Storage assignment problem with travel distance and blocking considerations for a picker-to-part order picking system

2012 ◽  
Vol 62 (2) ◽  
pp. 527-535 ◽  
Author(s):  
Jason Chao-Hsien Pan ◽  
Po-Hsun Shih ◽  
Ming-Hung Wu
Keyword(s):  
Assignment Problem ◽  
Order Picking ◽  
Travel Distance ◽  
Storage Assignment ◽  
Picking System

scholarly journals Defining a storage-assignment strategy for precedence-constrained order picking

2021 ◽  
Vol 31 (2) ◽  
Author(s):  
Maria A. M. Trindade ◽  
Paulo S. A. Sousa ◽  
Maria R. A. Moreira
Keyword(s):  
Assignment Problem ◽  
Real Life ◽  
Order Picking ◽  
Quadratic Assignment ◽  
Assignment Model ◽  
Storage Assignment ◽  
Storage Location ◽  
Assignment Strategy ◽  
Location Assignment ◽  
The Creation

This paper proposes a zero-one quadratic assignment model for dealing with the storage location assignment problem when there are weight constraints. Our analysis shows that operations can be improved using our model. When comparing the strategy currently used in a real-life company with the designed model, we found that the new placement of products allowed a reduction of up to 22% on the picking distance. This saving is higher than that achieved with the creation of density zones, a procedure commonly used to deal with weight constraints, according to the literature.

scholarly journals Storage Assignment Optimization in Robotic Mobile Fulfillment Systems

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ruiping Yuan ◽  
Juntao Li ◽  
Wei Wang ◽  
Jiangtao Dou ◽  
Luke Pan
Keyword(s):  
Turnover Rate ◽  
Simulation Experiment ◽  
Order Picking ◽  
Assignment Method ◽  
Total Correlation ◽  
Storage Assignment ◽  
New Type ◽  
The Difference ◽  
The Times ◽  
Picking System

Robotic mobile fulfillment system (RMFS) is a new type of parts-to-picker order picking system, where robots carry inventory pods to stationary pickers. Because of the difference in working mode, traditional storage assignment methods are not suitable for this new kind of picking system. This paper studies the storage assignment optimization of RMFS, which is divided into products assignment stage and pods assignment stage. In the products assignment stage, a mathematical model maximizing the total correlation of products in the same pods is established to reduce the times of pod visits, and a scattered storage policy is adopted to reduce system congestion. A heuristic algorithm is designed to solve the model. In the pods assignment stage, a model is established minimizing the total picking distance of the mobile robots considering the turnover rate and the correlation of pods as well as the workload balance among picking corridors. A two-stage hybrid algorithm combining greedy algorithm and improved simulated annealing is designed to solve the model. Finally, a simulation experiment is carried out based on the historical order data of an e-commerce company. Results show that the storage assignment method proposed in the paper significantly improves the efficiency of order picking.

scholarly journals Simulation Model for a Semi-automated Retail Order Picking System Under Uncertainty

2021 ◽  
pp. 759-767
Author(s):  
Sawssen Souiden ◽  
Audrey Cerqueus ◽  
Xavier Delorme ◽  
Jean-Lucien Rascle
Keyword(s):  
Simulation Model ◽  
Order Picking ◽  
Picking System

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.

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