Dynamic Order Picking Method for MultiUAV System in Intelligent Warehouse
Abstract
:1. Introduction
 The proposed method uses dynamic allocation based on interventionist order picking, and uses an efficient order picking method compared to previous logistics algorithms to achieve a smaller completion time. Through this, the warehouse operation’s performance is improved.
 The proposed HC strategy uses similarity information between orders allocated to the UAV and has a smaller average travel distance than previous algorithms. Through this, it contributes to increasing the operating time of each UAV compared to the charging time of the UAV.
 By applying the koptbased DTSP solving algorithm to the logistics environment, the proposed method can quickly calculate the task sequence for the order. Through this, the sequence is changed in real time even when the order is frequently changed in the picking list of the UAV, and the optimal path is guaranteed.
2. Related Works
2.1. Dynamic Order Picking
2.2. Method of Dynamic Order Picking
2.2.1. Dynamic Order Batching
2.2.2. Dynamic Pick Lists
2.3. Interventionist Order Picking Strategy
 1.
 Interventionist—accept all (IAA): This method waits in the warehouse’s depot until new order arrives. If N orders arrive, they are assigned to a picker to start picking. The picker in operation is assigned a new order until the capacity conditions allow. This method increases the processing time of orders that were already on the pick list. Therefore, both the task completion time and the travel distance increase.
 2.
 Interventionist—order completion time (IOCT): This method, like the IAA, waits in the warehouse’s depot until new order arrives. If N orders arrive, they are assigned to a picker to start picking. It is a method that adds a new order if the capacity condition is satisfied and the completion time increased by additional orders does not exceed a certain threshold. In this method, in order to obtain the completion time, it is necessary to find the optimal travel path in advance, and this computational cost increases the computation of the algorithm.
 3.
 Interventionist—rebatching (IRB): This method also waits for N orders. When a new order comes in, a new batch is created with either (1) orders that have been assigned to the picker but have not been picked yet or (2) orders that have not been assigned yet. Although it is possible in various ways, it requires a lot of computation time even for a single picker.
2.4. DTSP
2.5. MultiUAV Task Allocation
3. Problem Definition
 i: Index of an item
 P: Number of items present in the logistics warehouse
 ${p}_{i}$: Location of item i
 k: Index of orders
 ${O}_{k}$: An index set of items belonging to the kth order
 ${n}_{init}$: Number of initial customer orders
 ${n}_{last}$: Total orders, last customer orders
 ${s}_{k}$: The number of items that the kth order has
 S: Maximum number of items a customer’s order can have
 $\gamma $: Order growth rate
 ${n}_{\gamma}$: Number of orders arriving per cycle
 ${n}_{d}$: Number of depots
 ${c}_{min}$: Minimum number of orders for order batch creation
 ${n}_{r}$: The number of UAVs
 u: The index of a UAV
 ${l}_{u}$: Current location of UAV u
 ${v}_{UAV}$: The speed of the UAV
 ${c}_{u}$: Load capacity of orders currently held by the UAV
 ${c}_{max}$: Maximum order load on the UAV
 $P{L}_{u}$: Current picking list of UAV u
 $NP{L}_{u}$: List of items that have not yet passed through within the current pickup list of UAV u
 1.
 Each item is independent of other items and orders.
 2.
 The speed of the UAV is constant.
 3.
 The UAV has the ability to pick each item by itself.
 4.
 The aisle is wide enough to prevent collisions between UAVs.
 5.
 UAVs are equally assigned to each depot.
 6.
 Each UAV starts from the depot, picks up the item on the picking list, and then moves to the starting depot.
 7.
 It is assumed that the picking time of the UAV is constant. Therefore, no matter which algorithm is applied, the entire pickup time is the same, so the pickup time is ignored in the entire execution time of the algorithm.
 8.
 UAVs are assigned orders and order sequences from the WMS. Each UAV calculates an optimal path along this sequence and then autonomously flies between shelves.
4. Proposed Method
4.1. MultiUAV Task Allocation and Sequencing Strategy for Dynamic Order Picking
4.2. Halting and Correcting Strategy
4.2.1. Finding a Picking List Similar with New Orders
4.2.2. Constructing the Batch Similar to New Orders
4.2.3. Processing Waiting Orders with Expired Time
4.3. Order Sequencing
4.3.1. Normal Order Sequencing
4.3.2. Dynamic Order Sequencing
Algorithm 1 Proposed method. 
Input: path R, allocated tasks V Output: Best path ${R}_{best}$

