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研究生: 許銘宏
Shiu, Ming-Hung
論文名稱: 以新型及可變空間規劃法求工業電腦綠色供應鏈之渴望解
Derivations of Aspired Solutions of Green Supply Chains for Industrial Personal Computers Using the De Novo
指導教授: 黃啟祐
Huang, Chi-Yo
學位類別: 碩士
Master
系所名稱: 工業教育學系
Department of Industrial Education
論文出版年: 2018
畢業學年度: 106
語文別: 英文
論文頁數: 131
中文關鍵詞: 綠色供應鏈管理逆物流工業電腦可變空間規劃法De Novo 規劃法
英文關鍵詞: Green Supply Chain, Reverse logistics, Industrial Personal Computer, De Novo Programming, Changeable Space Programming
DOI URL: http://doi.org/10.6345/THE.NTNU.DIE.055.2018.E01
論文種類: 學術論文
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  • 隨著電子科技的進步及產業快速的發展及變化,人們對於科技產品的依賴也相對增加,近幾年來因工業高度發展下對於地球所相對的氣候異常、環境的破壞、對人類生存環境已達嚴重影響的程度;而對於在生產中所產生有害物質已造成環境所造成嚴重的汙染,人們也對於環保意識越來越重視,導入綠色材料及綠色供應鏈已變成目前電子產業勢在必行的趨勢。 綠色材料及綠色供應鏈在導入電子產業時,採購在對於供應鏈管理對整個綠色材料及供應鏈的環境管理績效影響甚鉅,採購部門對供應鏈材料及逆物流綠色標準和指標的要求,有助於促進逆物材料供應鏈的積極研發和應用綠色技術、採用環保節能工藝、創造安全無毒的工作場所,進而提升供應商的環保意識、提高環境管理、改善環境水準。由於傳統材料的價格,因為不需要考量生命週期與對社會環境影響的成本,所以成本比綠色材料還低,但綠色材料的應用除了成本提升外,生產時所產生的不良率也會增加及逆物流材料成本較高,尤其在工業電子產業中其特性為客製化多樣少量化的產品,企業如何能在多樣少量的產業特性中使產能同時兼顧產能及利潤,使其得到生產效率及利潤最佳化並在導入整個綠色供應鏈中達成事半功倍的目的。
    本研究先運用傳統線性規劃法求工業電腦產業綠色材料在達到產能及利潤之理想解後,再利用可變空間規劃法突破柏拉圖前緣之限制,進而得到產能及利潤渴望解。對於本研究的結果在將來可作為工業電腦在綠色供應鏈中產能及利潤規劃之基礎。

    According to the advancement of electronic technology and the rapid development and changes of the industry, people's dependence on technology products has also increased. In recent years, due to the high degree of industrial development, the relative climate anomalies, environmental damage, and human living environment have reached The extent of serious impact; as the harmful substances produced in production have caused serious pollution in the environment, people are paying more and more attention to environmental protection. The introduction of green materials and green supply-chains has become an imperative trend in the current electronics industry.
    When green materials and green supply-chains are introduced into the electronics industry, procurement has a significant impact on the environmental management performance of the entire green materials and supply-chain when it comes to supply-chain management.
    The procurement department's requirements for supply-chain materials and reverse logistics green standards and indicators will help promote the active research and development of the reverse material supply-chain and the application of green technologies, the adoption of environmentally friendly energy-saving processes, and the creation of safe and non-toxic workplaces; in order to improve the environmental awareness of suppliers, improve environmental management, and improve environmental standards.
    Due to the price of traditional materials, because the cost of the life cycle and the impact on the social environment does not need to be considered; the cost is lower than that of green materials. However, in addition to the cost increase, the application of green materials will increase the adverse rate generated during production. The cost of logistics materials is relatively high, especially in the industrial electronics industry, where the characteristics are customized and diversified. How can a company make production capacity and profit at the same time in a variety of industrial characteristics, so that it can optimize production efficiency and profit and achieve more with less effort in the whole green supply-chain.
    This study first uses the traditional linear programming method to find the ideal point for the production capacity and profit of the green materials of the industrial computer industry, and then use the changeable space programming to break through the limits of Pareto optimal solution and then obtain produce capacity and profit aspires level. The results of this study will be used as the basis for industrial computer capacity, and profit planning in the green supply-chain in the future.

