以電動車電氣化運籌產業

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姓名

陳氏姮妮(Tran Thi Hang Ni) 查詢紙本館藏

畢業系所

工業管理研究所

論文名稱

以電動車電氣化運籌產業
(ELECTRIFICATION OF LOGISTIC INDUSTRIES WITH ELECTRIC VEHICLES)

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摘要(中)

交通運輸是每間物流公司的發展重要框架，道路網絡的創新研究可以實行在很多方面，包括社區時間表，物流和現代技術。具體來說，主導綠色製造產業的發展因素則為在運輸過程中所消耗的燃料、行程規畫、與供應商之間的距離和載運貨物數量。高效的運輸調度可以減少對環境的危害，並發展供應鏈程序，但其不利影響在於環境中的廢氣排放以及運輸過程產生的噪音。因此，考量到環境永續性，生產者的運輸成本以及溫室氣體排放降被視為其影響因子。
本研究應用看法則認為未來處於大規模運輸的分銷路網狀態下，由於貨運公司在運輸過程將二氧化碳排放到大氣中，在環境保護法的壓力下，為了消除溫室效應導致的氣候變化，貨運公司嘗試採用EV替代貨運卡車，目前電動汽車被選為替代傳統車輛的最佳解決方案。本研究目的在展示基於啟發式方法的電動汽車路徑問題（EVRP），並以應用Tabu搜索來處理VRP問題，同時以最小的旅行時間費用計算最佳方法，並最大化傳輸載運量結果給予客戶，並計算台灣指定物流公司每次運輸過程所排放的二氧化碳量。
關鍵詞：物流網絡、電動汽車路徑問題、Tabu 搜尋、綠色物流。

摘要(英)

In this research, innovation of road networks can be fulfilled in many dimensions, those are community schedule, logistics and modern techniques. In details, dominant factors on green manufacture are the fuel which can be consumed in the processes of transportations, a number of journeys, distances among suppliers and quantities of goods. Transportation is an essential framework in every logistic company. Efficient transportation scheduling can reduce harmfully environmental influences and develop supply chain procedures, thus, it is one of the primary addresses of green logistics enterprises, as a result, its unfavorable effects are remaining on the environment in part of emissions, noise and bottleneck. There are now concentrations on the delivery expenses as factor of producers and greenhouse gas amount considered as a factor of environment sustainability.
This application also emphasized the deeper visions on distribution networks on massive transportation and its prospects in the near future. Additionally, under the pressures of environment protection laws towards the release of CO2 into the atmosphere because of trucking companies, companies have boosted their attempts to use EV as a wise replacement instead of cargo trucks. With the purposes to eliminate greenhouse gases which is the main factor causing greenhouse effect or climate change, electric vehicles are contemporarily chosen as best solutions to substitute traditional vehicles. This dissertation is aiming to demonstrate the Electric Vehicle Routing Problem (EVRP) based on heuristic methods by applying the Tabu search to deal with VRP problem; that calculates the optimal approaches with minimal travel time expense as well as to maximize the product quantity transferred to the customers and calculate CO2 amount simultaneously on each shipping dispatched in the specified logistic company in Taiwan.
Keywords: logistic networks; electric vehicle routing problem; Tabu search; green logistic.

關鍵字(中)

★ 物流網絡
★ 電動汽車路徑問題
★ Tabu 搜尋
★ 綠色物流

關鍵字(英)

★ logistic networks
★ electric vehicle routing problem
★ Tabu search
★ green logistic

論文目次

ACKNOWLEDGMENT i
Abstract ii
摘要 iii
Table of Contents iv
Chapter 1. Introduction 1
1.1 Motivation 3
1.2 Purposes of research 3
1.3 Overview of thesis 4
1.4 Research scope 4
Chapter 2. Research Background and Literature Review 6
2.1 Logistics industries and their latest development 6
2.1.1 Foundation of logistic chain 7
2.1.2 Previous studies for logistics industries 8
2.2 Greening logistics with electric vehicles 14
2.2.1 Greening logistics industries 14
2.2.2 Electric Vehicles 18
2.3 Studies on heuristic algorithm for vehicle routing problems (VRP) 24
Chapter 3. Problem Definition and Methodology 28
3.1 Case company A 28
3.2 Problem Definition 30
3.3 Research Methodology 31
3.4 Model establishment 33
3.4.1 Model 34
3.4.2 Description of heuristic methodology based on Tabu Search. 35
Chapter 4. Computer Experiment 40
4.1 Environment assumption 40
4.1.1 The actual data from company A 41
4.1.2 Assumption 42
4.2 Experiment 47
4.2.1 Introduction of Java 47
4.3 Analysis of result 48
4.3.1 The actual data of company for using the Java 48
4.3.2. Running the Java programming 49
Chapter 5. Conclusion 57
5.1 Conclusion 57
5.2 Contribution 58
5.3 Future research 59
References 60

