IL293899A - Task manager system and a method thereof - Google Patents
Task manager system and a method thereofInfo
- Publication number
- IL293899A IL293899A IL293899A IL29389922A IL293899A IL 293899 A IL293899 A IL 293899A IL 293899 A IL293899 A IL 293899A IL 29389922 A IL29389922 A IL 29389922A IL 293899 A IL293899 A IL 293899A
- Authority
- IL
- Israel
- Prior art keywords
- data
- optimization
- orders
- time
- indicative
- Prior art date
Links
- 238000000034 method Methods 0.000 title claims description 30
- 238000005457 optimization Methods 0.000 claims description 64
- 238000002360 preparation method Methods 0.000 claims description 29
- 238000012423 maintenance Methods 0.000 claims description 27
- 238000007726 management method Methods 0.000 claims description 11
- 238000012545 processing Methods 0.000 claims description 10
- 238000007596 consolidation process Methods 0.000 claims description 5
- 238000003860 storage Methods 0.000 claims description 5
- 238000012384 transportation and delivery Methods 0.000 claims description 5
- 238000004891 communication Methods 0.000 claims description 3
- 238000004088 simulation Methods 0.000 claims description 3
- 230000002860 competitive effect Effects 0.000 description 2
- 230000003203 everyday effect Effects 0.000 description 2
- 235000013305 food Nutrition 0.000 description 2
- 238000013439 planning Methods 0.000 description 2
- 241000286209 Phasianidae Species 0.000 description 1
- 238000003287 bathing Methods 0.000 description 1
- 238000010411 cooking Methods 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 230000000475 sunscreen effect Effects 0.000 description 1
- 239000000516 sunscreening agent Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
Description
TASK MANAGER SYSTEM AND A METHOD THEREOF TECHNOLOGICAL FIELD Embodiments of the presently disclosed relate generally to systems and methods for task managing.
BACKGROUND Warehouses and storage centers, for example ones that facilitate ecommerce orders, commonly use manual or semi manual processes to perform order fulfillment processes which are the activities preformed once an order is received. Some systems are based on operators standing in the picking area while boxes are moving and others create complex rack structures for space utilization. Some solutions utilize mobile robots to fetch cases and bins to a picking area. Moreover, order fulfilment of orders placed must take place within a relatively short period of time in order to be commercially competitive. Such order fulfilment is known as E-commerce and places demands on an order fulfilment system to meet such obligations. Each unique item has a specific inventory identification, known in the industry as a stock-keeping unit (SKU). Each item usually bears an optical code, such as a barcode or radio frequency identification (RFID) tag that identifies the SKU of the item. Picking stations in automated warehouses work in a way that the box arrives (by a robot) from storage to a particular station, the picker (human or robotic), picks from the specific box, one or several items, and places the items in a put wall so that packers can packages the one or more items to provide one or more packages that are outputted from the automated warehouse. The box awaits till the picker picks the one or several items and then immediately return the box to the storage.