    摘要 i Abstract ii Table of Contents iv List of Table vi List of Figure vii Chapter 1 Introduction 1 1.1 Research Background 1 1.2 Research Motivations 3 1.3 Research Purposes and Limitations 8 1.4 Research Framework and Method 10 1.5 Full text Framework 13 Chapter 2 Literature Review 15 2.1 Supply-chain and Logistics Management 15 2.2 Green supply-chain management 22 2.2.1 Green Supply-chain Regulations 28 2.2.2 Reverse Logistics in Green Supply-chain 35 2.3 Manufacturing Systems of Reconfigurable and Changeable 42 2.4 Flexible Manufacturing Systems 47 Chapter 3 Research Methods 51 3.1 Multi-Objective Decision Making 51 3.2 De Novo Programming and System Design Optimal 56 3.3 Formulation of Final Testing Capacity Planning Optimization 58 3.4 Changeable Space Programming Formulation 60 Chapter 4 An Empirical Study 67 4.1 Industrial personal computer industry in Green supply-chain of changeable parameters 67 4.2 Industrial personal computer industry 68 4.2.1 Industrial Characteristics 72 4.3 The Green Supply-chain of Industrial Personal Computer Manufacturing Process 76 4.4 Changeable Parameters of MOP 89 4.4.1 Changeable Capacity of MOP 89 4.4.2 Changeable Objective Coefficients of MOP 92 4.4.3 Changeable Technological Coefficients of MOP 95 Chapter 5 Discussion 99 5.1 Managerial Implications 106 5.2 De Novo programming differs from Changeable Space 107 Chapter 6 Conclusion 109 Reference 111

    Abdallah, T., Farhat, A., Diabat, A., & Kennedy, S. (2012). Green supply chains with carbon trading and environmental sourcing: Formulation and life cycle assessment. Applied Mathematical Modelling, 36(9), 4271-4285.
    Agi, M. A., & Nishant, R. (2017). Understanding influential factors on implementing green supply chain management practices: An interpretive structural modelling analysis. J Environ Manage, 188, 351-363.
    Agrawal, S., Singh, R. K., & Murtaza, Q. (2015). A literature review and perspectives in reverse logistics. Resources, conservation and recycling, 97, 76-92.
    Al-Salem, M., Diabat, A., Dalalah, D., & Alrefaei, M. (2016). A closed-loop supply chain management problem: Reformulation and piecewise linearization. Journal of manufacturing systems, 40, 1-8.
    Al‐Mashari, M., & Zairi, M. (2000). Supply‐chain re‐engineering using enterprise resource planning (ERP) systems: an analysis of a SAP R/3 implementation case. International Journal of Physical Distribution & Logistics Management, 30(3/4), 296-313.
    AlGeddawy, T., & ElMaraghy, H. A. (2009). Changeability effect on manufacturing systems design. Changeable and Reconfigurable Manufacturing Systems, 267-283.
    Aloini, D., Dulmin, R., Mininno, V., & Ponticelli, S. (2015). Key antecedents and practices for supply chain management adoption in project contexts. International Journal of Project Management, 33(6), 1301-1316.
    Amini, M. M., Retzlaff-Roberts, D., & Bienstock, C. C. (2005). Designing a reverse logistics operation for short cycle time repair services. International Journal of Production Economics, 96(3), 367-380.
    Andersen, M., & Skjoett-Larsen, T. (2009). Corporate social responsibility in global supply chains. supply chain management: an international journal, 14(2), 75-86.
    Appolloni, A., Sun, H., Jia, F., & Li, X. (2014). Green Procurement in the private sector: a state of the art review between 1996 and 2013. Journal of Cleaner Production, 85, 122-133.
    Ardente, F., & Mathieux, F. (2014). Identification and assessment of product's measures to improve resource efficiency: the case-study of an Energy using Product. Journal of Cleaner Production, 83, 126-141.
    Awasthi, A., & Kannan, G. (2016). Green supplier development program selection using NGT and VIKOR under fuzzy environment. Computers & Industrial Engineering, 91, 100-108.
    Azab, A., ElMaraghy, H., Nyhuis, P., Pachow-Frauenhofer, J., & Schmidt, M. (2013). Mechanics of change: A framework to reconfigure manufacturing systems. CIRP Journal of Manufacturing Science and Technology, 6(2), 110-119.
    Azevedo, S. G., Carvalho, H., & Cruz Machado, V. (2011). The influence of green practices on supply chain performance: A case study approach. Transportation Research Part E: Logistics and Transportation Review, 47(6), 850-871.
    Babic, Z., & Pavic, I. (1996). Multicriterial production planning by De Novo programming approach. International Journal of Production Economics, 43(1), 59-66.
    Bakal, I. S., & Akcali, E. (2006). Effects of random yield in remanufacturing with price‐sensitive supply and demand. Production and operations management, 15(3), 407-420.