參考文獻

[1] Abbasi, M., Vassilopoulou, P., & Stergioulas, L. (2017). Technology roadmap for the creative industries. Creative Industries Journal, 10(18), 40-58.

[2] Afroditi, A., Boile, M., Theofanis, S., Sdoukopoulos, E., & Margaritis, D. (2014). Electric vehicle routing problem with industry constraints: trends and insights for future research. Transportation Research Procedia, 3, 452-459.

[3] Albekov, A., Parkhomenko, T., & Polubotko, A. (2017). Green Logistics in Russia: The Phenomenon of Progress, Economic and Environmental Security. European Research Studies Journal, 20(8), 13-21.

[4] Ashaari, N. A. M. S., Mukhtar, M., & Sahari, N. (2009). Logistic E-marketplace for agro-based industries in Malaysia. Paper presented at the Electrical Engineering and Informatics, 2009. ICEEI′09 in International Conference.

[5] Bekta?, T., & Laporte, G. (2011). The pollution-routing problem. Transportation Research Part B: Methodological, 45(8), 1232-1250.

[6] Blyde, J., Iberti, G., & Mussini, M. (2018). When does innovation matter for exporting Empirical Economics, 54(4), 1653-1671.

[7] Blyde, J., & Molina, D. (2015). Logistic infrastructure and the international location of fragmented production. Journal of International Economics, 95(2), 319-332.

[8] Bogue, R. (2014). Sustainable manufacturing: a critical discipline for the twenty-first century. Assembly Automation, 34(5), 117-122.

[9] Bortolini, M., Galizia, F. G., & Mora, C. (2017). Efficiency & sustainability model to design and manage two-stage logistic networks. Procedia Manufacturing, 11, 2170-2177.

[10] Cakravastia, A., & Diawati, L. (1998). Industrial Policy Design Based on Supply and Demand Sides. Case: Indonesia’s.

[11] Castro, F. G., Kellison, J. G., Boyd, S. J., & Kopak, A. (2010). A methodology for conducting integrative mixed methods research and data analyses. Journal of Mixed Methods Research, 4(4), 342-360.

[12] Chavez, J., Escobar, J., Echeverri, M., & Meneses, C. (2018). A heuristic algorithm based on tabu search for vehicle routing problems with backhauls. Decision Science Letters, 7(2), 171-180.

[13] Chen, S.-L., Chen, Y.-Y., & Hsu, C. (2014). A new approach to integrate internet-of-things and software-as-a-service model for logistic systems: A case study. Sensors, 14(4), 6144-6164.

[14] Chryssolouris, G., Mavrikios, D., & Mourtzis, D. (2013). Manufacturing systems: skills & competencies for the future. Procedia CIRp, 7, 17-24.

[15] Coskuntuncel, A., & Rad, S. T. (2015). The international logistics center of turkey: situational analysis of mersin port′s seaways logistics. International Journal of Management and Sustainability, 4(4), 89.

[16] Davis, B. A., & Figliozzi, M. A. (2013). A methodology to evaluate the competitiveness of electric delivery trucks. Transportation Research Part E: Logistics and Transportation Review, 49(15), 8-23.

[17] El-Sherbeny, N. A. (2010). Vehicle routing with time windows: An overview of exact, heuristic and metaheuristic methods. Journal of King Saud University-Science, 22(3), 123-131.

[18] El-Yaakoubi, A., El-Fallahi, A., Cherkaoui, M., & Reghioui, M. (2017). Tabu Search and Memetic Algorithms for a Real Scheduling and Routing Problem.

[19] Elhedhli, S., & Merrick, R. (2012). Green supply chain network design to reduce carbon emissions. Transportation Research Part D: Transport and Environment, 17(5), 370-379.

[20] Erdo?an, S., & Miller-Hooks, E. (2012). A green vehicle routing problem. Transportation Research Part E: Logistics and Transportation Review, 48(13), 100-114.

