By applying RFID technology and cloud computing into inventory management, the proposed CIM system made significant improvement in the field of inventory management. The inventory levels and labor costs were reduced significantly and the inventory management information was integrated flawlessly. Integrating information technology enhanced the effectiveness and efficiency of supply chains. The proposed CIM system with RFID technology played an important role in the process to achieve rapid, real-time, standardized inventory system for the logistics management. The proposed CIM system specifically improved supply chain efficiency. Experiment result showed 83% efficiency improvement from the novel system.
28
Final Conclusions and Recommendations
In this project, we present two researches on flower replenishment logistics and design of cloud inventory management system based on RFIDs technology.
The aim of flower replenishment logistics is to minimize transportation cost of supplying flora product from suppliers to the Taipei Flora Exposition. We proposed KG algorithm which combined K-means algorithm and genetic algorithm to solve the flower replenishment problem (FRP). Major contribution of this part is providing feasible and near-optimal solutions for the Expo’s daily operations.
In the inventory management, by using RFID technology and cloud computing, the proposed CIM system integrated the flora inventory information from the Taipei Flora Exposition clearly and correctly. The proposed CIM system with RFID technology played an important role in the process to achieve rapid, real-time, standardized inventory system for the logistics management.
According to our experiments, FRP and CIM showed significant time reduction and enhanced the efficiency of logistics and supply chain. Comparing with traditional methods, the KG algorithm was able to find the consistent and almost 50% of initial solutions. The CIM system can reduce 82.9792% walking route and time requirement.
29
REFERENCE
For Chapter 1: Flower Replenishment Logistics for Flora Exposition
[1] Alvarenga, G. B., & Mateus, G. R. (2004). A Two-Phase Genetic and Set Partitioning Approach for the Vehicle Routing Problem with Time Windows.
Paper presented at the annual meeting of the 4th International Conference on Hybrid Intelligent Systems, Kitakyushu, Japan.
[2] Bräysy, O. (Ed.s). (2001). Genetic Algorithms for the Vehicle Routing Problem with Time Windows. [Special issue]. Bioinformatics and Genetic Algorithms, 33-38.
[3] Bräysy, O., & Gendreau, M. (2001). Genetic Algorithms for the Vehicle Routing Problem with Time Windows (SINTEF Report STF42 A01021). Retrieved from http://www.top.sintef.no/Publications/GAVRPTW_Report.pdf
[4] Chen, C. C. (2009). K means algorithm. Retrieved from http://ccckmit.wikidot.com/ai:kmeans
[5] Chunhua. T. (2010). An Improving Genetic Algorithm for Vehicle Routing Problems with Time Windows. Paper presented at the annual meeting of the 6th International Conference on Intelligent Computation Technology and Automation Engineering, Changsha, PRC.
[6] Cheng, C. B. & Wang, K. P. (2009). Solving a vehicle routing problem with time windows by a decomposition technique and a genetic algorithm. Expert Systems with Applications, 36 (4), 7758-7763.
[7] Desrochers, M., Desrosiers, J., & Solomon, M. (1992). A New Optimization Algorithm for the Vehicle Routing Problem with Time Windows. Operations Research, (2), 342-354.
[8] El Rhalibi, A., & Kelleher, G. (2003). An approach to dynamic vehicle routing, rescheduling and disruption metrics. Paper presented at the IEEE International Conference on Systems, Man and Cybernetics. Washigton D.C., USA, 3613-3618.
[9] Hartigan, J. A. & Wong, M. A. (1979). Algorithm AS136: A K-Means Clustering Algorithm, Applied Statistics, 28(1), 100-108. Transactions on Systems, man, and Cybernetics - Part A: Systems and Humans, 33(2), 169-178.
[13] MacQueen, J. (1967). Some Methods for classification and Analysis of Multivariate Observations, Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press, 1:281-297
[14] Solomon, M. M. (1987). Algorithms for the Vehicle Routing and Scheduling Problems with Time Window Constraints. Operations Research, 35(2), 254-265.
[15] Solomon, M. M. & Desrosiers, J. (1988). Time Window Constrained Routing and Scheduling Problems. Transportation Science, 22(1), 1-13.
30
[16] Thangiah, S. R. (1993). Vehicle Routing Problem with Time Windows using Genetic Algorithms. Applications Handbook of Genetic Algorithms: New Frontiers, 1-24.
[17] Tan, K. C., Lee, L. H., & Ou, K. (2001). Hybrid Genetic Algorithms in Solving Vehicle Routing Problems with Time Window Constraints. Asia-Pacific Journal of Operational Research, 18(1), 121-130.
[18] Thangiah, S. R., Nygard, K. E., & Juell, P. L. (1991, Feb). GIDEON: A Genetic Algorithm System for Vehicle Routing with Time Windows. Paper presented at the Seventh International Conference on Artificial Intelligence Applications, Florida.