    Ballou, R. H., Gilbert, S. M., & Mukherjee, A. (2000). New Managerial Challenges from Supply chain Opportunities. Industrial Marketing Management, 29(1), 7-18.
    Bastas, A., & Liyanage, K. (2018). Sustainable supply chain quality management: A systematic review. Journal of Cleaner Production, 181, 726-744.
    Batarfi, R., Jaber, M. Y., & Aljazzar, S. M. (2017). A profit maximization for a reverse logistics dual-channel supply chain with a return policy. Computers & Industrial Engineering, 106, 58-82.
    Beamon, B. M. (1999). Designing the green supply chain. Logistics information management, 12(4), 332-342.
    Bessant, J. (1993). The lessons of failure: learning to manage new manufacturing technology. International Journal of Technology Management, 8(3-5), 197-215.
    Bi, Z. M., Lang, S. Y., Shen, W., & Wang, L. (2008). Reconfigurable manufacturing systems: the state of the art. International Journal of Production Research, 46(4), 967-992.
    Carter, C. R., & Ellram, L. M. (1998). Reverse logistics: a review of the literature and framework for future investigation. Journal of business logistics, 19(1), 85.
    Cellura, M., La Rocca, V., Longo, S., & Mistretta, M. (2014). Energy and environmental impacts of energy related products (ErP): a case study of biomass-fuelled systems. Journal of Cleaner Production, 85, 359-370.
    Chan, S., & Yu, P. (1985). Stable habitual domains: existence and implications. Journal of mathematical analysis and applications, 110(2), 469-482.
    Charnes, A., & Cooper, W. W. (1957). Management models and industrial applications of linear programming. Management Science, 4(1), 38-91.
    Chen, C.-C., Shih, H.-S., Shyur, H.-J., & Wu, K.-S. (2012). A business strategy selection of green supply chain management via an analytic network process. Computers & Mathematics with Applications, 64(8), 2544-2557.
    Chen, L., Olhager, J., & Tang, O. (2014). Manufacturing facility location and sustainability: A literature review and research agenda. International Journal of Production Economics, 149, 154-163.
    Chianglin, C. Y., Lai, T. C., & Yu, P. L. (2007). Linear Programming Models With Changeable Parameters—Theoretical Analysis On" Taking Loss At The Ordering Time And Making Profit At The Delivery Time". International Journal of Information Technology & Decision Making, 6(04), 577-598.
    Chin, T. A., Tat, H. H., & Sulaiman, Z. (2015). Green supply chain management, Environmental Collaboration and Sustainability Performance. Procedia CIRP, 26, 695-699.
    Chopra, S., & Meindl, P. (2007). supply chain management. Strategy, planning & operation. Das summa summarum des management, 265-275.
    Choudhary, A., Sarkar, S., Settur, S., & Tiwari, M. K. (2015). A carbon market sensitive optimization model for integrated forward–reverse logistics. International Journal of Production Economics, 164, 433-444.
    Cohen, M. (1988). Replace, rebuild or remanufacture. Equipment Management, 16(1), 22-26.
    Cordella, M., Sanfelix, J., & Alfieri, F. (2018). Development of an Approach for Assessing the Reparability and Upgradability of Energy-related Products. Procedia CIRP, 69, 888-892.
    Dai, R., Zhang, J., & Tang, W. (2017). Cartelization or Cost-sharing? Comparison of cooperation modes in a green supply chain. Journal of Cleaner Production, 156, 159-173.
    Datta, D. K., & Yu, P. L. (1991). Corporate acquisitions: The merging of habitual domains. Human Systems Management, 10(1), 47-60.
    Daugherty, P. J., Richey, R. G., Genchev, S. E., & Chen, H. (2005). Reverse logistics: superior performance through focused resource commitments to information technology. Transportation Research Part E: Logistics and Transportation Review, 41(2), 77-92.
    De Boysère, J., & Beard, A. (2006). Halogen-free laminates: worldwide trends, driving forces and current status. Circuit World, 32(2), 8-11.
    Dhull, S., & Narwal, M. (2016). Drivers and barriers in green supply chain management adaptation: A state-of-art review. Uncertain supply chain management, 4(1), 61-76.
    Diabat, A., & Govindan, K. (2011). An analysis of the drivers affecting the implementation of green supply chain management. Resources, Conservation and Recycling, 55(6), 659-667.
    Diabat, A., Khodaverdi, R., & Olfat, L. (2013). An exploration of green supplychain practices and performances in an automotive industry. The International Journal of Advanced Manufacturing Technology, 68(1-4), 949-961.