[21] Facts, E. (2005). Average carbon dioxide emissions resulting from gasoline and diesel fuel. US EPA, February.

[22] Fudge, S., & Peters, M. (2011). Behaviour change in the UK climate debate: an assessment of responsibility, agency and political dimensions. Sustainability, 3(6), 789-808.

[23] Gilgeous, V., & Gilgeous, M. (1999). A framework for manufacturing excellence. Integrated Manufacturing Systems, 10(1), 33-44.

[24] Gimenez, C., Sierra, V., & Rodon, J. (2012). Sustainable operations: Their impact on the triple bottom line. International Journal of Production Economics, 140(10), 149-159.

[25] Glover, F. (1989). Tabu search—part I. ORSA Journal on Computing, 1(3), 190-206.

[26] Glover, F., Laguna, M., & Marti, R. (2018). Principles and Strategies of Tabu Search. Handbook of Approximation Algorithms and Metaheuristics: Methologies and Traditional Applications, 1.

[27] Goeke, D., & Schneider, M. (2015). Routing a mixed fleet of electric and conventional vehicles. European Journal of Operational Research, 245(18), 81-99.

[28] Gokilavani, S., Rajasekaran, R., & Krupa, M. E. A Study on Logistic and Supply Chain Management System of Hosiery Textile Industry in Tiruppur City-India.

[29] Greulich, C., Edelkamp, S., Gath, M., Warden, T., Humann, M., Herzog, O., & Sitharam, T. (2013). Enhanced Shortest Path Computation for Multiagent-based Intermodal Transport Planning in Dynamic Environments. Paper presented at the ICAART.

[30] Hannola, L., & Myller, E. (2012). Identifying the logistic bottlenecks of manufacturing industries in a transportation corridor. International Journal of Logistics Systems and Management, 13(15), 35-50.

[31] Harmanani, H. M., Azar, D., Helal, N., & Keirouz, W. (2011). A Simulated Annealing Algorithm for the Capacitated Vehicle Routing Problem. Paper presented at the CATA.
[32] Hiermann, G., Puchinger, J., Ropke, S., & Hartl, R. F. (2016). The electric fleet size and mix vehicle routing problem with time windows and recharging stations. European Journal of Operational Research, 252(3), 995-1018.

[33] Hutomo, A., Saudi, M. M., & Sinaga, H. (2018). The Part of Role Relational Bonding: Moderating Relationship Between Green Logistics and Sustainability Performance. Journal of Fundamental and Applied Sciences, 10, 732-751.

[34] Keskin, M., & Catay, B. (2016). Partial recharge strategies for the electric vehicle routing problem with time windows. Transportation Research Part C: Emerging Technologies, 65, 111-127.

[35] Kir, S., Yazgan, H. R., & Tuncel, E. (2017). A novel heuristic algorithm for capacitated vehicle routing problem. Journal of Industrial Engineering International, 13(3), 323-328.

[36] Koc, C., Bekta?, T., Jabali, O., & Laporte, G. (2014). The fleet size and mix pollution-routing problem. Transportation Research Part B: Methodological, 70, 239-254.

[37] Kolay, H. ?., & ?i?man, C. B. Investigation of the Greenhouses in Istanbul in terms of Structural Properties.

[38] Krishnan, K., White, J. A., & Maggenti, M. (2012). Integrated display and management of data objects based on social, temporal and spatial parameters: Google Patents.

[39] Kuo, R. J., Wang, Y. C., & Tien, F. C. (2010). Integration of artificial neural network and MADA methods for green supplier selection. Journal of Cleaner Production, 18(12), 1161-1170.

[40] Laporte, G. (2009). Fifty years of vehicle routing. Transportation Science, 43(4), 408-416.

[41] Li, Q., Luo, W., Wang, Y., & Wu, L. (2013). Firm performance, corporate ownership, and corporate social responsibility disclosure in China. Business Ethics: A European Review, 22(5), 159-173.

[42] Lin, C.-Y., & Ho, Y.-H. (2008). An empirical study on logistics service providers’ intention to adopt green innovations. Journal of Technology Management & Innovation, 3(9), 17-26.

[43] Lin, J., Zhou, W., & Wolfson, O. (2016). Electric vehicle routing problem. Transportation Research Procedia, 12, 508-521.

[44] Liu, Y., Zhu, Q., & Seuring, S. (2017). Linking capabilities to green operations strategies: The moderating role of corporate environmental proactivity. International Journal of Production Economics, 187, 182-195.

