1. Order Picking Policies: Pick Sequencing, Batching and Zoning

2. The Pick Sequencing Problem Given a picking list, sequence the visits to the picking locations so that the overall traveling effort (time) is minimized. x x x x x x x x x x Docking station

3. Problem Abstraction: The Traveling Salesman Problem (TSP) Given a complete TSP graph: 1 2 3 45 c_ij find a tour that visits all cities, with minimal total (traveling) cost; e.g.: 1 2 3 45

4. Analytical Problem Formulation Parameters: –N : graph size (number of graph nodes) –c_ij : cost associated with arc (i,j) Decision Variables: –x_ij : binary variable indicating whether arc (i,j) is in the optimal tour –u_i : auxiliary (real) variable for the formulation of the “no subtour” constraints min  _i  _j c_ij x_ij s.t.  _j x_ij = 1  i  _i x_ij = 1  j (No subtours: u_i - u_j + N x_ij  N-1  i,j  {2,…,N} and i  j ) x_ij  {0, 1}  i,j

5. Some remarks on the TSP problem and its application in pick sequencing The TSP problem is an NP-complete problem: It can be solved optimally for small instances, but in general, it will be solved through heuristics. There is a vast literature on TSP and the development of heuristic algorithms for it (e.g., Lawler, Lenstra, Rinnooy Kan and Shmoys, “The Traveling Salesman Problem: A guided tour of combinatorial optimization”, John Wiley and Sons, 1985). When the “no subtour” constraint is removed, the remaining formulation defines a Linear Assignment Problem (LAP) (which is an easy one; e.g., the “Hungarian Algorithm”) => Solving the corresponding LAP can provide lower bounds for assessing the sub- optimality of the solutions provided by the applied heuristics. In the considered application context, the distances c_ij should be computed based on the appropriate distance metric; i.e., rectilinear, Tchebychev, “shortest path”

6. The closest insertion algorithm: A TSP heuristic (symmetric version) Initialization: S_p = ; S_a = {2,…,N}; c(j) = 1,  j  {2,…,N}; n=1; While n < N do n = n+1; Selection step: j* = argmin_{j  S_a} { c_{j,c(j)} } ; S_a = S_a \ {j*}; Insertion step: i* = argmin_{i =1}^|S_p| { c_{[i],j*} + c_{j*,[i mod |S_p|+1]} - c_{[i],[i mod |S_p|+1]} } ; S_p = ;  j  S_a, if c_{j,j*} < c_{j,c(j)} then c(j) = j*; Remarks: 1. [i] denotes the node at i-th position of the constructed sub-tour. 2. If the distances are symmetric and satisfy the triangular inequality, the cost of the solution provided by this heuristic is no worse than twice the optimal cost.

7. A special case admitting polynomial solution (Ratliff and Rosenthal, Operations Research, 31(3): 507-521, 1983) Docking station Picking Aisles Crossover Aisles Items to be picked x x x x x x x x x x x x

8. A graph-based representation of the underlying topology 0

9. A picking tour

10. Lj-, Aj and Lj+ sub-graphs, j=1,2,…,n x x x x x x x x x x x x b1b2b3b4b5b6 a1a2a3a4a5a6 v0 v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v11 v12 22222 2 7 3 3 22222 3 5 3 44 6 5 3 6 3 3 15 8 7 Lj+ = Lj-  Aj

11. Lj(- or +) PTS (partial tour sub-graph) L3- PTS : (E, E, 2C) L3+PTS: (U, U, 1C)

12. A key observation The only possible characterizations for an Lj (- or +) PTS are the following: (U, U, 1C) (0, E, 1C) (E, 0, 1C) (E, E, 1C) (E, E, 2C) (0, 0, 0C) (0, 0, 1C) where the triplet (X, Y, Z) should be interpreted as follows: X (Y): degree parity for node a_j (b_j) - 0, Even, Uneven (odd) Z: number of connected components in Lj PTS, excluding the vertices with zero degree