GENERAL DESCRIPTION As described above, order fulfilment of orders must take place within a relatively short period of time in order to be commercially competitive. The warehouse usually has a number of predefined resources including a predefined number of picking stations being associated with a certain number of picker persons working usually a predefined number of shift working hours and a predefined number of robots. Generally, the predefined resources are attributed arbitrarily according to the averaged orders received during a certain period or according to the size of the warehouse affecting the number of picking stations. However, it should be noted that if the orders are not fulfilled on time, the picker persons may work overtime, maintenance tasks may be postponed, or even worse, the delivery time may be delayed. On the contrary, if the number of orders does not correspond to the predefined resources, some picking stations may be closed, and the time of the picker persons may be wasted. Moreover, it should be noted that when the operator of the automated warehouse attributes the number of resources arbitrary, the operator is not able to evaluate the consequences of the different attributions of resources and/or tasks on the system throughput. Moreover, the operator is not capable to calculate the time at which the tasks would be completed. Additionally, the operator is also not capable to decide when maintenance tasks should be applied, if any. Moreover, in the order fulfillment processes, special events (order triggering events) can accelerate the sales of specific items. For example, certain seasons may accelerate the sales of certain types of food and/or items for cooking such foods (turkeys before thanksgiving, grills and/or barbeque equipment at the beginning of spring), rain and/or snow can accelerate the sales of umbrellas and/or raincoats and/or boots, the beginning of summer may accelerate the sales of bathing suits and/or sunscreens and/or sun glasses, holidays may accelerate the sales of certain items such as Christmas ornament every Decembers, flowers on Valentine’s day or costumes before Halloween. Therefore, the need for resources (e.g. number of picker persons, allocation of tasks to picker persons when not be dedicated at the picking station, number of "open" picking stations and number of robots ...) is not uniform all over the year. More specifically, some periods of the year may require a certain number of picker persons working every day in a full-time position and some periods of the year (that might be very short) may require to double the number of picker persons and to extend the picking hours to be able to match the delivery time. Therefore, there is a need to forecast an optimal execution of the operational tasks of an automated warehouse, to optimize the resources of a warehouse management system to be able to manage or predict the trends in demand in general, and on specific items in particular, for example based on the past, e.g. an item is demanded every Friday, every 1st of the month, every Christmas, especially at peak times - such as black Friday, cyber-Monday or Christmas, i.e. the months of November and December to be able to meet the customer's needs.
The presently disclosed subject matter relates to a task manager system being capable of performing algorithms providing to an operator, in real-time or in a predicted manner, a recommendation regarding an optimization of a warehouse with respect to optimal resources (e.g. number of picker persons, allocation of tasks to picker persons when not be dedicated at the station, number of "open" picking stations and number of robots ..) to be deployed at each period of time in the actual day/week/month or as anticipation for future days/weeks/months as well . The optimization data may include at least one of the followings: number of picking stations that should be actuated, number of actuated robots of each type, optimal time of operation of the picking stations, managing time of the picker persons, and also optimization data regarding maintenance tasks such as managing the inventory, filing the stock, optimal location of selected items before or after the pick-up … Therefore, according to one broad aspect of the present disclosure, there is provided a task manager system for a warehouse comprising a processor being configured and operable for receiving a time data being indicative of a predetermined period of time at which the preparation of orders to be prepared using items from the warehouse should be completed, processing the time data, and generating a recommendation including an optimization data for the predetermined period of time comprising a plurality of optimization parameters being indicative of optimal resources that should be optimally attributed to complete at least one task including at least one maintenance task. In this connection, it should be noted that the predetermined period of time may define any desirable period of time including but not limited to any one of: part of a day, a single day, a plurality of days, an entire week or even an entire month. The optimization data provides the optimization parameters for the predetermined period of time. During this period of time, the optimization data can provide sub-period of times during which the task to be performed relates only to order preparation and sub-period of times for maintenance tasks. Therefore, in some sub-period of times, only orders can be prepared without processing with a maintenance task. For example, replenishment or consolidation or cycle count or replacing batteries should not be performed every day. The recommendation may provide to the operator the optimized time to perform maintenance tasks, so the throughput required of the order preparation is not impacted. As will be described further below, for example, the optimization data may recommend performing replenishment and/or consolidation tasks the week before black Friday because the load on picking is low and the inventory to fulfill the picking is needed during this peak. In some embodiments, the processor is configured and operable for receiving order data being indicative of the orders to be prepared using items from the warehouse, wherein generating a recommendation including an optimization data comprising a plurality of optimization parameters being indicative of optimal resources that should be optimally attributed to complete at least one task including at least one of completion of the preparation of the orders and at least one maintenance task. The order data may comprise at least one order line or recall data. Each order line may comprise at least one order related parameter including an item identifier, a due date data for supplying the item and a quantity. The due date data may comprise an expected delivery due date and/or an indication for a prioritized handling. In this connection, it should be noted that, typically, the purpose of an automatic warehouse is to fulfill orders. However, the ordering can be suspended at certain period of time (e.g. at the company yearly report time when cycle count should be performed on every item). The robots should then bring every box to the picking station(s) to perform the cycle count. The task manager of the present disclosure can then provide a forecast of time/picking stations/robots needed for the operation. Also, days before a special event, the number of orders can drop to zero, since customers wait for these events to order items. Thus, at this time, the warehouse workers can prepare the special events operation by performing at least one maintenance tasks such as consolidating, recalling and replenishing inventory as will be detailed further below. In some embodiments, the optimization of a warehouse may include a special planning in real-time of the warehouse (e.g. location of the items) according to the orders (amount and/or type of items) or to the predicted orders being related to special events. The optimization of a warehouse may also include planning an optimized organization of the warehouse before the special events. The optimization data may take into consideration the orders or the predicted orders. In some embodiments, the optimization of a warehouse may include optimizing the number of actuated robots according to the maintenance data of the robots and/or of the movement data of the robots in the warehouse and/or data relating to the size of the warehouse and the distance/time of the round trip.