[45] McKinnon, A. (2010). Green logistics: the carbon agenda. Electronic Scientific Journal of Logistics.

[46] Pagliari, S. (2012). Making good financial regulation: Towards a policy response to regulatory capture: Grosvenor House Publishing.

[47] Paz, J., Granada-Echeverri, M., & Escobar, J. (2018). The multi-depot electric vehicle location routing problem with time windows. International Journal of Industrial Engineering Computations, 9(13), 123-136.

[48] Penna, P. H. V., Afsar, H. M., Prins, C., & Prodhon, C. (2016). A Hybrid Iterative Local Search Algorithm for The Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and Recharging Stations. IFAC-PapersOnLine, 49(12), 955-960.

[49] Perona, M., Cigolini, R., Adani, M., Biondi, R., Guzzetti, S., Jenna, R., Agellara, S. (2001). The integrated management of logistic chains in the white goods industry. A field research in Italy. International Journal of Production Economics, 69(11), 227-238.

[50] Rad, S. T., & Gulmez, Y. S. (2017). Green logistics for sustainability. International Journal of Management Economics & Business, 13(11), 603-614.

[51] Sahu, A. K., Datta, S., & Mahapatra, S. S. (2013). Green supply chain performance benchmarking using integrated IVFN-TOPSIS methodology. International Journal of Process Management and Benchmarking, 3(4), 511-551.

[52] Salehi, M., Jalalian, M., & Siar, M. M. V. (2017). Green transportation scheduling with speed control: trade-off between total transportation cost and carbon emission. Computers & Industrial Engineering, 113, 392-404.

[53] Santa Chaveza, J. J., Escobarb, J. W., Echeverric, M. G., Augusto, C., & Menesesd, P. (2017). A heuristic algorithm based on tabu search for vehicle routing problems with backhauls. Decision Science Letters, 7(9), 171-180.

[54] Savelsberg, R. (2013). Green Logistics – Marketing Needs influencing Supply Chains. IFAC Proceedings Volumes, 46(8), 185-190.

[55] Schiffer, M., & Walther, G. (2017). The electric location routing problem with time windows and partial recharging. European Journal of Operational Research, 260(15), 995-1013.

[56] Shao, S., Guan, W., Ran, B., He, Z., & Bi, J. (2017). Electric Vehicle Routing Problem with Charging Time and Variable Travel Time. Mathematical Problems in Engineering, 2017.

[57] Silvestrin, P. V., & Ritt, M. (2017). An iterated tabu search for the multi-compartment vehicle routing problem. Computers & Operations Research, 81, 192-202.

[58] Singla, A., Ahuja, I., & Sethi, A. (2017). The effects of demand pull strategies on sustainable development in manufacturing industries. International Journal of Innovations in Engineering and Technology, 8(7), 27-34.

[59] Soto, M., Sevaux, M., Rossi, A., & Reinholz, A. (2017). Multiple neighborhood search, tabu search and ejection chains for the multi-depot open vehicle routing problem. Computers & Industrial Engineering, 107, 211-222.

[60] Sun, L.-Y., Miao, C.-L., & Yang, L. (2018). Ecological environmental early-warning model for strategic emerging industries in China based on logistic regression. Ecological Indicators, 84, 748-752.

[61] Toth, P., & Vigo, D. (2002). An overview of vehicle routing problems The vehicle routing problem, SIAM, 1-26.

[62] Vanderheyden, B., & Priestley, J. L. (2017). Logistic Ensemble Models. arXiv preprint arXiv:1806.04555.

[63] Wood, G. M., Johnson, B. C., & McCormack, J. G. (1996). Moraxella catarrhalis: pathogenic significance in respiratory tract infections treated by community practitioners. Clinical infectious diseases, 22(4), 632-636.

[64] Yadav, M., & Xu, Q. (2012). Liquid-phase chemical hydrogen storage materials. Energy & Environmental Science, 5(12), 9698-9725.

[65] Yang, Y.-C., & Chen, S.-L. (2016). Determinants of global logistics hub ports: Comparison of the port development policies of Taiwan, Korea, and Japan. Transport Policy, 45, 179-189.

[66] Zheng, H., & Peeta, S. (2017). Routing and charging locations for electric vehicles for intercity trips. Transportation Planning and Technology, 40(4), 393-419.

指導教授

王啟泰(Chi–Tai Wang)

審核日期

2018-7-25

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