Facility Design-Week12 Warehouse Operation

1. Facility Design-Week12Warehouse Operation Anastasia L. Maukar

2. Warehouse Functions • Provide temporary storage of goods • Put together customer orders • Serve as a customer service facility • Protect goods • Segregate hazardous or contaminated materials • Perform value-added services • Inventory

3. Elements of a Warehouse • Storage Media • Material Handling System • Building

4. Storage Media • Block Stacking • Stacking frames • Stool like frames • Portable (collapsible) frames • Cantilever Racks

5. Storage Media (Continued) • Selective Racks • Single-deep • Double-deep • Multiple-depth • Combination • Drive-in Racks • Drive-through Racks

6. Storage Media (Continued) • Mobile Racks • Flow Racks • Push-Back Rack

7. Storage Media (Continued) • Racks for AS/RS • Combination Racks • Modular drawers (high density storage) • Racks for storage and building support

8. Storage and Retrieval Systems • Person-to-item • Item-to-person • Manual S/RS • Semi-automated S/RS • Automated S/RS • Aisle-captive AS/RS • Aisle-to-aisle AS/RS

9. Storage and Retrieval Systems (cont) • Storage Carousels • Vertical • Horizontal • Miniload AS/RS • Robotic AS/RS • High-rise AS/RS (two motors)

10. Phoenix Pharmaceuticals • German company founded in 1994 • Receives supplies from 19 plants across Germany and distributes to drugstores • $400 million annual turnover

11. Phoenix Pharmaceuticals • 30% market share • Fill orders in < 30 minutes • 87,000 items • 61% pharmaceutical, 39% cosmetic

12. Phoenix Pharmaceuticals (cont.) • 150-10,000 picks per month • Three levels of automation • Manual picking via flow-racks • Semi-automated using dispensers • Full automation via robotic AS/RS

13. AVS/RS

14. RFID

15. Warehouse Problems • Design • Operational or Planning

16. Warehouse Design • Location • How many? • Where? • Capacity • Overall Layout

17. Warehouse Design

18. Warehouse Design • Layout and Location of Docks • Pickup by retail customers? • Combine or separate shipping and receiving? • Layout of road/rail network • Room available for maneuvering trucks? • Similar trucks or a variety of them?

19. Warehouse Design (cont) • Number of Docks • Shipping and receiving combined or separated? • Average and peak number of trucks or rail cars? • Average and peak number of items per order? • Seasonal highs and lows • Types of load handled? Sizes? Shapes? Cartons? Cases? Pallets? • Protection from weather elements

20. Model for Rack Design • x, y are # of columns, rows of rack spaces • a, b are aisle space multipliers in x, y directions

21. Model for Rack Design (Cont) • In the relaxed problem, xyz=n x=n/yz • The unconstrained objective is

22. Model for Rack Design (Cont) • Taking derivative with respect to y, setting equation to zero and solving, we get

23. Rack Design Example • Consider warehouse shown in figure 10.29 • Assume travel originates at lower left corner • Assume reasonable values for the aisle space multipliers a, b

24. Rack Design Example (Cont) • Example 1: Determine length and width of the warehouse so as to accommodate 2000 square storage spaces of equal area in: • 3 levels • 4 levels • 5 levels

25. Rack Design Example Solution • Reasonable values for a, b are 0.5, 0.2 • For the 3-level case,

26. Rack Design Example Solution (Cont) • Previous solution gives a total storage of 24x29x3=2088 • Due to rounding, we get 88 more spaces • If inadequate to cover the area required for lounge, customer entrance/exit and other areas, the aisle space multipliers a, b must be increased appropriately and the x, y values recalculated

27. Rack Design Example Solution (Cont) • For the 4 level and 5 level case, the building dimensions are 25x20 units and 18x23 units, respectively • Easy to calculate the average distance traveled - simply substitute a, b, x and y values in the objective function • For 3-level case, average one-way distance = 35.4 units

28. Warehouse Design Model

29. Model Assumptions • 1. The available total storage space is known. • 2. The expected time a product spends on the shelves is known. This is referred to as the dwell time throughout this paper. • 3. The cost of handling each product in each flow is known. • 4. The dwell time and cost have a linear relationship. • 5. The annual product demand rates are known. • 6. The storage policies and material handling equipment are known and these affect the unit handling and storage costs.

30. Model Notation

31. Model Notation

32. Model Notation

33. Model

34. Model

35. Spreadsheet Based AS/RS Design Tool

36. Spreadsheet Based AS/RS Design Tool

37. Block Stacking • Simple formula to determine a near-optimal lane depth assuming • goods are allocated to storage spaces using the random storage operating policy • instantaneous replenishment in pre-determined lot sizes • replenishment done only when inventory excluding safety stock has been fully depleted • lots are rotated on a FIFO basis

38. Block Stacking (Cont) • withdrawal of lots takes place at a constant rate • empty lot is available for use immediately • Let Q, w and z denote lot size in pallet loads, width of aisle (in pallet stacks) and stack height in pallet loads, respectively

39. Block Stacking (Cont) • Kind’s (1975) formula for near-optimal lane depth, d

40. Block Stacking (Cont) • E.g., if lot size is 60 pallets, pallets are stacked 3 pallets high and aisle width is 1.7 pallet stacks, then • Verify optimality by checking the utilization for all possible lane depths (a finite number)

41. Block Stacking (Cont) • Several issues omitted in Kind’s formula. Some examples • What if pallets withdrawn not at a constant rate but in batches of varying sizes? • What if lots are relocated to consolidate pallets containing similar items?

42. Storage Policies • Random • In practice, not purely random • Dedicated • Requires more storage space than random, but throughput rate is higher because no time is lost in searching for items • Cube-per-order index (COI) policy • Class-based storage policy

43. Storage Policies (Cont) • Shared storage policy • Class based and shared storage policies are between the two “extreme” policies - random and dedicated • Class based policy variations • if each item is a class, we have dedicated policy • if all items in one class, we have random policy

44. Design Model for Dedicated Policy • Warehouse has p I/O points • m items are stored in one of n storage spaces or locations • Each location requires the same storage space • Item i requires Si storage spaces

45. Design Model for Dedicated Policy (Cont) • Ideally, we would like • However, if LHS < RHS, add a dummy product (m+1) to take up remaining spaces

46. Design Model for Dedicated Policy (Cont) • So, assume that the above equality holds • But, if RHS < LHS, no feasible solution • Model Parameters • fik trips of item i through I/O point k • cost of moving a unit load of item i to/from I/O point k is cik • distance of storage space j from I/O point k is dkj

47. Design Model for Dedicated Policy (Cont) • Model Variable • binary decision variable xij specifying whether or not item i is assigned to storage space j

48. Design Model for Dedicated Policy (Cont)

49. Design Model for Dedicated Policy (Cont)

50. Design Model for Dedicated Policy (Cont)