In some embodiments, the processor is configured and operable for receiving historical data being indicative picking parameters including averaged picked-up time. the optimization of a warehouse may thus include providing recommendations based on historical data including averaged picked-up time. In some embodiments, the plurality of optimization parameters comprises at least one of predicted optimized number of robots of each type that should be actuated, predicted optimized number of picker persons, predicted optimized number of picking stations and allocation of at least one maintenance task to at least one picker person when not be dedicated at the station. The maintenance task may comprise replenishment and/or consolidation and/or cycle count and/or space reduction and/or warehouse maintenance and/or recall and/or replacement of batteries. The optimization data may comprise a plurality of options, each option being indicative of a different optimization of the plurality of the optimization parameters. In some embodiments, processing the time data (and optionally order data) comprises performing a plurality of simulations on the plurality of the optimization parameters. The order data may be received from a customer management system and the time data may be received from an operator, the customer management system and the operator being in data communication of the processor. In some embodiments, generating the optimization data comprises generating the optimization data before the preparation of the orders including the day at which the orders are prepared or at least one day before the preparation of the orders or in real-time during the preparation of the orders. In some embodiments, when the optimization data is generated before the preparation of the orders, the optimization data comprises data being indicative of at least one of the followings: optimal location of selected items before or after pick-up, optimal time of operation of picking stations before and/or during the preparation of the orders, managing time of picker persons. In some embodiments, the order data comprises predicting data being indicative of at least one of predicted orders enabling to provide an optimization data being related to special events. The predicting data may be based on historical data predicting the special events. According to one broad aspect of the present disclosure, there is provided a method for optimizing a warehouse management. The method comprising obtaining, by at least one computerized system, a time data being indicative of a predetermined period of time at which the preparation of the orders should be completed, processing the time data, and generating a recommendation including an optimization data for the predetermined period of time comprising a plurality of optimization parameters being indicative of optimal resources that should be optimally attributed to complete at least one task including at least one of completion of the preparation of the orders and at least one maintenance task. In some embodiments, the method further comprises receiving together with the time data, an order data being indicative of orders to be prepared using items from the warehouse and wherein generating a recommendation including an optimization data comprising a plurality of optimization parameters being indicative of optimal resources that should be optimally attributed to complete at least one task including at least one of completion of the preparation of the orders and at least one maintenance task. In some embodiments, the method further comprises receiving historical data being indicative picking parameters including averaged picked-up time. In some embodiments, the method further comprises receiving a predicting data being indicative of at least one of predicted orders enabling to provide an optimization data being related to special events. According to one broad aspect of the present disclosure, there is provided at least one non-transitory computer readable medium that stores instructions that once executed by a computerized system causes the computerized system to execute a process for optimizing a warehouse management, the non-transitory computer readable medium stores instructions for: obtaining, by at least one computerized system, a time data being indicative of a predetermined period of time at which the preparation of the orders should be completed, processing the time data, and generating a recommendation including an optimization data for the predetermined period of time comprising a plurality of optimization parameters being indicative of optimal resources that should be optimally attributed to complete at least one task including at least one maintenance task. According to one broad aspect of the present disclosure, there is provided an automated warehouse that comprises: a storage configured to store multiple items, wherein the multiple items are stored in item containers; a plurality of picking stations that comprise at least one picking station; one or more robots that are configured to convey item containers to the plurality of picking stations; and a task manager system as defined above.