    Dou, Y., Zhu, Q., & Sarkis, J. (2014). Evaluating green supplier development programs with a grey-analytical network process-based methodology. European Journal of Operational Research, 233(2), 420-431.
    Dubey, R., Gunasekaran, A., Papadopoulos, T., & Childe, S. J. (2015). Green supply chain management enablers: Mixed methods research. Sustainable Production and Consumption, 4, 72-88.
    Eguía, I., Villa, G., & Lozano, S. (2017). Efficiency Assessment of Reconfigurable Manufacturing Systems. Procedia Manufacturing, 11, 1027-1034.
    El-Tamimi, A. M., Abidi, M. H., Mian, S. H., & Aalam, J. (2012). Analysis of performance measures of flexible manufacturing system. Journal of King Saud University-Engineering Sciences, 24(2), 115-129.
    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.
    ElMaraghy, H., Azab, A., Schuh, G., & Pulz, C. (2009). Managing variations in products, processes and manufacturing systems. CIRP Annals, 58(1), 441-446.
    ElMaraghy, H. A. (2005). Flexible and reconfigurable manufacturing systems paradigms. International journal of flexible manufacturing systems, 17(4), 261-276.
    ElMaraghy, H. A. (2009). Changing and evolving products and systems–models and enablers. In Changeable and Reconfigurable Manufacturing Systems, 25-45.
    Eltayeb, T. K., Zailani, S., & Ramayah, T. (2011). Green supply chain initiatives among certified companies in Malaysia and environmental sustainability: Investigating the outcomes. Resources, conservation and recycling, 55(5), 495-506.
    Ervural, B. Ç., Ervural, B., & Kabak, Ö. (2016). A Group Decision Making Approach for the Evaluation of Flexible Manufacturing Systems. IFAC-PapersOnLine, 49(12), 1329-1334.
    Fahimnia, B., Sarkis, J., & Davarzani, H. (2015). Green supply chain management: A review and bibliometric analysis. International Journal of Production Economics, 162, 101-114.
    Fargnoli, M., & Kimura, F. (2007). The Optimization of the Design Process for an Effective Use in Eco-Design. In Advances in Life Cycle Engineering for Sustainable Manufacturing Businesses, 59-64.
    Filip, F. G., & Duta, L. (2015). Decision Support Systems in Reverse supplychain management. Procedia Economics and Finance, 22, 154-159.
    Fleischmann, M., Beullens, P., BLOEMHOF‐RUWAARD, J. M., & Wassenhove, L. N. (2001). The impact of product recovery on logistics network design. Production and operations management, 10(2), 156-173.
    Fleischmann, M., Bloemhof-Ruwaard, J., Dekker, R., Van der Laan, E., van Nunen, J., & Van Wassenhove, L. (1997). Quantitative models for reverse logistics. European Journal of Operational Research, 103, 1-17.
    Fleischmann, M., Krikke, H. R., Dekker, R., & Flapper, S. D. P. (2000). A characterisation of logistics networks for product recovery. Omega, 28(6), 653-666.
    Foerstl, K., Schleper, M. C., & Henke, M. (2017). Purchasing and supply management: From efficiency to effectiveness in an integrated supply chain. Journal of Purchasing and Supply Management, 23(4), 223-228.
    Francalanza, E., Borg, J. C., & Constantinescu, C. L. (2012). A Case for Assisting ‘Product Family’ Manufacturing System Designers. Procedia CIRP, 3, 376-381.
    Friesz, T. L., Tourreilles, F. A., Han, A. F.-W., & Fernandez, J. E. (1980). Comparison of multicriteria optimization methods in transport project evaluation (abridgement). Transportation Research Record(751), 38-41.
    Fu, X., Zhu, Q., & Sarkis, J. (2012). Evaluating green supplier development programs at a telecommunications systems provider. International Journal of Production Economics, 140(1), 357-367.
    Genovese, A., Acquaye, A. A., Figueroa, A., & Koh, S. C. L. (2017). Sustainable supply chain management and the transition towards a circular economy: Evidence and some applications. Omega, 66, 344-357.
    Giuliano, A. E., & Eilber, F. R. (1985). The rationale for planned reoperation after unplanned total excision of soft-tissue sarcomas. Journal of Clinical Oncology, 3(10), 1344-1348.
    Govindan, K., Kaliyan, M., Kannan, D., & Haq, A. N. (2014). Barriers analysis for green supply chain management implementation in Indian industries using analytic hierarchy process. International Journal of Production Economics, 147, 555-568.
