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題名 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作者 季意
Ji, Yi貢獻者 林我聰
Lin, Woo-Tsong
季意
Ji, Yi關鍵詞 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日期 2019 上傳時間 5-九月-2019 15:45:44 (UTC+8) 摘要 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.參考文獻 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. 1223-12402. 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. 4271-42853. 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. 4151-41784. Chemla et al. 2013). Bike sharing systems: Solving the static rebalancing problem, Discrete Optimization,Volume 10, Issue 2, May 2013, pp.120-1465. Cohen&Welling,(2015). Transformation Properties of Learned Visual Representations, In International Conference on Learning Representations (ICLR), 20156. 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, France7. Deb et al.(2002). A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans Evol Comput, Vol. 6 (2), pp. 182-1978. 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, France9. Diabat et al.(2013).Strategic closed-loop facility location problem with carbon market trading,IEEE Trans. Eng. Manag., 60 (2) (2013), pp. 398-40810. 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. 210-22511. Fleischmann et al.(1997). Quantitative models for reverse logistics: A review, European Journal of Operational Research,Volume 103, Issue 1, 16 November 1997, pp. 1-1712. Fleischmann et al. (2001). The impact of product recovery on logistics network design,Prod. Oper. Manag., Vol. 10 , pp. 156-17313. 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, USA14. Guide&Van(2001). WassenhoveManaging product returns for remanufacturing, Prod. Oper. Manag., Vol. 10 (2), pp. 142-15515. Goldberg,(1989), Genetic algorithms in search, optimization, and machine learning, Addison-Wesley Longman Publishing Co., Boston, MA, USA16. Gui et al.(2016). Efficient Implementation of Collective Extended Producer Responsibility Legislation,Manage. Sci., Vol. 62 (4), pp. 1098-112317. 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. 603-62618. 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) 148-16619. 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 128-14920. 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. 346-36621. 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. 382-39322. 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. 1196-120523. Konak et al.(2006). Multi-objective optimization using genetic algorithms: A tutorial, Reliability Engineering & System Safety,Volume 91, Issue 9, September 2006, pp.992-100724. Lee,(2009),Dynamic network design for reverse logistics operations under uncertaintyTransp. Res. Part E, 45 (2009), pp. 61-7125. Min (2006). A genetic algorithm approach to developing the multi-echelon reverse logistics network for product returns, Omega Vol.34, pp.56–6926. 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. 917-93127. Pishvaee et al. (2009),A stochastic optimization model for integrated forward/reverse logistics network design J. Manuf. Syst., 28 (2009), pp. 107-11428. Pishvaee,&Kianfar(2010),Reverse logistics network design using simulated annealing Int. J. Adv. Manuf. Technol., 47 (2010), pp. 269-28129. 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. 239-24831. Su(2014). Fuzzy multi-objective recoverable remanufacturing planning decisions involving multiple components and multiple machines, Computers & Industrial Engineering, Vol. 72 , pp. 72-8332. 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. 14-3033. Walther&Spengler, (2005). Impact of WEEE-directive on reverse logistics in Germany, Int. J. Phys. Distrib. Logist. Manag. 35 337–36134. Özkır&Başlıgil, (2013). Multi-objective optimization of closed-loop supply chains in uncertain environment, Journal of Cleaner Production, Vol. 41, pp. 114-12535. Zitzler&Thiele,(1999).Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach,IEEE Trans Evol Comput, 3 (4) (1999), pp. 257-27136. Zitzler et al.(2000). Comparison of multiobjective evolutionary algorithms: empirical results, Evol Comput, 8 (2), pp. 173-19537. 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. 330-34038. Zhou&Gen(1999). Genetic algorithm approach on multi-criteria minimum spanning tree problem, Eur. J. Oper. Res., Vol. 114, pp. 141-15239. 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.htm46. 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/3576812147. 企鵝智酷調查，2016年12月，檢自https://tech.qq.com/a/20170228/019218.htm 描述 碩士
國立政治大學
資訊管理學系
106356042資料來源 http://thesis.lib.nccu.edu.tw/record/#G0106356042 資料類型 thesis dc.contributor.advisor 林我聰 zh_TW dc.contributor.advisor Lin, Woo-Tsong en_US dc.contributor.author (作者) 季意 zh_TW dc.contributor.author (作者) Ji, Yi en_US dc.creator (作者) 季意 zh_TW dc.creator (作者) Ji, Yi en_US dc.date (日期) 2019 en_US dc.date.accessioned 5-九月-2019 15:45:44 (UTC+8) - dc.date.available 5-九月-2019 15:45:44 (UTC+8) - dc.date.issued (上傳時間) 5-九月-2019 15:45:44 (UTC+8) - dc.identifier (其他 識別碼) G0106356042 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/125534 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊管理學系 zh_TW dc.description (描述) 106356042 zh_TW dc.description.abstract (摘要) 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。 