BRIEF DESCRIPTION OF THE DRAWINGS In order to better understand the subject matter that is disclosed herein and to exemplify how it may be carried out in practice, embodiments will now be described, by way of non-limiting example only, with reference to the accompanying drawings, in which: Fig. 1 is a functional block diagram showing the task manager system according to some teachings of the presently disclosed subject matter; Fig. 2 is a functional flow chart showing the optimization method according to some teachings of the presently disclosed subject matter; and Fig. 3 is a schematical illustration of an automated warehouse according to some teachings of the presently disclosed subject matter.
Claims (32)
1. A task manager system for a warehouse comprising: a processor being configured and operable for receiving a time data being indicative of a predetermined period of time at which the preparation of orders to be prepared using items from the warehouse should be completed, processing the time data, and generating a recommendation including an optimization data for the predetermined period of time comprising a plurality of optimization parameters being indicative of optimal resources that should be optimally attributed to complete at least one task including at least one maintenance task.
2. The task manager system of claim 1, wherein said processor is configured and operable for receiving order data being indicative of the orders to be prepared using items from the warehouse, wherein generating a recommendation including an optimization data comprising a plurality of optimization parameters being indicative of optimal resources that should be optimally attributed to complete at least one task including at least one of completion of the preparation of the orders and at least one maintenance task.
3. The task manager system of claim 1 or claim 2, wherein the order data comprises at least one order line or recall data.
4. The task manager system of claim 3, wherein each order line comprises at least one order related parameter including an item identifier, a due date data for supplying the item and a quantity.
5. The task manager system of claim 4, wherein the due date data comprises an expected delivery due date and/or an indication for a prioritized handling.
6. The task manager system of any one of the preceding claims, wherein said processor is configured and operable for receiving historical data being indicative picking parameters including averaged picked-up time.
7. The task manager system of any one of the preceding claims, wherein said plurality of optimization parameters comprises at least one of predicted optimized number of robots of each type that should be actuated, predicted optimized number of picker persons, predicted optimized number of picking stations and allocation of at least one maintenance task to at least one picker person when not be dedicated at the station.
8. The task manager system of any one of the preceding claims, wherein the at least one maintenance task comprises at least one of replenishment, consolidation, cycle count, space reduction, warehouse maintenance, recall or replacement of batteries. - 21 -
9. The task manager system of any one of the preceding claims, wherein the optimization data comprises a plurality of options, each option being indicative of a different optimization of the plurality of the optimization parameters.
10. The task manager system of any one of the preceding claims, wherein said processing of the time data comprises performing a plurality of simulations on the plurality of the optimization parameters.
11. The task manager system of any one of claims 2 to 10, wherein said order data is received from a customer management system and said time data is received from an operator, the customer management system and the operator being in data communication of the processor.
12. The task manager system of any one of the preceding claims, wherein generating the optimization data comprises generating the optimization data before the preparation of the orders including the day at which the orders are prepared or at least one day before the preparation of the orders or in real-time during the preparation of the orders.
13. The task manager system of claim 12, wherein when the optimization data is generated before the preparation of the orders, said optimization data comprises data being indicative of at least one of the followings: optimal location of selected items before or after pick-up, optimal time of operation of picking stations before and/or during the preparation of the orders, managing time of picker persons.
14. The task manager system of any one of claims 2 to 13, wherein the order data comprises predicting data being indicative of at least one of predicted orders enabling to provide an optimization data being related to special events.
15. The task manager system of claim 14, wherein the predicting data is based on historical data predicting the special events.
