05eab47d146ccfb95c6325c2d3227e52 mat_a2023m4v11n7p1577.pdf 903f711c145d907dd033392ce37c085c941144f2 mat_a2023m4v11n7p1577.pdf 42a695ad57a0050f572e23405bb43b5f14f8f97f6a9e73c1e7480777ae347a7a mat_a2023m4v11n7p1577.pdf Title: A Discrete-Event Simheuristic for Solving a Realistic Storage Location Assignment Problem Subject: In the context of increasing complexity in manufacturing and logistic systems, the combination of optimization and simulation can be considered a versatile tool for supporting managerial decision-making. An informed storage location assignment policy is key for improving warehouse operations, which play a vital role in the efficiency of supply chains. Traditional approaches in the literature to solve the storage location assignment problem present some limitations, such as excluding the stochastic variability of processes or the interaction among different warehouse activities. This work addresses those limitations by proposing a discrete-event simheuristic framework that ensures robust solutions in the face of real-life warehouse conditions. The approach followed embraces the complexity of the problem by integrating the order sequence and picking route in the solution construction and uses commercial simulation software to reduce the impact of stochastic events on the quality of the solution. The implementation of this type of novel methodology within a warehouse management system can enhance warehouse efficiency without requiring an increase in automation level. The method developed is tested under a number of computational experiments that show its convenience and point toward future lines of research. Keywords: simheuristics; storage location assignment problem; managerial decision-making; simulation commercial software Author: Jonas F. Leon, Yuda Li, Mohammad Peyman, Laura Calvet, and Angel A. Juan Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Fri Mar 24 10:45:43 2023 CET ModDate: Fri Mar 24 10:48:59 2023 CET Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 24 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 2860912 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- ADTBFO+URWPalladioL-Bold Type 1 Custom yes yes no 10 0 HKTGVX+URWPalladioL-Roma Type 1 Custom yes yes no 15 0 TZJBFI+URWPalladioL-Ital Type 1 Custom yes yes no 19 0 DVTHKG+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 37 0 GQNMRP+CMEX10 Type 1 Builtin yes yes no 82 0 AWGMAG+PazoMath-Italic Type 1 Builtin yes yes no 86 0 XNDIHY+PazoMath Type 1 Builtin yes yes no 90 0 GIGFZE+CMR10 Type 1 Builtin yes yes no 94 0 UNXWPD+CMSY10 Type 1 Builtin yes yes no 98 0 GWJWRV+CMMI10 Type 1 Builtin yes yes no 142 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2024-04-20 02:36:20 CEST RepresentationInformation: mat_a2023m4v11n7p1577.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2024-04-19 16:24:29 CEST Size: 2860912 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 366 FreeObjects: 13 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Literature Review Destination: section.2 Children: Item: Title: The Storage Location Assignment Problem Destination: subsection.2.1 Item: Title: Solving Complex Optimization Problems: Simheuristics and Biased Randomization Destination: subsection.2.2 Item: Title: FlexSim as Discrete-Event Simulation Research Tool Destination: subsection.2.3 Item: Title: Problem Definition Destination: section.3 Children: Item: Title: A Generic Formulation for the Static SLAP with Orders Destination: subsection.3.1 Item: Title: Additional Assumptions for the Considered SLAP Study Destination: subsection.3.2 Children: Item: Title: Warehouse Destination: subsubsection.3.2.1 Item: Title: Products Destination: subsubsection.3.2.2 Item: Title: Orders Destination: subsubsection.3.2.3 Item: Title: Solution Destination: subsubsection.3.2.4 Item: Title: Modeling Approach Destination: section.4 Children: Item: Title: Simheuristic Framework Destination: subsection.4.1 Item: Title: Order Generation Destination: subsection.4.2 Item: Title: Warehouse Navigation Destination: subsection.4.3 Item: Title: Picking Operation and Solution Evaluation Destination: subsection.4.4 Item: Title: A Simheuristic for Solving the Storage Location Assignment Problem Destination: subsection.4.5 Item: Title: Computational Experiments Destination: section.5 Children: Item: Title: Experiment Setting Destination: subsection.5.1 Item: Title: FlexSim Modeling Destination: subsection.5.2 Item: Title: Results and Discussion Destination: subsection.5.3 Item: Title: Conclusions and Future Work Destination: section.6 Item: Title: References Destination: section.6 Info: Title: A Discrete-Event Simheuristic for Solving a Realistic Storage Location Assignment Problem Author: Jonas F. Leon, Yuda Li, Mohammad Peyman, Laura Calvet, and Angel A. Juan Subject: In the context of increasing complexity in manufacturing and logistic systems, the combination of optimization and simulation can be considered a versatile tool for supporting managerial decision-making. An informed storage location assignment policy is key for improving warehouse operations, which play a vital role in the efficiency of supply chains. Traditional approaches in the literature to solve the storage location assignment problem present some limitations, such as excluding the stochastic variability of processes or the interaction among different warehouse activities. This work addresses those limitations by proposing a discrete-event simheuristic framework that ensures robust solutions in the face of real-life warehouse conditions. The approach followed embraces the complexity of the problem by integrating the order sequence and picking route in the solution construction and uses commercial simulation software to reduce the impact of stochastic events on the quality of the solution. The implementation of this type of novel methodology within a warehouse management system can enhance warehouse efficiency without requiring an increase in automation level. The method developed is tested under a number of computational experiments that show its convenience and point toward future lines of research. 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