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

4. 1. Manufacturing Control Frameworks Hybrid Features of Hierarchical and Heterarchical Hierarchy and Semi- Autonomous components Globally optimized solution Robustness against disturbances 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 days 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. 2. Industrial Order Picking Problem: GPC Gantry Robot Pick tote Compartment Conveyor Drop buffer (b) Picking Zone Layout (a) GPC Layout 15 11 7 3 14 10 6 2 Sub-zone CSub-zone B 16 12 8 4 13 9 5 1 Sub-zone A Sub-zone D 43 Pick Zone 12 87 56 1211 910 1615 1314 6 / 14

7. 3. Intelligent Pick-zone Assignment 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 Old ModelNew Model 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. 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 Task-BoardBid-Board 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 Gantry Complex Agent Gantry Agent Gantry Agent Gantry Agent Gantry Agent (1) Can conveyor speed up(down) ? (2) Everybody is OK with new speed ? (3) No problem (3) No I cant AP-Plex Agent A-Plex Agent Manual Agent (4) I want to reset my conveyor speed. Is it OK to you ? Negotiation Protocol 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 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 Conclusions 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 In Progress & Future Works 14 / 14

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

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