16. A method for optimizing a warehouse management, the method comprising obtaining, by at least one computerized system, a time data being indicative of a predetermined period of time at which the preparation of the orders should be completed, processing the time data, and generating a recommendation including an optimization data for the predetermined period of time comprising a plurality of optimization parameters being indicative of optimal resources that should be optimally attributed to complete at least one task including at least one of completion of the preparation of the orders and at least one maintenance task. - 22 -
17. The method of claim 16, further comprising receiving together with the time data an order data being indicative of orders to be prepared using items from the warehouse and wherein generating a recommendation including an optimization data comprising a plurality of optimization parameters being indicative of optimal resources that should be optimally attributed to complete at least one task including at least one of completion of the preparation of the orders and at least one maintenance task.
18. The method of claim 17, wherein the order data comprises at least one order line or recall data.
19. The method of claim 17 or 18, wherein each order line comprises at least one order related parameter including an item identifier, a due date data for supplying the item and a quantity.
20. The method of claim 19, wherein the due date data comprises an expected delivery due date and/or an indication for a prioritized handling.
21. The method of any one of claims 16 to 20, further comprising receiving historical data being indicative picking parameters including averaged picked-up time.
22. The method of any one of claims 16 to 21, wherein said plurality of optimization parameters comprises at least one of predicted optimized number of robots of each type that should be actuated, predicted optimized number of picker persons, predicted optimized number of picking stations and allocation of at least one maintenance task to at least one picker person when not be dedicated at the station.
23. The method of any one of claims 16 to 22, wherein the at least one maintenance task comprises at least one of replenishment, consolidation, cycle count, space reduction, warehouse maintenance, recall or replacement of batteries.
24. The method of any one of claims 16 to 23, wherein the optimization data comprises a plurality of options, each option being indicative of a different optimization of the plurality of the optimization parameters.
25. The method of any one of claims 17 to 24 wherein the order data is received from a customer management system and said time data is received from an operator, the customer management system and the operator being in data communication of the processor.
26. The method of any one of claims 16 to 25 wherein said processing of the time data comprises performing a plurality of simulations on the plurality of the optimization parameters. - 23 -
27. The method of any one of claims 16 to 26, wherein generating the optimization data comprises generating the optimization data before the preparation of the orders including the day at which the orders are prepared or at least one day before the preparation of the orders or in real-time during the preparation of the orders.
28. The method of any one of claims 16 to 27, wherein when the optimization data is generated before the preparation of the orders, said optimization data comprises data being indicative of at least one of the followings: optimal location of selected items before or after pick-up, optimal time of operation of picking stations before and/or during the preparation of the orders, managing time of picker persons.
29. The method of any one of claims 16 to 28, further comprising receiving a predicting data being indicative of at least one of predicted orders enabling to provide an optimization data being related to special events.
30. The method of claim 29, wherein said predicting data is based on historical data predicting the special events.
31. At least one non-transitory computer readable medium that stores instructions that once executed by a computerized system causes the computerized system to execute a process for optimizing a warehouse management, the non-transitory computer readable medium stores instructions for: obtaining, by at least one computerized system, a time data being indicative of a predetermined period of time at which the preparation of the orders should be completed, processing the time data, and generating a recommendation including an optimization data for the predetermined period of time comprising a plurality of optimization parameters being indicative of optimal resources that should be optimally attributed to complete at least one task including at least one maintenance task.
