Research on Hybrid Real-Time Picking Routing Optimization Based on Multiple Picking Stations

Wang, Deyao; Jiang, Jun; Ma, Ran; Shen, Guicheng

doi:10.1155/2022/5510749

Cited by 2 publications

(3 citation statements)

References 10 publications

(16 reference statements)

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“…Real-time demonstrations showcased the success rate of the algorithm in navigating towards fire or light sources while maintaining obstacle avoidance. Moving to warehouse operations, Wang et al [44] proposed a hybrid picking mode for real-time order picking in warehouse centers. Multiple picking stations were utilized to handle a large number of orders arriving at inconsistent quantities.…”

Section: Advancements In Rl With Simulation For Warehouse Operationsmentioning

confidence: 99%

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A Hybrid Simulation and Reinforcement Learning Algorithm for Enhancing Efficiency in Warehouse Operations

Leon,

Li,

Martin

et al. 2023

Algorithms

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The use of simulation and reinforcement learning can be viewed as a flexible approach to aid managerial decision-making, particularly in the face of growing complexity in manufacturing and logistic systems. Efficient supply chains heavily rely on steamlined warehouse operations, and therefore, having a well-informed storage location assignment policy is crucial for their improvement. The traditional methods found in the literature for tackling the storage location assignment problem have certain drawbacks, including the omission of stochastic process variability or the neglect of interaction between various warehouse workers. In this context, we explore the possibilities of combining simulation with reinforcement learning to develop effective mechanisms that allow for the quick acquisition of information about a complex environment, the processing of that information, and then the decision-making about the best storage location assignment. In order to test these concepts, we will make use of the FlexSim commercial simulator.

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Section: Advancements In Rl With Simulation For Warehouse Operationsmentioning

confidence: 99%

“…Ren and Huang [35] Balachandran et al [36] Gazebo Arslan and Ekren [37] Lewis et al [38] NVIDIA Isaac Sim Ho et al [39] Zhou et al [40] Cestero et al [41] Choi et al [42] Elkunchwar et al [43] Wang et al [44] Y. Ekren and Arslan [45] Arena…”

Section: Advancements In Rl With Simulation For Warehouse Operationsmentioning

confidence: 99%

A Hybrid Simulation and Reinforcement Learning Algorithm for Enhancing Efficiency in Warehouse Operations

Leon,

Li,

Martin

et al. 2023

Algorithms

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“…Vehicle capacity, route design, freight distribution, and personnel distribution in the impacted areas are all important variable parameters for the routing instances to be able to be scheduled effectively. Inconsistent and subpar service at the point of demand can result from facility allocation's frequent dealings with limited facility resources, high demand, and high pressure Wang, Jiang, Ma, & Shen, 2022). The decision of how to distribute and transfer resources according to demand necessity is one of the most crucial steps in planning and automating the multi-objective routing process (Shi et al, 2020;Stodola, 2018).…”

Section: Challenges Of Implementing Better Cost Interoperability In F...mentioning

confidence: 99%

Using Routing Heuristics to Improve Cost Interoperability: Strategy, Modelling Annotations, and Dynamism

Morsidi

2023

IJGOIA

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Routing systems mechanisms have piqued researchers' interest in heuristic components for routing problems that could alter problem complexity. As a result, myriad routing strategies were proposed that minimize deployment costs while maximizing traversal coverage. Several constraints were considered, including deployment times, load capacities, and projected coverage. Research into routing systems has focused on heuristics to optimize complex routing problems. Multiple strategies have been proposed to optimize deployment costs and maximize route coverage, focusing on deployment times, load capacities, and coverage. This literature study examines data interpolation for cost optimization features coupled with relative scheduling systems, with the primary purpose of supporting heterogeneity subjugation towards cost interoperability based on varied goals and objective functions. A total of 250 papers were analyzed for relevance regarding routing scheduling from relevant academic-based user-accessed scientific journal databases such as Scopus, Web of Science, Hindawi, ACM, and Google Scholar to perform a concise analysis of the relative cost interoperability measures in routing strategies, including single objective purposes undertakings. The research evaluated the application, niche problem-solving methodologies, and viability for future refinement or integration with comparable solutions. This qualitative study aims to present an information synthesis based on the PRISMA (Systematic Literature Review) framework on various recognized developments and trends for routing heuristic research works that will serve as a benchmark for refining improvisation on current solution strategies. Ultimately, this study presents a comprehensive review of the applicable field, an analysis of existing problem-solving strategies, and a comprehensive overview of the possibilities for incorporating them into further research.

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Research on Hybrid Real-Time Picking Routing Optimization Based on Multiple Picking Stations

Cited by 2 publications

References 10 publications

A Hybrid Simulation and Reinforcement Learning Algorithm for Enhancing Efficiency in Warehouse Operations

A Hybrid Simulation and Reinforcement Learning Algorithm for Enhancing Efficiency in Warehouse Operations

Using Routing Heuristics to Improve Cost Interoperability: Strategy, Modelling Annotations, and Dynamism

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