zh_TW dc.description.abstract (摘要) 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. en_US dc.description.tableofcontents 第一章 緒論 8第一節 研究背景介紹 8第二節 研究動機與目的 12第二章 文獻回顧 14第一節 共享經濟及共享單車 14第二節 綠色閉環供應鏈 16第三節 解決辦法 20第三章 研究問題介紹 22第一節 問題概述 22第二節 模型假設 25第三節 多目標整數規劃模型 30一、目標式之一 利潤的最大化 30二、目標式之二 CO2排放量的最小化 32三、限制式 33第四章 模型求解方法 36第一節 多目標規劃問題的解法 36第二節 NSGA-II演算法介紹 40一、演算法簡介 40二、演算法套用 45第五章 數值算例及結果分析 48第一節、模擬測試問題 48第二節、參數值的設置 52第三節、計算結果及分析 55第六章 結論 58第一節、結論 58第二節、未來研究方向 59參考文獻 61 zh_TW dc.format.extent 1711313 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0106356042 en_US dc.subject (關鍵詞) 共享經濟 zh_TW dc.subject (關鍵詞) 共享單車 zh_TW dc.subject (關鍵詞) 綠色閉環供應鏈 zh_TW dc.subject (關鍵詞) 多目標整數規劃模型 zh_TW dc.subject (關鍵詞) 利潤最大化 zh_TW dc.subject (關鍵詞) 碳排放最小化 zh_TW dc.subject (關鍵詞) NSGA-II演算法 zh_TW dc.subject (關鍵詞) Pareto解集 zh_TW dc.subject (關鍵詞) sharing economy en_US dc.subject (關鍵詞) sharing bicycle en_US dc.subject (關鍵詞) green closed-loop supply chain en_US dc.subject (關鍵詞) multi-objective integer programming model en_US dc.subject (關鍵詞) profit maximization en_US dc.subject (關鍵詞) carbon minimization en_US dc.subject (關鍵詞) NSGA-II Algorithm en_US dc.subject (關鍵詞) Pareto solution set en_US dc.title (題名) 共享單車企業的綠色閉環供應鏈模型設計 zh_TW dc.title (題名) A Green Closed-loop Supply Chain Model for Sharing Bicycle Enterprises en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 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. 1223-12402. 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. 4271-42853. 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. 4151-41784. Chemla et al. 2013). Bike sharing systems: Solving the static rebalancing problem, Discrete Optimization,Volume 10, Issue 2, May 2013, pp.120-1465. Cohen&Welling,(2015). Transformation Properties of Learned Visual Representations, In International Conference on Learning Representations (ICLR), 20156. 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, France7. Deb et al.(2002). A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans Evol Comput, Vol. 6 (2), pp. 182-1978. 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, France9. Diabat et al.(2013).Strategic closed-loop facility location problem with carbon market trading,IEEE Trans. Eng. Manag., 60 (2) (2013), pp. 398-40810. 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. 210-22511. Fleischmann et al.(1997). Quantitative models for reverse logistics: A review, European Journal of Operational Research,Volume 103, Issue 1, 16 November 1997, pp. 1-1712. Fleischmann et al. (2001). The impact of product recovery on logistics network design,Prod. Oper. Manag., Vol. 10 , pp. 156-17313. 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, USA14. Guide&Van(2001). WassenhoveManaging product returns for remanufacturing, Prod. Oper. Manag., Vol. 10 (2), pp. 142-15515. Goldberg,(1989), Genetic algorithms in search, optimization, and machine learning, Addison-Wesley Longman Publishing Co., Boston, MA, USA16. Gui et al.(2016). Efficient Implementation of Collective Extended Producer Responsibility Legislation,Manage. Sci., Vol. 62 (4), pp. 1098-112317. 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. 603-62618. 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) 148-16619. 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 128-14920. 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. 346-36621. 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. 382-39322. 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. 1196-120523. Konak et al.(2006). Multi-objective optimization using genetic algorithms: A tutorial, Reliability Engineering & System Safety,Volume 91, Issue 9, September 2006, pp.992-100724. Lee,(2009),Dynamic network design for reverse logistics operations under uncertaintyTransp. Res. Part E, 45 (2009), pp. 61-7125. Min (2006). A genetic algorithm approach to developing the multi-echelon reverse logistics network for product returns, Omega Vol.34, pp.56–6926. 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. 917-93127. Pishvaee et al. (2009),A stochastic optimization model for integrated forward/reverse logistics network design J. Manuf. Syst., 28 (2009), pp. 107-11428. Pishvaee,&Kianfar(2010),Reverse logistics network design using simulated annealing Int. J. Adv. Manuf. Technol., 47 (2010), pp. 269-28129. 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. 239-24831. Su(2014). Fuzzy multi-objective recoverable remanufacturing planning decisions involving multiple components and multiple machines, Computers & Industrial Engineering, Vol. 72 , pp. 72-8332. 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. 14-3033. Walther&Spengler, (2005). Impact of WEEE-directive on reverse logistics in Germany, Int. J. Phys. Distrib. Logist. Manag. 35 337–36134. Özkır&Başlıgil, (2013). Multi-objective optimization of closed-loop supply chains in uncertain environment, Journal of Cleaner Production, Vol. 41, pp. 114-12535. Zitzler&Thiele,(1999).Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach,IEEE Trans Evol Comput, 3 (4) (1999), pp. 257-27136. Zitzler et al.(2000). Comparison of multiobjective evolutionary algorithms: empirical results, Evol Comput, 8 (2), pp. 173-19537. 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. 330-34038. Zhou&Gen(1999). Genetic algorithm approach on multi-criteria minimum spanning tree problem, Eur. J. Oper. Res., Vol. 114, pp. 141-15239. 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.htm46. 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/3576812147. 企鵝智酷調查，2016年12月，檢自https://tech.qq.com/a/20170228/019218.htm zh_TW dc.identifier.doi (DOI) 10.6814/NCCU201901020 en_US