    Govindan, K., Khodaverdi, R., & Vafadarnikjoo, A. (2015). Intuitionistic fuzzy based DEMATEL method for developing green practices and performances in a green supply chain. Expert Systems with Applications, 42(20), 7207-7220.
    Govindan, K., Soleimani, H., & Kannan, D. (2015). Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future. European Journal of Operational Research, 240(3), 603-626.
    Gualandris, J., & Kalchschmidt, M. (2014). Customer pressure and innovativeness: Their role in sustainable supply chain management. Journal of Purchasing and Supply Management, 20(2), 92-103.
    Guo, J.-J., & Tsai, S.-B. (2015). Discussing and evaluating green supply chain suppliers: a case study of the printed circuit board industry in China. South African Journal of Industrial Engineering, 26(2), 56-67.
    Han, X., Wu, H., Yang, Q., & Shang, J. (2016). Reverse channel selection under remanufacturing risks: Balancing profitability and robustness. International Journal of Production Economics, 182, 63-72.
    Hees, A., & Reinhart, G. (2015). Approach for Production Planning in Reconfigurable Manufacturing Systems. Procedia CIRP, 33, 70-75.
    Hervani, A. A., Helms, M. M., & Sarkis, J. (2005). Performance measurement for green supply chain management. Benchmarking: An international journal, 12(4), 330-353.
    Hoshino, T., Yura, K., & Hitomi, K. (1995). Optimization analysis for recycle-oriented manufacturing systems. International journal of production research, 33(8), 2069-2078.
    Hsu, C.-W., & Hu, A. H. (2009). Applying hazardous substance management to supplier selection using analytic network process. Journal of Cleaner Production, 17(2), 255-264.
    Hu, T.-L., Sheu, J.-B., & Huang, K.-H. (2002). A reverse logistics cost minimization model for the treatment of hazardous wastes. Transportation Research Part E: Logistics and Transportation Review, 38(6), 457-473.
    Hu, Z., Rao, C., Zheng, Y., & Huang, D. (2015). Optimization decision of supplier selection in green procurement under the mode of low carbon economy. International Journal of Computational Intelligence Systems, 8(3), 407-421.
    Huang, H.-S., Larbani, M., & Yu, P.-L. (2012). Quantification and applications of identification spheres. Human Systems Management, 31(2), 97-109.
    Huang, J.-J., & Tzeng, G.-H. (2014). New thinking of multi-objective programming with changeable space–in search of excellence. Technological and Economic Development of Economy, 20(2), 254-273.
    Huettemann, G., Gaffry, C., & Schmitt, R. H. (2016). Adaptation of Reconfigurable Manufacturing Systems for Industrial Assembly – Review of Flexibility Paradigms, Concepts, and Outlook. Procedia CIRP, 52, 112-117.
    Hwang, C.-L., Paidy, S. R., Yoon, K., & Masud, A. S. M. (1980). Mathematical programming with multiple objectives: A tutorial. Computers & Operations Research, 7(1), 5-31.
    Hwang, C.-L., & Yoon, K. (2012). Multiple attribute decision making: methods and applications a state-of-the-art survey (Vol. 186): Springer Science & Business Media.
    Islam, M. T., & Huda, N. (2018). Reverse logistics and closed-loop supply chain of Waste Electrical and Electronic Equipment (WEEE)/E-waste: A comprehensive literature review. Resources, conservation and recycling, 137, 48-75.
    Jayaraman, V., Patterson, R. A., & Rolland, E. (2003). The design of reverse distribution networks: Models and solution procedures. European Journal of Operational Research, 150(1), 128-149.
    Johnson, P. F. (1998). Managing value in reverse logistics systems. Transportation Research Part E: Logistics and Transportation Review, 34(3), 217-227.
    Jones, T. O., & Sasser, W. E. (1995). Why satisfied customers defect. Harvard business review, 73(6), 88-198.
    Kafa, N., Hani, Y., & El Mhamedi, A. (2013). Sustainability Performance Measurement for Green supply chain management. IFAC Proceedings Volumes, 46(24), 71-78.
    Kandananond, K. (2014). A Roadmap to Green Supply chain System through Enterprise Resource Planning (ERP) Implementation. Procedia Engineering, 69, 377-382.
    Kannan, D., Khodaverdi, R., Olfat, L., Jafarian, A., & Diabat, A. (2013). Integrated fuzzy multi criteria decision making method and multi-objective programming approach for supplier selection and order allocation in a green supply chain. Journal of Cleaner Production, 47, 355-367.
    Khorramshahgol, R., & Steiner, H. M. (1988). Resource analysis in project evaluation: a multicriteria approach. Journal of the Operational Research Society, 795-803.