13. Going from Lj- to Lj+… a_j b_j (I-i)(I-ii)(I-iii)(I-iv)(I-v)(I-vi)

14. Going from Lj- to Lj+…(cont.) a: This is not a feasible configuration if there is any item to be picked in aisle j b: This class can occur only if there are no items to be picked to the left of aisle j c: This class is feasible only if there are no items to be picked to the right of aisle j d: Could never be optimal TABLE I

15. Going from Lj+ to L(j+1)-… a_j+1 a_j b_j b_j+1 a_j+1 a_j b_j b_j+1 a_j+1 a_j b_j b_j+1 a_j+1 a_j b_j b_j+1 a_j+1 a_j b_j b_j+1 (II-i)(II-ii) (II-iii) (II-iv) (II-v)

16. Going from Lj+ to L(j+1)-…(cont.) a: The degrees of a_j and b_j are odd. b: No completion can connect the graph. c: Would never be optimal. TABLE II

17. A polynomial-complexity algorithm for computing a minimum-length tour Initialization: L1- PTS = null graph for every class type For <L1+, L2-, L2+,…,Ln-, Ln+) –compute a minimum-length PTS for each of the seven classes, using the minimum-length PTS’s constructed in the previous stage, and the information provided in Tables I and II. –Remark: For case (I-iv), a minimum-length PTS is obtained by putting the gap between the two adjacent v_i’s in aisle j that are farthest apart. A minimum-length tour is defined by a minimum- length Ln+ PTS.

18. Example (c.f., slide 8)

19. Example: The optimal tour x x x x x x x x x x x x b1b2b3b4b5b6 a1a2a3a4a5a6 v0 v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v11 v12 22222 2 7 3 3 22222 3 5 3 44 6 5 3 6 3 3 15 8 7 0

20. k-STRIP: A computationally simple heuristic for rectangular areas x x x x x x x x x x I/O point x x When A is the unit square, an optimized k =   (n/2) , and for this value, the worst- case tour length generated by the heuristic is between 1.075  n and 1.414  n, for large n. The computational complexity is O(n logn). Supowit, Reingold and Plaisted, “The traveling salesman problem and minimum matching in the unit square”, SIAM J. Computing, 12(1): 144-156, 1983.

21. The bin-numbering heuristic ( Bartholdi and Platzman, Material Flow, 4: 247-254, 1988) Basic idea: Number bins / storage locations in a way that filling the orders by visiting the associated bins in increasing numbers will lead to efficient routings. Advantages: –Once the numbering is established, developing the order routes becomes extremely simple. –Easy to adjust routes dynamically upon the arrival of new orders. Basic underlying problem: How do you establish good bin-numbering schemes?

22. Example of a numbering scheme (Is it a good one?) Order: {1, 10, 13, 28, 30, 44, 50, 62} 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 1718 19 202122 23 24 2526 27 28 29 30 31 32 3334 35 3637 38 39 40 41 42 43 64 44 45 46 47 48 495051 52 53 5455 56 57 58 59 60 61 62 63 1 6 7 4 2 2 4 7 11 Resulting route length: 44 (using rectilinear distances of the cell centroids)

23. An alternative numbering scheme 12 34 56 78 910 1112 13 15161718 1920 2122 2324 2526 2728 2930 31323334 3536 3738 3940 4142 43 64 44 4546 47484950 5152 5354 5556 5758 5960 6162 63 14 Order: {1,4,12,14,21,48,58,64} 1 1 4 22 4 7 4 7 Resulting route length: 32

24. Key concept: Space-filling curves (see also http://www.isye.gatech.edu/faculty/John_Bartholdi/mow/mow.html) Closed curves that sweep the entire region while preserving nearness. Technically, they define a continuous mapping of the unit interval on the unit square. Typical example: Sierpinski’s space-filling curve:

25. The Sierpinski space-filling curve

26. Applying the Sierpinski space-filling curve in the previous bin-numbering example 1 23 45 67 8 910 1112 13 1516 17 1819 2021 22 2324 25 26 27 2829 3031 3233 34 3536 37 3839 404142 43 64 444546 4748 4950 5152 53 545556 5758 596061 62 63 14 Order: {1,2,13,17,18,32,46,52} Resulting route length: 34 1 1 6 2 2 4 7 4 7

27. Some properties of the bin-numbering schemes based on the Sierpinski space-filling curve If n locations are to be visited throughout a warehouse of area A, then the length of the retrieval route is at most  (2nA). If every location is equally likely to be visited, then on average, the retrieval route produced by the corresponding bin-numbering heuristic will be 25% longer than the shortest possible route length. The above results have been derived using the Euclidean metric for measuring the traveling distances, but they are robust with respect to other metrics that preserve “closeness” according to the Euclidean metric.

28. Characterizing the best bin-numbering scheme... …is computationally very hard. Some good schemes can be obtained through interchange techniques (e.g., 2 or 3-opt), where the efficiency of each of the considered schemes is evaluated through simulation. The optimal bin-numbering scheme depends on: –the underlying geometry of the picking facility –the frequency with which the various storage locations are visited (and therefore, the applying storage policy) In general, the logic underlying the utilization of the space- filling curves is more useful / pertinent for storage areas with small visitation frequencies for their locations. For areas with high visitation frequencies, numbering schemes suggesting an exhaustive sweeping of the region tend to perform better (c.f., Bartholdi & Platzman, pg. 252).

29. Bin-numbering in structures with complicated geometry When the considered area has a structure too complex to measure traveling effort by Euclidean or a relative metric, the logic underlying the application of space-filling curves to bin-numbering can be applied in a hierarchical fashion: separate the entire area under consideration to smaller areas of simpler geometry; design a numbering sequence for each of these areas using the space-filling curve logic; develop a visiting sequence for the areas developed in step 1, by passing a space-filling curve among their I/O points.

30. Order Batching (based on De Koster et. al., “Efficient orderbatching methods in warehouses”, Inlt. Jrnl of Prod. Res., Vol. 37, No. 7, pgs 1479- 1504, 1999)

31. Problem Description Given a set of orders, cluster them into batches - i.e., subsets of orders that are to be picked simultaneously by one picker at a single trip - s.t. –the total traveling distance / time is minimized –while each batch does not exceed some measure of the picker capacity (e.g., number of items / volume of the resulting batch, number of distinct orders in a batch) Theoretically, the problem can be solved by: –enumerating all feasible partitions of the given order set into batches; –evaluating the total traveling distance / time for each partition; –picking the partition with the smallest traveling distance / time. However, combinatorial explosion of partitions => heuristics

32. Order-Batching Heuristics NaiveIntelligent FCFSSeed Algorithms Savings Algorithms (Batches are built sequentially, one at a time) (All batches are built simultaneously, by merging partially developed batches) (Orders are clustered based on the sequence of their appearance)

33. The generic structure for seed algorithms While there are unprocessed orders, –Pick a new seed order according to some seed selection rule; –while there are unprocessed orders and the batch has not reached the imposed capacity limit pick a new order to be added to the batch according to an order addition rule; add the selected order to the batch, provided that the imposed capacity limit is not violated; (update the batch seed to the union of the previous batch seed and the new order)

34. Typical seed selection rules Random selection the order with the farthest item (w.r.t. the shipping station) the order with the largest number of aisles to be visited the order with the largest aisle range (absolute difference between the most left aisle number and the most right aisle number to be visited) the order with the largest number of items the order with the longest travel time Remark: If the batch seed is updated after every order addition, the algorithm is characterized as dynamic or cumulative mode; ow., it is said to be static or single mode.

