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Intelligent Agent Based Model for an Industrial Order Picking Problem*

Intelligent Agent Based Model for an Industrial Order Picking Problem*. Byung-In Kim, Robert J. Graves and Sunderesh S. Heragu Rensselaer Polytechnic Institute Art St. Onge St. Onge Company May 20, 2001 * Supported by NSF Grant #DMI 9900039. IIE Annual Conference 2001.

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Intelligent Agent Based Model for an Industrial Order Picking Problem*

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  1. Intelligent Agent Based Model for an Industrial Order Picking Problem* Byung-In Kim, Robert J. Graves and Sunderesh S. Heragu Rensselaer Polytechnic Institute Art St. Onge St. Onge Company May 20, 2001 * Supported by NSF Grant #DMI 9900039 IIE Annual Conference 2001

  2. Presentation Outline 1. Manufacturing Control Frameworks 2. Industrial Order Picking Problem 3. Intelligent Pick-zone Assignment 4. Intelligent Conveyor Speed Adjustment 5. Conclusions & Discussion 2 / 14

  3. Control components Manufacturing entities 1. Manufacturing Control Frameworks • Hierarchy: master/slave relationship • Structural rigidity • Difficulty of control system design • Lack of flexibility (Assume Deterministic) Hierarchical • Interaction of autonomous components • Lack of global information • Difficulty in predicting system performance • Sensitivity to market rules Heterarchical 3 / 14

  4. 1. Manufacturing Control Frameworks • Features of Hierarchical and Heterarchical • Hierarchy and Semi- Autonomous components • Globally optimized solution • Robustness against disturbances Hybrid 4 / 14

  5. 2. Industrial Order Picking Problem • Cosmetics Warehouse • Gantry Picking Complex (GPC): 16 pick zones • GPC characteristics: 65142 orders (116589 line items)/day • OAPS (Order Analysis and Planning System) • Order Processing • Create Next day’s order sequencing, pick plan, • and replenishment plan • FSS (Finite Scheduling System) • Make detailed scheduling plan for gantry robots • Execution of picking and replenishment 5 / 14

  6. Sub-zone C Sub-zone B 2 Pick Zone 1 3 Sub-zone D 7 3 4 11 15 4 6 5 8 7 8 12 Sub-zone A 16 10 9 11 12 Pick tote Gantry Robot Compartment 14 13 15 16 Conveyor Drop buffer (b) Picking Zone Layout 2 1 5 6 9 10 14 13 2. Industrial Order Picking Problem: GPC (a) GPC Layout 6 / 14

  7. 3. Intelligent Pick-zone Assignment Old Model New Model • Decision Maker: OAPS • Method: Hierarchical & Static • Decision Timing : One day • before picking • Benefit: Ease of Calculation • Problem: Separates Planning • from Execution • -> Lacks ability to handle • dynamic situation • Decision Maker: FSS • Method: Intelligent Agent Based • Hybrid & Dynamic • Decision Timing: The moment • when the line-item enters • the system • Benefit: Synchronized Planning • & Execution • -> Fault Tolerant • -> Last minute changes to • picking can be accommodated • Problem: More sophisticated • calculation 7 / 14

  8. Task-Board Bid-Board 3. Intelligent Pick-zone Assignment Negotiation Protocol Order Agent Pick-zone Agent Task Announcement Monitoring Bid-board Select Best Bid Delete Task, Bid Confirm Task Monitoring Task-board Make a Bid Bid Submission Confirm Task 8 / 14

  9. 3. Intelligent Pick-zone Assignment Simulation Results with various conveyor speeds • Errors of the new model are always less than • the original hierarchical model • Average utilization levels are almost the same • Standard deviation of utilization levels are almost the same 9 / 14

  10. 3. Intelligent Pick-zone Assignment • Flexibility and reconfigurability • - machine breakdown scenario: G1-G4 down for 10,000 ~ 20,000 sec • - the remaining 12 gantry robots are able to absorb the tasks of • the down gantries if they have the needed SKUs 10 / 14

  11. 4. Intelligent Conveyor Speed Adjustment Negotiation Protocol (4) I want to reset my conveyor speed. Is it OK to you ? AP-Plex Agent (2) Everybody is OK with new speed ? A-Plex Agent Gantry Complex Agent Manual Agent (1) Can conveyor speed up(down) ? (3) No I can’t (3) No problem Gantry Agent Gantry Agent Gantry Agent Gantry Agent 11 / 14

  12. 4. Intelligent Conveyor Speed Adjustment • More frequent oscillations in (a) than in (b) • Simple logic at the higher level agent to filter requests • - Threshold 0.05 sec => Hybrid * 0.75 sec : starting conveyor feed interval 5 min , 10 min : specified checking interval 0.05 sec : threshold for filtering logic of higher level agent (Gantry Complex agent) 12 / 14

  13. 4. Intelligent Conveyor Speed Adjustment Fault Tolerance - Machine breakdown scenario: G1-G4 down for 10,000 ~ 20,000 sec - With dynamic speed adjustment, number of errors can be reduced Using Naive Conservative Logic - Mean Utilization with Dynamic Speed < Mean utilization with Static Speed => conservative 13 / 14

  14. 5. Conclusions & Discussions Conclusions • Intelligent agent based hybrid model for actual • industrial problem • Resource assignment problem and dynamic conveyor • speed adjustment • Hybrid model outperforms pure hierarchical and • heterarchical models In Progress & Future Works • Hybrid Scheduling and Control System Architecture for • Robustness and Global Optimization • Guidelines for designing intelligent agent based production/ • warehousing planning, scheduling, and control systems • Chaos concerns in manufacturing 14 / 14

  15. Higher Level Global Optimizer Agent Middle Level Guide Agent Bulletin Board Machine/ MHD Agent Part Agent Hybrid Scheduling and Control System Architecture

  16. Work Load Balancing between Order Trains Order-Stream is made by OAPS Work Load between trains is unbalanced Lack of OAPS => Developed a Preprocessor Pair wise exchange between non zip-qualified orders

  17. Work Load Balancing between Order Trains Simulation Results with various conveyor speeds • Throughput improvement in the system by balancing workload and • using bidding, 12.5 %(0.10/0.80) easily seen

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