Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/125534
|
Title: | 共享單車企業的綠色閉環供應鏈模型設計 A Green Closed-loop Supply Chain Model for Sharing Bicycle Enterprises |
Authors: | 季意 Ji, Yi |
Contributors: | 林我聰 Lin, Woo-Tsong 季意 Ji, Yi |
Keywords: | 共享經濟 共享單車 綠色閉環供應鏈 多目標整數規劃模型 利潤最大化 碳排放最小化 NSGA-II演算法 Pareto解集 sharing economy sharing bicycle green closed-loop supply chain multi-objective integer programming model profit maximization carbon minimization NSGA-II Algorithm Pareto solution set |
Date: | 2019 |
Issue Date: | 2019-09-05 15:45:44 (UTC+8) |
Abstract: | 共享經濟是源於實踐的全新經濟模式,當共享的理念慢慢深入人心,各種基於共享理念的商業模式紛紛出現,並顯示出強大的發展趨勢和潛力。共享單車作為共享經濟中備受矚目的一員,從誕生開始就伴隨著爭議,共享單車能夠解決城市交通“最後一公里”的問題,能夠促進資源合理分配推動環保出行,但在發展過程中卻造成很多意想不到的社會問題。本研究通過為共享單車企業設計適合的綠色閉環供應鏈來解決這些企業現存的種種問題。通過分析共享單車企業的模式與特點,建立出以最大化利潤以及最小化鏈上碳排放量為目標的多目標整數規劃模型,模型求解的部分使用NSGA-II演算法尋找模型的Pareto解集,通過求得的解集可以幫助共享單車企業妥善設計、建設和安排閉環供應鏈上的設施以及開啟狀況並能夠合理控制鏈上節點間的流量,以獲得系統利潤最大化且盡可能減少系統的碳排放。 Sharing economy is a brand-new economic model which originates from practice. When the concept of sharing is deeply rooted in people`s mind, various business models based on sharing concept emerge one after another and show strong development trend and potential. As a member of the sharing economy, sharing bicycle has been controversial since its birth. Sharing bicycle can solve the problem of "the last kilometer" of urban traffic, and can promote the rational allocation of resources to promote environmental protection travel. But in the process of development, it has caused many unexpected social problems. In this paper, we design a green closed-loop supply chain for bicycle-sharing enterprises to solve the existing problems of these enterprises. A multi-objective integer programming model is established to maximize the profit and minimize the carbon emissions in the chain by analyzing the models and characteristics of bicycle-sharing enterprises The part of the solution of the model uses NSGA-II Algorithm to find the Pareto solution set of the model The solution set can help the bicycle-sharing enterprise to design, construct and arrange the facilities and the open status of the closed-loop supply chain and control the flow between the nodes To profit maximization the system and minimize the carbon footprint of the system. |
Reference: | 1. Aras et al.(2008).Locating collection centers for incentive-dependent returns under a pick-up policy with capacitated vehicles,Eur. J. Oper. Res., 191 2008, pp. 1223-1240 2. Abdallah et al.(2012). Green supply chains with carbon trading and environmental sourcing: Formulation and life cycle assessment, Applied Mathematical Modelling, Vol. 36 (9), pp. 4271-4285 3. Bazan et al. 2016) A review of mathematical inventory models for reverse logistics and the future of its modeling: An environmental perspective, Applied Mathematical Modelling,Volume 40, Issues 5–6, March 2016, pp. 4151-4178 4. Chemla et al. 2013). Bike sharing systems: Solving the static rebalancing problem, Discrete Optimization,Volume 10, Issue 2, May 2013, pp.120-146 5. Cohen&Welling,(2015). Transformation Properties of Learned Visual Representations, In International Conference on Learning Representations (ICLR), 2015 6. Corne, et al.(2000). The Pareto envelope-based selection algorithm for multiobjective optimization, Proceedings of sixth international conference on parallel problem solving from Nature, 18–20 September, 2000, Springer, Paris, France 7. Deb et al.(2002). A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans Evol Comput, Vol. 6 (2), pp. 182-197 8. Deb et al.(2000). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II,Proceedings of sixth international conference on parallel problem solving from nature, 18–20 September, 2000, Springer, Paris, France 9. Diabat et al.(2013).Strategic closed-loop facility location problem with carbon market trading,IEEE Trans. Eng. Manag., 60 (2) (2013), pp. 398-408 10. Fahimnia et al. (2013).The impact of carbon pricing on a closed-loop supply chain: an Australian case study, Journal of Cleaner Prod., 59 (13), pp. 210-225 11. Fleischmann et al.(1997). Quantitative models for reverse logistics: A review, European Journal of Operational Research,Volume 103, Issue 1, 16 November 1997, pp. 1-17 12. Fleischmann et al. (2001). The impact of product recovery on logistics network design,Prod. Oper. Manag., Vol. 10 , pp. 156-173 13. Fonseca&Fleming,(1993).Genetic algorithms for multiobjective optimization: formulation, discussion and generalization, Proceedings of the ICGA-93: fifth international conference on genetic algorithms, 17–22 July 1993, Morgan Kaufmann, Urbana-Champaign, IL, USA 14. Guide&Van(2001). WassenhoveManaging product returns for remanufacturing, Prod. Oper. Manag., Vol. 10 (2), pp. 142-155 15. Goldberg,(1989), Genetic algorithms in search, optimization, and machine learning, Addison-Wesley Longman Publishing Co., Boston, MA, USA 16. Gui et al.