    Kim, K., Song, I., Kim, J., & Jeong, B. (2006). Supply planning model for remanufacturing system in reverse logistics environment. Computers & Industrial Engineering, 51(2), 279-287.
    Koh, S. C. L., Gunasekaran, A., & Tseng, C. S. (2012). Cross-tier ripple and indirect effects of directives WEEE and RoHS on greening a supply chain. International Journal of Production Economics, 140(1), 305-317.
    Koren, Y., Heisel, U., Jovane, F., Moriwaki, T., Pritschow, G., Ulsoy, G., & Van Brussel, H. (1999). Reconfigurable manufacturing systems. CIRP annals, 48(2), 527-540.
    Koren, Y., & Shpitalni, M. (2010). Design of reconfigurable manufacturing systems. Journal of manufacturing systems, 29(4), 130-141.
    Krishnan, M., Chinnusamy, T., & Karthikeyan, T. (2012). Performance study of flexible manufacturing system scheduling using dispatching rules in dynamic environment. Procedia engineering, 38, 2793-2798.
    Kuhn, H. W., & Tucker, A. W. (1951). Linear programming and the theory of games. Activity analysis of production and allocation, 13, 317-335.
    Kusi-Sarpong, S., Bai, C., Sarkis, J., & Wang, X. (2015). Green supply chain practices evaluation in the mining industry using a joint rough sets and fuzzy TOPSIS methodology. Resources Policy, 46, 86-100.
    Laari, S., Töyli, J., Solakivi, T., & Ojala, L. (2016). Firm performance and customer-driven green supply chain management. Journal of Cleaner Production, 112, 1960-1970.
    Lafou, M., Mathieu, L., Pois, S., & Alochet, M. (2016). Manufacturing System Flexibility: Product Flexibility Assessment. Procedia CIRP, 41, 99-104.
    Lambert, D. M., & Cooper, M. C. (2000). Issues in supply chain management. Industrial Marketing Management, 29(1), 65-83.
    Landherr, M., & Westkämper, E. (2014). Integrated product and assembly configuration using systematic modularization and flexible integration. Procedia CIRP, 17, 260-265.
    Larbani, M., & Yu, P. L. (2009). Two-person second-order games, Part 2: Restructuring operations to reach a win-win profile. Journal of Optimization Theory and Applications, 141(3), 641-659.
    Lee, C. K. M., & Lam, J. S. L. (2012). Managing reverse logistics to enhance sustainability of industrial marketing. Industrial Marketing Management, 41(4), 589-598.
    Leinbach, T. R., & Cromley, R. G. (1983). A goal programming approach to public investment decisions: a case study of rural roads in Indonesia. Socio-Economic Planning Sciences, 17(1), 1-10.
    Li, H.-L., & Yu, P.-L. (1994). Optimal competence set expansion using deduction graphs. Journal of Optimization Theory and Applications, 80(1), 75-91.
    Liou, J. J. H., & Tzeng, G.-H. (2014). Comments on “Multiple criteria decision making (MCDM) methods in economics: an overview”. Technological and Economic Development of Economy, 18(4), 672-695.
    Lu, L. X., & Swaminathan, J. M. (2015). Supply chain management. In J. D. Wright (Ed.), International Encyclopedia of the Social & Behavioral Sciences (Second Edition), 709-713.
    Mahmood, K., Karaulova, T., Otto, T., & Shevtshenko, E. (2017). Performance Analysis of a Flexible Manufacturing System (FMS). Procedia CIRP, 63, 424-429.
    Massam, B. H. (1988). Multi-criteria decision making (MCDM) techniques in planning. Progress in planning, 30, 1-84.
    Mehrabi, M. G., Ulsoy, A. G., Koren, Y., & Heytler, P. (2002). Trends and perspectives in flexible and reconfigurable manufacturing systems. Journal of Intelligent manufacturing, 13(2), 135-146.
    Modak, N. M., Modak, N., Panda, S., & Sana, S. S. (2018). Analyzing structure of two-echelon closed-loop supply chain for pricing, quality and recycling management. Journal of Cleaner Production, 171, 512-528.
    Nagurney, A., & Toyasaki, F. (2003). Supply chain supernetworks and environmental criteria. Transportation Research Part D: Transport and Environment, 8(3), 185-213.
    Nagurney, A., & Toyasaki, F. (2005). Reverse supply chain management and electronic waste recycling: a multitiered network equilibrium framework for e-cycling. Transportation Research Part E: Logistics and Transportation Review, 41(1), 1-28.