5. Experiment
5.1. Experiment Design
5.2. Experiment Result
5.2.1. The Performance of Algorithms for Order Arrival Rates and Expired Time in Simulation
 1.
 Because the HC strategy creates a new batch through waiting rather than frequently applying kopt every time a new order arrives, the convergence time and the number of executions for the algorithm to find the optimal solution are reduced.
 2.
 Orders added to the picking list in the HC strategy are likely to have the same item composition as the existing picking list. Since this means a decrease in the number of nodes added in the TSP algorithm, the number of algorithm executions will be reduced.
5.2.2. The Performance of Algorithms for Number of UAVs
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Method type  Dynamic order batch  Dynamic order batch  Dynamic pick lists  Dynamic pick lists  Dynamic pick lists  Dynamic pick lists 
Picker type  Multiple  Single  Multiple  Multiple  Single  Multiple 
Item size of order  Variable  Variable  One  Variable  Variable  Variable 
Allow to change picking list  No  No  Yes  Yes  Yes  Yes 
Allow to change picker’s route  No  No  Yes  No  Yes  Yes 
${N}_{\gamma}$  2  4  6  8  10  

Completion Time [s]  FCFS + ACO  122.60  112.21  108.63  101.58  128.57 
FCFS + kopt  128.25  111.57  107.04  107.86  118.47  
HC + kopt  127.78  97.78  94.87  95.08  89.63  
Dif.  4.23%  $12.86$%  $12.67$%  $6.4$%  $30.29$%  
Median of Travel Distance (${n}_{r}$ = 10) [m]  FCFS + ACO  892.50  868.50  759.00  765.00  874.50 
FCFS + kopt  937.00  830.00  834.00  814.00  824.00  
HC + kopt  666.00  705.50  644.00  667.50  579.50  
Dif.  $25.38$%  $18.77$%  $15.15$%  $12.75$%  $33.73$%  
Collapsed Time [s]  FCFS + ACO  50.81  57.67  58.23  57.28  86.04 
FCFS + kopt  20.66  23.11  22.83  30.67  37.96  
HC + kopt  16.24  23.27  19.37  21.50  21.11  
Dif.  $68.04$%  $60.65$%  $66.74$%  $62.47$%  $75.46$% 
${N}_{r}$  1  2  3  4  5  6  7  8  9  10  

Completion Time [s]  ACO  1.60  1.57  1.50  1.46  1.57  1.28  1.40  1.22  1.27  1.10 
Proposed  0.30  0.31  0.32  0.32  0.35  0.38  0.28  0.36  0.38  0.40  
Travel Distance [m]  ACO  6512  3205  2298  1811  1480  1402  1222  998  887  868 
Proposed  6712  3254  2256  1905  1528  1311  1187  982  852  807 
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Han, C.; Jeon, H.; Oh, J.; Lee, H. Dynamic Order Picking Method for MultiUAV System in Intelligent Warehouse. Remote Sens. 2022, 14, 6106. https://doi.org/10.3390/rs14236106
Han C, Jeon H, Oh J, Lee H. Dynamic Order Picking Method for MultiUAV System in Intelligent Warehouse. Remote Sensing. 2022; 14(23):6106. https://doi.org/10.3390/rs14236106
Chicago/Turabian StyleHan, Changwan, Hyeongjun Jeon, Junghyun Oh, and Heungjae Lee. 2022. "Dynamic Order Picking Method for MultiUAV System in Intelligent Warehouse" Remote Sensing 14, no. 23: 6106. https://doi.org/10.3390/rs14236106