35. Typical order addition rules Time saving: choose the order that, together with the batch seed, ensures the largest time saving compared with the individual picking of the two orders. Choose the order that minimizes the number of additional aisles, compared to the seed order, that have to be visited by the resulting batch route. Choose the order for which the absolute difference between the order’s center of gravity (COG) and the COG of the batch seed is the smallest; COG is the weighted average aisle number of the order, with the aisle weights defined by the number of items in the aisle. Choose the order with the property that the sum of distances* between every item of the seed and the closest item in the order is minimized. * distances must be measured by an appropriately selected metric

36. The (standard) savings algorithm Initialization: B: = order set (each order defines its own batch) Repeat –For each pair (i,j) in the current batch set B compute the time savings s_ij = t_i + t_j - t_ij, where t_i/j is the time required for picking batch i/j and t_ij is the time required for picking the batch resulting from the merging of batches i and j. –Rank batch pairs (i,j) in decreasing s_ij. –Pick the first batch pair (i,j) in the ranked list, for which the merging of its constituent batches does not violate the imposed capacity limit, and merge batches i and j: B := (B-{i,j}) U {i+j} until no further batch merging is possible. Remark: The algorithm result depends on the adopted pick sequencing rule.

37. Some findings regarding the (relative) performance of the presented batch algorithms (De Koster et. al.) Intelligent batching leads to significant improvements compared to single-order picking and naïve batching schemes. In seed algorithms, dynamic seed definition leads to better performance than static seed definition. The best seed selection rules are focusing on orders dispersed over a large number of aisles and involving long travel times. The best order addition rules (c.f. corresponding slide) tend also to be the most robust (i.e., they yield the best results in all warehouse configurations considered in the simulation). Savings algorithms have good performance, in general, but they tend to be computationally more expensive than seed algorithms. The performance of the applied batching algorithm has a significant dependence on the adopted pick sequencing rule. The largest the number of orders per batch (the batch capacity limit), the smaller the savings from intelligent batching (and therefore, simpler batching schemes become more eligible candidates)

38. Warehouse Zoning

39. The two main concepts of zoning in contemporary warehousing Warehouse zoning: The physical and/or logical segmentation of the warehouse / picking area, through –the employment of different storage modes and practices due to the product differentiation w.r.t. dimensions, physical characteristics storage and material handling requirements throughput, etc. –the parallelization of the order-picking activity.

40. Zone-based order picking Progressive Zoning / Order assembly Parallel/Simultaneous Zoning (typically organized in pick-waves with downstream sortation) OrderTo packing and shipping Z1Z2Z3Z4Z5 Order (Batch) To sorting and consolidation Z1Z2Z3Z4Z5

41. Defining effective zones for order-picking Main objective: Try to achieve maximum utilization of the picking resources, by distributing “equally” the total (picking) workload among the defined zones. However, the warehouse picking environment usually is a very dynamic environment; workload profiles are constantly changing. Existing zoning systems seek to balance the average workloads across zones, based on some hypothetical order work-content and worker behavioral models. Furthermore, constant zone redefinition requires a lot of effort from, both, the warehouse management (who must keep track of all the workload changes and re-establish the zones) and the warehouse pickers (who must adjust to the new policies). Very limited scientific literature.

42. Bucket Brigades ( c.f. Bartholdi & Hackman, Chpt. 10) A dynamic self-balancing scheme for progressive zoning. The three main requirements of bucket brigades: –Carry work forward, from station to station, until someone takes over your work. –If a worker catches up to his successor, he remains idle until the station is available (i.e., no overpassing is allowed) –Workers are sequenced from slowest to fastest.

43. The self-balancing property of bucket brigades Theorem: The line operation under the three main requirements of bucket brigades, converges to a balanced partition of the effort wherein the fraction of work performed by the i-th worker is equal to v_i /  _{j=1}^n v_j and the line production rate is equal to  _{j=1}^n v_j items per unit time.