A puzzle‐based material handling system for order picking

Ashgzari, Mina Shekari; Gue, Kevin R.

doi:10.1111/itor.12886

Cited by 17 publications

(13 citation statements)

References 21 publications

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“…After optimization, the total time to complete the material picking process is 9:31, which represents a significant saving compared to the original time of 10:20. The optimal storage of material items in shelving positions according to categories is 17,19,18,14,6,27,3,32,8,1,28,22,7,33,29,5,21,26,15,13,16,11,25,23,34,10,9,20,2,24,12,31,30,4. In this order, the individual categories of material items should be placed in the racking positions.…”

Section: Resultsmentioning

confidence: 99%

“…As part of the paper, it was selected the way of accelerating the process of supplying and picking of consignments on the base of targeted placement of material items. This approach thus differs from the procedure that used authors Ashgzari and Gue [28]. They searched for the automatized transport system, but this was not based on the use of AGV systems, but conveyor systems.…”

Section: Discussionmentioning

confidence: 99%

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The Use of a Simulation Model for High-Runner Strategy Implementation in Warehouse Logistics

2020

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This paper examines the use of computer simulation methods to streamline the process of picking materials within warehouse logistics. The article describes the use of a genetic algorithm to optimize the storage of materials in shelving positions, in accordance with the method of High-Runner Strategy. The goal is to minimize the time needed for picking. The presented procedure enables the creation of a software tool in the form of an optimization model that can be used for the needs of the optimization of warehouse logistics processes within various types of production processes. There is a defined optimization problem in the form of a resistance function, which is of general validity. The optimization is represented using the example of 400 types of material items in 34 categories, stored in six rack rows. Using a simulation model, a comparison of a normal and an optimized state is realized, while a time saving of 48 min 36 s is achieved. The mentioned saving was achieved within one working day. However, the application of an approach based on the use of optimization using a genetic algorithm is not limited by the number of material items or the number of categories and shelves. The acquired knowledge demonstrates the application possibilities of the genetic algorithm method, even for the lowest levels of enterprise logistics, where the application of this approach is not yet a matter of course but, rather, a rarity.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The Use of a Simulation Model for High-Runner Strategy Implementation in Warehouse Logistics

2020

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“…The analytical models and solution methodology developed by the authors allow to compare these two settings and to quantify the magnitude of benefits rendered by the dynamic batching, which performs better but requires a more complex material handling system. Also motivated by the growth of e‐commerce, Shekari Ashgzari and Gue (2021) propose a new type of automated goods‐to‐person system. The need of online retailers for stocking large assortment of items in small spaces can be met with compact storage systems.…”

Section: Transportation and Logistics With Autonomous Technologiesmentioning

confidence: 99%

Editorial to the Special Issue on Transportation and Logistics with Autonomous Technologies

Crainic

Guajardo

Roy

et al. 2021

Int Trans Operational Res

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“…Item to be moved e authors gave the proof that the system is deadlock free and studied the performance of the system in several configurations. Shekari Ashgzari and Gue [18] proposed a puzzle-based goods-toperson system named "GridPick+". e system is based on a grid of conveyor modules and decentralized control similar to the GridStore in [17].…”

Section: Escortmentioning

confidence: 99%

A Two-Stage Path Planning Method for the Rearrangement Problem in Modular Puzzle-Based Storage System

Guo

Xiao

Rui

et al. 2021

Mathematical Problems in Engineering

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The modular puzzle-based storage system consists of multiple storage modules. The system has multiple input/output (IO) points which can simultaneously deal with multiple orders in one batch. When a batch of orders comes in advance, it is necessary to rearrange the items near each IO point to reduce the picking time of customers. To complete the rearrangement quickly, this paper proposes a two-stage path planning method considering the simultaneous movement of multiple items. This method includes two stages: planning single moves and merging single moves. In the first stage, the sequence of single moves of each module needs to be obtained so as to convert the system from an initial state to a target state. In the second stage, the single moves in the sequence are merged into block moves and parallel moves to reduce the steps of movement. The simulation results show that the single move planning method can be used to solve the rearrangement problem stably and effectively and that the single move merging method can greatly optimize the experimental results with the optimization rate more than 50% in different configurations.

show abstract

A puzzle‐based material handling system for order picking

Cited by 17 publications

References 21 publications

The Use of a Simulation Model for High-Runner Strategy Implementation in Warehouse Logistics

The Use of a Simulation Model for High-Runner Strategy Implementation in Warehouse Logistics

Editorial to the Special Issue on Transportation and Logistics with Autonomous Technologies

A Two-Stage Path Planning Method for the Rearrangement Problem in Modular Puzzle-Based Storage System

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