    Ongondo, F. O., Williams, I. D., & Cherrett, T. J. (2011). How are WEEE doing? A global review of the management of electrical and electronic wastes. Waste Management, 31(4), 714-730.
    Östlin, J., Sundin, E., & Björkman, M. (2008). Importance of closed-loop supply chain relationships for product remanufacturing. International Journal of Production Economics, 115(2), 336-348.
    Othman, A. A., Kaliani Sundram, V. P., Mohamed Sayuti, N., & Shamsul Bahrin, A. (2016). The Relationship between Supply chain Integration, Just-In-Time and Logistics Performance: A Supplier’s Perspective on the Automotive Industry in Malaysia. International journal of supply chain management, 5(1), 44-51.
    Pecht, M., Shibutani, T., & Wu, L. (2016). A reliability assessment guide for the transition planning to lead-free electronics for companies whose products are RoHS exempted or excluded. Microelectronics Reliability, 62, 113-123.
    Po, L. Y., & Zhang, D. (1990). A foundation for competence set analysis. Mathematical Social Sciences, 20(3), 251-299.
    Prajogo, D., & Olhager, J. (2012). Supply chain integration and performance: The effects of long-term relationships, information technology and sharing, and logistics integration. International Journal of Production Economics, 135(1), 514-522.
    Rösiö, C., & Säfsten, K. (2013). Reconfigurable production system design–theoretical and practical challenges. Journal of Manufacturing Technology Management, 24(7), 998-1018.
    Rau, K. R., & Chetty, O. K. (1996). Production planning of FMS under tool magazine constraints: a dynamic programming approach. The International Journal of Advanced Manufacturing Technology, 11(5), 366-371.
    Rezaei, J. (2015). A Systematic Review of Multi-criteria Decision-making Applications in Reverse Logistics. Transportation Research Procedia, 10, 766-776.
    Roehrich, J. K., Hoejmose, S. U., & Overland, V. (2017). Driving green supply chain management performance through supplier selection and value internalisation: A self-determination theory perspective. International Journal of Operations & Production Management, 37(4), 489-509.
    Sabri, E. H., & Beamon, B. M. (2000). A multi-objective approach to simultaneous strategic and operational planning in supply chain design. Omega, 28(5), 581-598.
    Sarkis, J. (2003). A strategic decision framework for green supply chain management. Journal of Cleaner Production, 11(4), 397-409.
    Sazvar, Z., Mirzapour Al-e-Hashem, S., Baboli, A., & Jokar, M. A. (2014). A bi-objective stochastic programming model for a centralized green supply chain with deteriorating products. International Journal of Production Economics, 150, 140-154.
    Schuh, G., Lenders, M., Nussbaum, C., & Kupke, D. (2009). Design for changeability. Changeable and Reconfigurable Manufacturing Systems, 251-266.
    Sethi, A. K., & Sethi, S. P. (1990). Flexibility in manufacturing: a survey. International journal of flexible manufacturing systems, 2(4), 289-328.
    Seuring, S., & Müller, M. (2008). From a literature review to a conceptual framework for sustainable supply chain management. Journal of Cleaner Production, 16(15), 1699-1710.
    Shakerian, H., Dehnavi, H. D., & Shateri, F. (2016). A Framework for the Implementation of Knowledge Management in supply chain management. Procedia - Social and Behavioral Sciences, 230, 176-183.
    Sheu, J.-B., Chou, Y.-H., & Hu, C.-C. (2005). An integrated logistics operational model for green supply chain management. Transportation Research Part E: Logistics and Transportation Review, 41(4), 287-313.
    Shi, Y. (1995). Studies on optimum-path ratios in multicriteria De Novo programming problems. Computers & Mathematics with Applications, 29(5), 43-50.
    Sobotka, A., Sagan, J., Baranowska, M., & Mazur, E. (2017). Management of reverse logistics supply chains in construction projects. Procedia Engineering, 208, 151-159.
    Srivastava, S. K. (2007). Green supply chain management: A state-of-the-art literature review. International journal of management reviews, 9(1), 53-80.
    Sucky, E. (2005). Inventory management in supply chains: A bargaining problem. International Journal of Production Economics, 93, 253-262.
    Tabone, M. D., Cregg, J. J., Beckman, E. J., & Landis, A. E. (2010). Sustainability metrics: life cycle assessment and green design in polymers. Environmental Science & Technology, 44(21), 8264-8269.
    Tan, K. C. (2001). A framework of supply chain management literature. European Journal of Purchasing & Supply Management, 7(1), 39-48.
    Teng, J.-Y., & Tzeng, G.-H. (1996). A multiobjective programming approach for selecting non-independent transportation investment alternatives. Transportation Research Part B: Methodological, 30(4), 291-307.
