROBUST SCHEDULES <ul><li>Insert Idle times (Especially Where Perturbations are to be Expected) </li></ul><ul><li>Less Flexible Job First More Flexible Jobs Later </li></ul><ul><li>Do NOT Postpone Processing when Possible (NOTE: This Would Go Against JIT Principles) </li></ul>
  30. 30. LEARNING MECHANISMS <ul><li>Rote Learning (When Solution Space is Relatively Small) </li></ul><ul><li>Classifier Systems (Often Based on Genetic Algorithms) </li></ul><ul><li>Case Based Reasoning (Parameter Adjustment Methods) </li></ul><ul><li>Induction Methods and Neural Nets </li></ul>
  31. 31. NEURAL NET <ul><li>Application: </li></ul><ul><ul><ul><li>m Resources in Parallel </li></ul></ul></ul><ul><ul><ul><li>Different Speeds </li></ul></ul></ul><ul><ul><ul><li>Setups </li></ul></ul></ul>INPUT UNIT OUTPUT UNIT <ul><ul><ul><li>Jobs Arrive at Different Times </li></ul></ul></ul><ul><ul><ul><li>Jobs Have Due Dates </li></ul></ul></ul><ul><li>Machines have Attributes </li></ul><ul><ul><ul><li>Increase in Total Weighted Completion Time </li></ul></ul></ul><ul><ul><ul><li>Increase in Number of Late Jobs </li></ul></ul></ul><ul><ul><ul><li>Current Number of Jobs on Machine </li></ul></ul></ul>HIDDEN UNITS
  32. 32. OFF-LINE TRAINING BY AN EXPERT <ul><li>Expert Plus Learning </li></ul><ul><li>Algorithm (Back Propagation) </li></ul><ul><li>Determine the Connection Weights </li></ul>
  33. 33. MULTIPLE OBJECTIVES <ul><li>EXAMPLE: </li></ul><ul><ul><ul><li>m Resources in Parallel </li></ul></ul></ul><ul><ul><ul><li>n Jobs </li></ul></ul></ul><ul><ul><ul><li>Due Dates </li></ul></ul></ul><ul><ul><ul><li>Sequence Dependent Setups </li></ul></ul></ul><ul><li>OBJECTIVES: </li></ul><ul><ul><ul><li>Minimize Sum of Setup Times </li></ul></ul></ul><ul><ul><ul><li>Minimize Penalties Due to Late Delivery </li></ul></ul></ul>Weights of the Two Objectives Vary over Time and Depend on Status Quo.
  34. 34. GENERAL FRAMEWORK: <ul><li>Mixing of Priority Rules </li></ul><ul><li>Switching Over Between Rules </li></ul>Scaling Parameters and Switch-Over Times Depend on the Data Set Framework Above can be Combined with Local Search Heuristic.
  35. 35. DESIGN ISSUES WITH REGARD TO DECISION SUPPORT SYSTEMS FOR PLANNING AND SCHEDULING <ul><li>Robustness </li></ul><ul><li>Multiple Objectives </li></ul><ul><li>Learning mechanisms </li></ul>
  36. 36. DECISION SUPPORT SYSTEMS <ul><li>Forecasting </li></ul><ul><li>Facility Location </li></ul><ul><li>Supply Chain Management </li></ul><ul><li>Routing and Distribution </li></ul>
  37. 37. PLANNING AND SCHEDULING <ul><li>Characteristics: </li></ul><ul><ul><li>Engines Often Based on Combinatorial Algorithms </li></ul></ul><ul><ul><li>Systems Often have to Operate in Real Time </li></ul></ul>
  38. 38. PLANNING AND SCHEDULING FRAMEWORK <ul><li>Resources (Machines) </li></ul><ul><li>Tasks (Jobs) </li></ul><ul><li>Due Dates </li></ul><ul><li>Objectives </li></ul>GOAL: <ul><li>Determine a Schedule (solution) That Minimizes the Objective(s) </li></ul>