Trolley line picking: storage assignment and order sequencing to increase picking performance

OR Spectrum ◽  
2019 ◽  
Vol 41 (4) ◽  
pp. 1087-1121 ◽  
Author(s):  
David Füßler ◽  
Nils Boysen ◽  
Konrad Stephan
Keyword(s):  
Storage Assignment ◽  
Order Sequencing

Demand-predictive storage assignment mechanism for flower auction centers

2021 ◽  
pp. 1-17
Author(s):  
Xiang T.R. Kong ◽  
Miaohui Zhu ◽  
Kaida Qin ◽  
Pengyu Yan
Keyword(s):  
Storage Assignment

Storage Assignment with Rack-Moving Mobile Robots in KIVA Warehouses

2018 ◽  
Vol 52 (6) ◽  
pp. 1479-1495 ◽  
Author(s):  
Felix Weidinger ◽  
Nils Boysen ◽  
Dirk Briskorn
Keyword(s):  
Mobile Robots ◽  
Storage Assignment

scholarly journals Comparison of Lowest-Slot and Nearest-Stack Heuristics for Storage Assignment of Steel Bar Sets

2018 ◽  
Vol 9 (2) ◽  
pp. 37-45
Author(s):  
Jakob Marolt ◽  
Tone Lerher
Keyword(s):  
Information Infrastructure ◽  
Order Picking ◽  
Steel Plant ◽  
Steel Plates ◽  
Stored Products ◽  
Overhead Crane ◽  
Long Run ◽  
Steel Bar ◽  
Storage Assignment ◽  
Steel Coils

Abstract Our research objective is to lower intralogistics costs by minimizing the number of shuffling operations in a steel plant company commercial warehouse. The process of dispatching products consists of retrieving set of steel bar (SSB) from a floor stored stack or a special stacking frame by an overhead crane. To retrieve a targeted merchandise all SSB above targeted must be reshuffled. Proper assignment of storage locations is a key logistics problem for efficient order picking. We are comparing two heuristics, that do not require information of dispatching sequence of any stored products. We simulated the problem at hand with both methods. Our objective is to count the number of reshuffles using each heuristic on randomly generated examples and decide which is better in the long run. Our problem has similarities with storage assignment of steel plates or steel coils for minimization of reshuffling operations. The problem is also comparable to storage assignment of containers in a container yard. In our case we are dealing with a special stacking configuration of products, that demands different approach. We want to demonstrate which heuristic should be used in companies that lack necessary storage information infrastructure.

Relocation and Storage Assignment Strategies Evaluation in a Multiple-deep Tier-captive Automated Vehicles Storage and Retrieval System with Undetermined Retrieval Sequence

2021 ◽  
Author(s):  
Jakob Marolt ◽  
Nenad Kosanić ◽  
Tone Lerher
Keyword(s):  
Retrieval System ◽  
Discrete Event ◽  
Nearest Neighbour ◽  
Throughput Performance ◽  
Storage And Retrieval ◽  
Event Simulation ◽  
Retrieval Systems ◽  
Storage Assignment ◽  
And Storage

Abstract This paper studies multiple-deep automated vehicle storage and retrieval systems (AVS/RS) known for their high throughput performance and flexibility. Compared to a single-deep system, multiple-deep AVS/RS has a better space area utilisation. However, a relocation cycle occurs, reducing the throughput performance whenever another stock-keeping unit (SKU) blocks a retrieving SKU. The SKU retrieval sequence is undetermined, meaning that the arrangement is unknown, and all SKUs have an equal probability of retrieval. In addition to the shuttle carrier, a satellite vehicle is attached to the shuttle carrier and is used to access storage locations in multiple depths. A discrete event simulation of multiple-deep AVS/RS with a tier captive shuttle carrier was developed. We focused on the dual command cycle time assessment of nine different storage and relocation assignment strategies combinations in the simulation model. The results of a simulation study for (i) Random, (ii) Depth-first and (iii) Nearest neighbour storage and relocation assignment strategies combinations are examined and benchmarked for five different AVS/RS case study configurations with the same number of storage locations. The results display that the fivefold and sixfold deep AVS/RS outperform systems with fewer depths by utilising Depth-first storage and Nearest neighbour relocation assignment strategies.

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