f1b1e8d3517ffdbdfc6b9ac58c08cb74 Alg_a2023v16n9p408.pdf 7cff9e8823617da5e34d07971a803b66a77367cf Alg_a2023v16n9p408.pdf 0219302e596b51e49f05f227f7382a8d95fc5f5af077640339837cd452362e84 Alg_a2023v16n9p408.pdf Title: A Hybrid Simulation and Reinforcement Learning Algorithm for Enhancing Efficiency in Warehouse Operations Subject: 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. Keywords: warehouse operations; hybrid algorithms; simulation; reinforcement learning; optimization Author: Jonas F. Leon, Yuda Li, Xabier A. Martin, Laura Calvet, Javier Panadero, and Angel A. Juan Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Mon Aug 28 09:56:46 2023 CEST ModDate: Mon Aug 28 10:01:05 2023 CEST Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 22 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 2622620 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- UMJDPA+URWPalladioL-Bold Type 1 Custom yes yes no 10 0 IURMZC+URWPalladioL-Roma Type 1 Custom yes yes no 15 0 OIJIBR+URWPalladioL-Ital Type 1 Custom yes yes no 19 0 WCYBKT+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 38 0 RXNEKG+MSAM10 Type 1 Builtin yes yes no 81 0 QQVCCZ+CMMI10 Type 1 Builtin yes yes no 92 0 GIGFZE+CMR10 Type 1 Builtin yes yes no 96 0 HPZLSD+CMSY10 Type 1 Builtin yes yes no 100 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2024-04-20 02:35:57 CEST RepresentationInformation: Alg_a2023v16n9p408.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2024-04-19 19:58:12 CEST Size: 2622620 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 371 FreeObjects: 5 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Related Work Destination: section.2 Children: Item: Title: Early Work on RL with Simulation for Warehouse Operations Destination: subsection.2.1 Item: Title: Applications of RL with Simulation in Warehouse Operations Destination: subsection.2.2 Item: Title: Advancements in RL with Simulation for Warehouse Operations Destination: subsection.2.3 Item: Title: Reinforcement Learning in FlexSim Destination: section.3 Item: Title: Case Study Destination: section.4 Children: Item: Title: Use Case Description Destination: subsection.4.1 Item: Title: Simulation Environment Destination: subsection.4.2 Item: Title: Reinforcement Learning Implementations Destination: subsection.4.3 Item: Title: Training Results Destination: subsection.4.4 Item: Title: Validation Results Destination: subsection.4.5 Item: Title: Performance Discussion Destination: subsection.4.6 Item: Title: Key Applications and Open Research Lines Destination: section.5 Item: Title: Conclusions Destination: section.6 Item: Title: References Destination: section.7 Info: Title: A Hybrid Simulation and Reinforcement Learning Algorithm for Enhancing Efficiency in Warehouse Operations Author: Jonas F. Leon, Yuda Li, Xabier A. Martin, Laura Calvet, Javier Panadero, and Angel A. Juan Subject: 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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