32. An automated warehouse that comprises: a storage configured to store multiple items, wherein the multiple items are stored in item containers; a plurality of picking stations that comprise at least one picking station; one or more robots that are configured to convey item containers to the plurality of picking stations; and a task manager system of any one of claims 1 to 15. 30
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| IL293899A IL293899A (en) | 2022-06-13 | 2022-06-13 | Task manager system and a method thereof |
| PCT/IL2023/050608 WO2023242840A1 (en) | 2022-06-13 | 2023-06-13 | A system and method for optimization of a robotic automated warehouse and a task manager system thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| IL293899A IL293899A (en) | 2022-06-13 | 2022-06-13 | Task manager system and a method thereof |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| IL293899A true IL293899A (en) | 2024-01-01 |
Family
ID=89386426
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| IL293899A IL293899A (en) | 2022-06-13 | 2022-06-13 | Task manager system and a method thereof |
Country Status (1)
| Country | Link |
|---|---|
| IL (1) | IL293899A (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050278062A1 (en) * | 2004-06-15 | 2005-12-15 | Janert Philipp K | Time-based warehouse movement maps |
| EP3805131A1 (en) * | 2018-06-06 | 2021-04-14 | Beijing Geekplus Technology Co., Ltd. | Shelf management method and system, pickup area, and stock pickup system |
| CN113283826A (en) * | 2021-03-23 | 2021-08-20 | 北京京东振世信息技术有限公司 | Method and system for delivering articles out of warehouse |
| WO2022008735A1 (en) * | 2020-07-10 | 2022-01-13 | Ifollow | System and method for managing a plurality of mobile robots for the order-picking of products stored in a warehouse |
-
2022
- 2022-06-13 IL IL293899A patent/IL293899A/en unknown
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050278062A1 (en) * | 2004-06-15 | 2005-12-15 | Janert Philipp K | Time-based warehouse movement maps |
| EP3805131A1 (en) * | 2018-06-06 | 2021-04-14 | Beijing Geekplus Technology Co., Ltd. | Shelf management method and system, pickup area, and stock pickup system |
| WO2022008735A1 (en) * | 2020-07-10 | 2022-01-13 | Ifollow | System and method for managing a plurality of mobile robots for the order-picking of products stored in a warehouse |
| CN113283826A (en) * | 2021-03-23 | 2021-08-20 | 北京京东振世信息技术有限公司 | Method and system for delivering articles out of warehouse |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US7552066B1 (en) | Method and system for retail store supply chain sales forecasting and replenishment shipment determination | |
| CN114022078A (en) | Automatic replenishment method and system for warehouse management | |
| US20090089150A1 (en) | System and method for operation management of logistics center | |
| Gagliardi et al. | Space allocation and stock replenishment synchronization in a distribution center | |
| KR20150089793A (en) | method of processing pick and pass framework in warehouse management system | |
| Poon et al. | A real-time production operations decision support system for solving stochastic production material demand problems | |
| Caputo et al. | A model for planning and economic comparison of manual and automated kitting systems | |
| CN111415069B (en) | Discrete manufacturing process intelligent real-time scheduling method | |
| Miller et al. | A logistics deployment decision support system at Pfizer | |
| De Koster | Warehouse assessment in a single tour | |
| JP2002541564A (en) | System and method for scheduling manufacturing resources | |
| IL293899A (en) | Task manager system and a method thereof | |
| CN111144802B (en) | Intelligent warehousing and delivery method integrating AGV and mechanical arm | |
| Chauhan et al. | Application of FMOMILP for aggregate production planning: A case of multi-product and multi-period production model | |
| CN114971496A (en) | Method for sorting goods selected by e-commerce purchase orders in industrial enterprises | |
| CN116171456A (en) | Logistics management device, control method for logistics management device, and control program | |
| Turner | Evaluation of automated storage and retrieval in a distribution center | |
| WO2016020982A1 (en) | Information processing system and information processing method | |
| US20190287053A1 (en) | System for planning where to place merchandise items and method for planning where to place merchandise items | |
| CN110705945A (en) | Article transportation method and device, storage medium and electronic device | |
| Ala-Risku et al. | A solution for the material delivery problems in construction projects | |
| WO2023242840A1 (en) | A system and method for optimization of a robotic automated warehouse and a task manager system thereof | |
| WO2022270162A1 (en) | Information processing system, warehouse management method, and warehouse control device | |
| Dachyar et al. | Design On Improvement of Distribution Process in Logistic Service Provider Companies Using Business Process Reengineering Approach | |
| IL295460A (en) | A system and method for optimization of a robotic automated warehouse |