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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 吳政鴻(Cheng-Hung Wu) | |
| dc.contributor.author | Martin Starker | en |
| dc.contributor.author | 沈茂容 | zh_TW |
| dc.date.accessioned | 2021-06-17T06:16:20Z | - |
| dc.date.available | 2021-08-31 | |
| dc.date.copyright | 2018-09-07 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-08-31 | |
| dc.identifier.citation | Abbasi, M. (2011). 10 - Storage, Warehousing, and Inventory Management. Logistics Operations and Management. R. Z. Farahani, S. Rezapour and L. Kardar. London, Elsevier: 181-197.
Agrawal, R., T. Imieliński and A. Swami (1993). 'Mining Association Rules Between Sets of Items in Large Databases.' ACM SIGMOD Record 22(2): 207-216. Andrea, R. D. and P. Wurman (2008). Future challenges of coordinating hundreds of autonomous vehicles in distribution facilities. 2008 IEEE International Conference on Technologies for Practical Robot Applications. Bartholdi, J. J. and S. T. Hackman (2016). Warehouse & Distribution Science. Atlanta, The Supply Chain & Logistics Institute H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology. Boysen, N., D. Briskorn and S. Emde (2017). 'Parts-to-picker based order processing in a rack-moving mobile robots environment.' European Journal of Operational Research 262(2): 550-562. Chen, D., S. L. Sain and K. Guo (2012). 'Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining.' Journal of Database Marketing and Customer Strategy Management 19(3): 197-208. Chiang, D. M. H., C. P. Lin and M. C. Chen (2014). 'Data mining based storage assignment heuristics for travel distance reduction.' Expert Systems 31(1): 81-90. de Koster, R., T. Le-Duc and K. J. Roodbergen (2007). 'Design and control of warehouse order picking: A literature review.' European Journal of Operational Research 182(2): 481-501. Ekren, B. Y., Z. Sari and T. Lerher (2015). Warehouse Design under Class-Based Storage Policy of Shuttle-Based Storage and Retrieval System. Enright, J. and P. Wurman (2011). Optimization and Coordinated Autonomy in Mobile Fulfillment Systems. Kirks, T., J. Stenzel, A. Kamagaew and M. ten Hompel (2012). 'Zellulare Transportfahrzeuge für flexible und wandelbare Intralogistiksysteme.' Logistics Journal 2012(01). Lamballais, T., D. Roy and M. B. M. De Koster (2017). 'Estimating performance in a Robotic Mobile Fulfillment System.' European Journal of Operational Research 256(3): 976-990. Petersen, C. G. and G. Aase (2004). 'A comparison of picking, storage, and routing policies in manual order picking.' International Journal of Production Economics 92(1): 11-19. Rouwenhorst, B., B. Reuter, V. Stockrahm, G. J. van Houtum, R. J. Mantel and W. H. M. Zijm (2000). 'Warehouse design and control: Framework and literature review.' European Journal of Operational Research 122(3): 515-533. Tompkins, J. A., J. A. White, Y. A. Bozer and J. M. A. Tanchoco (2010). Facilities Planning, Wiley. Wulfraat, M. (2012). Is Kiva Systems a Good Fit for Your Distribution Center? An Unbiased Distribution Consultant Evaluation. Wurman, P. R., R. D'Andrea and M. Mountz (2008). 'Coordinating hundreds of cooperative, autonomous vehicles in warehouses.' AI Magazine 29(1): 9-19. Xiang, X., C. Liu and L. Miao (2018). 'Storage assignment and order batching problem in Kiva mobile fulfilment system.' Engineering Optimization: 1-22. Zhang, Y. (2016). 'Correlated Storage Assignment Strategy to reduce Travel Distance in Order Picking.' IFAC-PapersOnLine 49(2): 30-35. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/71952 | - |