(2016). Efficient Implementation of Collective Extended Producer Responsibility Legislation,Manage. Sci., Vol. 62 (4), pp. 1098-1123 17. Govindan et al.(2015). Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future, European Journal of Operational Research,Volume 240, Issue 3, 1 February 2015, pp. 603-626 18. Holland(1975)Adaptation in natural and artificial systems,University of Michigan Press, Ann Arbor Kapetanopoulou , Tagaras , (2010) . Drivers and obstacles of product recovery activities in the Greek industry, Int. J. Oper. Prod. Manag. Vol. 31 (2) 148-166 19. Jayaramana et al.(2003). The design of reverse distribution networks: Models and solution procedures European Journal of Operational ResearchVolume 150, Issue 1, 1 October 2003, Pages 128-149 20. Ko&Evans(2007)A genetic algorithm-based heuristic for the dynamic integrated forward/reverse logistics network for 3PLs,Computers & Operations Research, 34 (2) (2007), pp. 346-366 21. Kadambala et al.,(2017). Closed loop supply chain networks: Designs for energy and time value efficiency, International Journal of Production Economics,Volume 183, Part B, January 2017, pp. 382-393 22. Krikke,(2011). Impact of closed-loop network configurations on carbon footprints: A case study in copiers, Resources, Conservation and Recycling,Volume 55, Issue 12, October 2011,pp. 1196-1205 23. Konak et al.(2006). Multi-objective optimization using genetic algorithms: A tutorial, Reliability Engineering & System Safety,Volume 91, Issue 9, September 2006, pp.992-1007 24. Lee,(2009),Dynamic network design for reverse logistics operations under uncertaintyTransp. Res. Part E, 45 (2009), pp. 61-71 25. Min (2006). A genetic algorithm approach to developing the multi-echelon reverse logistics network for product returns, Omega Vol.34, pp.56–69 26. Soleimani et al.(2013). Designing and planning a multi-echelon multi-period multi-product closed-loop supply chain utilizing genetic algorithm, The International Journal of Advanced Manufacturing Technology, Vol. 68 (1–4), pp. 917-931 27. Pishvaee et al. (2009),A stochastic optimization model for integrated forward/reverse logistics network design J. Manuf. Syst., 28 (2009), pp. 107-114 28. Pishvaee,&Kianfar(2010),Reverse logistics network design using simulated annealing Int. J. Adv. Manuf. Technol., 47 (2010), pp. 269-281 29. RoHS (2008).Working with EEE producers to ensure RoHS compliance through the European Union, URL http://www.rohs.eu/english/index.html. 30. Rahman&Subramanian,(2012). Factors for implementing end-of-life computer recycling operations in reverse supply chains, Int. J. Prod. Econ., Vol. 140 pp. 239-248 31. Su(2014). Fuzzy multi-objective recoverable remanufacturing planning decisions involving multiple components and multiple machines, Computers & Industrial Engineering, Vol. 72 , pp. 72-83 32. Shi et al.(2017). Multi-objective optimization for a closed-loop network design problem using an improved genetic algorithm, Applied Mathematical Modelling,Volume 45, May 2017, pp. 14-30 33. Walther&Spengler, (2005). Impact of WEEE-directive on reverse logistics in Germany, Int. J. Phys. Distrib. Logist. Manag. 35 337–361 34. Özkır&Başlıgil, (2013). Multi-objective optimization of closed-loop supply chains in uncertain environment, Journal of Cleaner Production, Vol. 41, pp. 114-125 35. Zitzler&Thiele,(1999).Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach,IEEE Trans Evol Comput, 3 (4) (1999), pp. 257-271 36. Zitzler et al.(2000). Comparison of multiobjective evolutionary algorithms: empirical results, Evol Comput, 8 (2), pp. 173-195 37. Zailini et al.(2012), Sustainable supply chain management (SSCM) in Malaysia: A survey, International Journal of Production Economics,Volume 140, Issue 1, November 2012, pp. 330-340 38. Zhou&Gen(1999). Genetic algorithm approach on multi-criteria minimum spanning tree problem, Eur. J. Oper. Res., Vol. 114, pp. 141-152 39. 李敏蓮,(2017)。共享單車市場調研與分析。財經界,pp.121-123。 40. 劉亞楠,(2017)。共享單車發展研究分析。時代金融,No.03,pp.251-254。 41. 常山,宋瑞,何世偉,黎浩東,(2018)。共享單車故障車輛回收模型。吉林大學學報,Vol.48,No.6 pp.1677-1683。 42. 郭鹏,林祥枝,黄艺,涂思明,白晓明,杨雅雯,叶林,(2017)。共享单车:互联网技术与公共服务中的协同治理。公共管理學報,No.3 ,pp.1-10。 43. 湯天波,吳曉隽,(2015)。共享经济:“互联网+”下的颠覆性经济模式。科學發展,No.12,pp.78-85。 44. 胡靜靜,(2018)。共享經濟:國內外文獻綜述與研究展望。改革與戰略No.34,pp.134-138。 45. 中國信通院,2017年共享單車經濟社會影響報告,上網日期2018年2月6日,檢自:http://www.caict.ac.cn/sytj/201802/t20180206_172836.htm 46. 李成東,摩拜1000,哈罗800,ofo的车500块到底有什么差别?,上網日期2018年4月17日,檢自:https://zhuanlan.zhihu.com/p/35768121 47. 企鵝智酷調查,2016年12月,檢自https://tech.qq.com/a/20170228/019218.htm |
Description: | 碩士 國立政治大學 資訊管理學系 106356042 |
Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0106356042 |
Data Type: | thesis |
DOI: | 10.6814/NCCU201901020 |
Appears in Collections: | [資訊管理學系] 學位論文
|
Files in This Item:
File |
Size | Format | |
604201.pdf | 1671Kb | Adobe PDF2 | 0 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|