[19] Tan, K. C., Lee, L. H., Zhu, Q.L., & Ou, K. (2001a). Artificial intelligence heuristic in solving vehicle routing problems with time window constraints.
Engineering Applications of Artificial Intelligence, 14(6), 825-837.
[20] Tan, K. C., Lee, L. H., Zhu, Q. L., & Ou, K. (2001b). Heuristic methods for vehicle routing problems with time windows. Artificial Intelligence in Engineering, 15(3), 281-295.
[21] Wang, W., Wang, Z., & Qiao, F. (2008). An Improved Genetic Algorithm for Vehicle Routing Problems with Time-Window. Paper presented at the meeting of International Symposium on Computer Science and Computational Technology, Jiaozuo, PRC.
[22] Wang, X. P., Xu, C. L., & Hu, X. P. (2008). Genetic Algorithm for Vehicle Routing Problems with Time Windows and a Limited Number of Vehicles. Paper presented at the annual meeting of the 15th International Conference on Management Science & Engineering, Jiaozuo.
[23] Zhu, K. Q. (2000). A New Genetic Algorithm for VRPTW. Paper presented at the 5th International Conference on Artificial Intelligence, Las Vegas.
[24] Zou, T., Li, N., & Sun, D. (2004). Genetic Algorithm for Vehicle Routing Problems with Time Window with Uncertain Vehicle Number. Paper presented at the meeting of the 5th Intelligent Control and Automation, Hangzhou, PRC.
For Chapter 2: Design of Cloud Inventory Management System Based on RFID Technology - A Case Study on Taipei Flora Exposition
[1] Ge´rard P. Cachon and Marshall Fisher, “Supply Chain Inventory Management and the Value of Shared Information,” Management Science, vol. 46, no. 8, pp.
1032–1048, 2000.
[2] Shenghua Bao and Minjie Zhu, “Study of the Product Oil inventory Management Model Based on VMI,” International Forum on Information Technology and Applications, November, Kunming, 2010.
[3] Payman S and Chun W, “Improving Inventory Management through Automated Dynamic Promotion Scheduling,” IEEE International Conference on Computer Science and Information Technology, September, Chengdu, 2010.
[4] Liu June, Zhang Xiaocui and Liu Bingwu, “The application of RFID technology in the inventory management,” 2nd International Conference on Signal Processing Systems, August, Dalian, 2010.
[5] Shuai Zhang, Shufen Zhang, Xuebin Chen and Xiuzhen Huo, “The Comparison
31
Between Cloud Computing and Grid Computing,” International Conference on Computer Application and System Modeling, November, Taiyuan, 2010.
[6] Jiyi Wu, Lingdi Ping, Xiaoping Ge, Ya Wang and Jianqing Fu, “Cloud Storage as the Infrastructure of Cloud Computing,” International Conference on Intelligent Computing and Cognitive Informatics, September, Kuala Lumpur, 2010.
[7] Shufen Zhang, Shuai Zhang, Xuebin Chen, and Shangzhuo Wu, “Analysis and Research of Cloud Computing System Instance,” 2010 Second International Conference on Future Networks, March, Sanya, Hainan, 2010.
[8] Daniel Hellström, “The cost and process of implementing RFID technology to manage and control returnable transport items,” International Journal of Logistics Research and Applications, vol. 12, no. 1, pp. 1–21, 2009.
[9] Young M. Leea, Feng Cheng and Ying Tat Leung, “A quantitative view on how RFID can improve inventory management in a supply chain,” International Journal of Logistics Research and Applications, vol. 12, no. 1, pp. 23–43, 2009.
[10] P. Sasikumar and G. Kannan, “Issues in reverse supply chains, part I: end-of-life product recovery and inventory management - an overview,” International Journal of Sustainable Engineering, vol. 1, no. 3, pp. 154–172, 2008.
[11] Lapide, L, “RFID: What’s in it for the forecaster?” The Journal of Business Forecasting Methods & Systems, vol. 23, no. 2, pp. 16–19, 2004.
[12] Angeles, R, “RFID Technologies: Supply-Chain Applications and Implementation Issues,” Information Systems Management, vol. 22, no. 1, pp.
51–65, 2005.
[13] Helders, B and Vethman, A.J, “How RFID Will Change the Global Supply Chain?” Chain Store Age, vol. 79, no. 12, pp. 39–48, 2003.
[14] Su, X., Chu, C.-C., Prabhu, B. S., & Gadh, R., “On the identification device management and data capture via WinRFID1 edge-server.” IEEE Systems Journal, vol. 1, no. 2, pp. 95–104, 2007.
[15] Prabhu, B. S., Su, X., Ramamurthy, H., Chu, C.-C., & Gadh, R., “WinRFID: A middleware for the enablement of radio frequency identification (RFID)-based applications.” In Mobile, wireless, and sensor networks: Technology, applications, and future directions, John Wiley & Sons, Inc., pp. 331–336.
[16] EPC global RFID Implementation Cookbook,
<http://www.epcglobalinc.org/what/cookbook>.