| dc.description.abstract | This thesis develops the Summed Correlation Assignment Algorithm (SCAA) to solve the Storage Location Assignment Problem (SLAP) in rack-moving mobile robots warehouses (RMWs). The combination of goods stored on the inventory pods mainly influences the warehouse workload. As a shared storage policy, in which each product is stored on multiple inventory pods, is applied, every inventory pod only holds few items of its products. The proposed algorithm combines several heuristics, to manage the replenishment process of products on inventory pods. A computational study is conducted to simulate warehouse operations under changing warehouse parameters and control policies. An artificial data set with adjustable correlation is generated and applied to the computation, in addition to a real-world data set. The results show that SCAA performs well in a RMW environment with small order sizes, where orders typically contain only a single item of each product. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T06:16:20Z (GMT). No. of bitstreams: 1 ntu-107-R05546036-1.pdf: 2530415 bytes, checksum: f1092ff8ee9696fd3f0d937200ef8c4d (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | ABSTRACT i
CONTENTS iii LIST OF FIGURES v LIST OF TABLES vi ABBREVIATIONS vii Chapter 1 Introduction 1 1.1 Research Background 1 1.2 Motivation and Purpose 3 1.3 Methods 4 Chapter 2 Decision Problems and Literature Review 5 2.1 Decision Problems of RMWs 6 2.2 Real Time Operation vs. Optimization 9 Chapter 3 Problem Description and Model Construction 12 3.1 Notation 12 3.2 Introduction to the Influencing Factors 13 3.2.1 Effect of the Inventory Pod Content on the Picking Effort 13 3.2.2 Replenishment and Picking Trade-Off: The Block Size 16 3.2.3 Model Limitations 18 3.3 Replenishment Algorithm 19 3.3.1 Summed Correlation Analysis 19 3.3.2 Inventory Pod Capacity Constraint 22 3.3.3 Relevance of Items for Inventory Pods 23 3.3.4 Constraint Relaxation 27 3.3.5 Assignment Rule Priority 28 3.4 Pseudo Code 28 3.5 Construction of the Simulation 30 3.5.1 Picking Heuristic 30 3.5.2 Warehouse Control Policy 32 3.5.3 Simulation Workflow 35 3.6 Initial Storage Assignment Solution 36 Chapter 4 Simulation Study 37 4.1 Benchmarks 37 4.1.1 Strategic Optimization 37 4.1.2 Low Replenishment Storage Allocation 39 4.1.3 High Correlation Storage Allocation 39 4.1.4 Variable Block Size Storage Allocation 39 4.1.5 Strategies Overview 42 4.2 Computation Using Real Data 42 4.2.1 Small Problem Size 43 4.2.2 Big Problem Size 49 4.3 Generating Artificial Data Set for the Sensitivity Analysis 51 4.3.1 Order Size 52 4.3.2 Item Frequency 52 4.3.3 Emphasize Correlation between Item Pairs 53 4.3.4 Flowchart 57 4.3.5 Analysis of the Artificial Orders 58 4.4 Sensitivity Analysis 60 4.4.1 Increased Problem Size 60 4.4.2 Necessity of a Warm-Up Phase 63 4.4.3 Order Size Influence 64 4.4.4 Correlation Influence 68 4.4.5 Inventory Pod Capacity 69 4.4.6 Warehouse Size 72 4.4.7 Warehouse Fill Rate 74 4.4.8 Alternative Warehouse Control Policy 76 Chapter 5 Conclusion 81 REFERENCES 83 | |
| dc.language.iso | en | |
| dc.title | 考慮需求相依性之移動貨架倉儲系統儲位管理方法 | zh_TW |
| dc.title | Storage Location Assignment Strategy in a Rack-Moving Mobile Robots Warehouse Environment Considering Item Correlation | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 洪一薰(I-Hsuan Hong),陳文智(Wen-Chih Chen) | |
| dc.subject.keyword | Storage Location Assignment Problem,Mobile Robots Rack-Moving Warehouse,Item Correlation,Order Picking, | zh_TW |
| dc.relation.page | 84 | |
| dc.identifier.doi | 10.6342/NTU201804096 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2018-09-03 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 工業工程學研究所 | zh_TW |
| 顯示於系所單位： | 工業工程學研究所 | |
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