    Terkaj, W., Tolio, T., & Valente, A. (2009). Focused flexibility in production systems. Changeable and Reconfigurable Manufacturing Systems, 47-66.
    Toffel, M. W. (2004). Strategie management of product recovery. California management review, 46(2), 120-141.
    Tsai, S.-B., Chien, M.-F., Xue, Y., Li, L., Jiang, X., Chen, Q., . . . Wang, L. (2015). Using the fuzzy DEMATEL to determine environmental performance: a case of printed circuit board industry in Taiwan. PloS one, 10(6), 129-153.
    Tsai, W.-H., & Hung, S.-J. (2009). Treatment and recycling system optimisation with activity-based costing in WEEE reverse logistics management: an environmental supply chain perspective. International journal of production research, 47(19), 5391-5420.
    Tseng, S. C., & Hung, S. W. (2014). A strategic decision-making model considering the social costs of carbon dioxide emissions for sustainable supply chain management. J Environ Manage, 133, 315-322.
    Tzeng, G.-H. (2003). Multiple objective decision making in past, present, and future. In Multi-Objective Programming and Goal Programming, 65-76.
    Vachon, S., & Klassen, R. D. (2008). Environmental management and manufacturing performance: The role of collaboration in the supply chain. International Journal of Production Economics, 111(2), 299-315.
    Vural, C. A. (2015). Sustainable Demand Chain Management: An Alternative Perspective for Sustainability in the Supply chain. Procedia - Social and Behavioral Sciences, 207, 262-273.
    Walker, H., Di Sisto, L., & McBain, D. (2008). Drivers and barriers to environmental supply chain management practices: Lessons from the public and private sectors. Journal of Purchasing and Supply Management, 14(1), 69-85.
    Wang, W., & Koren, Y. (2012). Scalability planning for reconfigurable manufacturing systems. Journal of manufacturing systems, 31(2), 83-91.
    Wang, Y.-C., Chen, T., Chiang, H., & Pan, H.-C. (2016). A simulation analysis of part launching and order collection decisions for a flexible manufacturing system. Simulation Modelling Practice and Theory, 69, 80-91.
    Wang, Q., Zhang, S., & Zhao, X. (2018). Effects of customer and cost drivers on green supply chain management practices and environmental performance. Journal of Cleaner Production, 189, 673-682.
    Westkämper, E. (2007). Digital Manufacturing in the global Era. Digital Enterprise Technology, 3-14.
    Wibowo, M. A., Elizar, Sholeh, M. N., & Adji, H. S. (2017). supply chain management Strategy for Recycled Materials to Support Sustainable Construction. Procedia Engineering, 171, 185-190.
    Wiendahl, H.-P., ElMaraghy, H. A., Nyhuis, P., Zäh, M. F., Wiendahl, H.-H., Duffie, N., & Brieke, M. (2007). Changeable manufacturing-classification, design and operation. CIRP Annals, 56(2), 783-809.
    Wiendahl, H. P., ElMaraghy, H. A., Nyhuis, P., Zäh, M. F., Wiendahl, H. H., Duffie, N., & Brieke, M. (2007). Changeable Manufacturing - Classification, Design and Operation. CIRP Annals, 56(2), 783-809.
    Wolf, J. (2014). The relationship between sustainable supply chain management, stakeholder pressure and corporate sustainability performance. Journal of business ethics, 119(3), 317-328.
    Won, J. (1990). Multicriteria evaluation approaches to urban transportation projects. Urban Studies, 27(1), 119-138.
    Wu, H.-H., & Chang, S.-Y. (2015). A case study of using DEMATEL method to identify critical factors in green supply chain management. Applied Mathematics and Computation, 256, 394-403.
    Wysk, R. A., Yang, N.-S., & Joshi, S. (1991). Detection of deadlocks in flexible manufacturing cells. IEEE Transactions on robotics and automation, 7(6), 853-859.
    Yildirim, M. B., Cakar, T., Doguc, U., & Meza, J. C. (2006). Machine number, priority rule, and due date determination in flexible manufacturing systems using artificial neural networks. Computers & Industrial Engineering, 50(1-2), 185-194.
    Ylä-Mella, J., Poikela, K., Lehtinen, U., Keiski, R. L., & Pongrácz, E. (2014). Implementation of Waste Electrical and Electronic Equipment Directive in Finland: Evaluation of the collection network and challenges of the effective WEEE management. Resources, conservation and recycling, 86, 38-46.
    Yu, F., Yang, Y., & Chang, D. (2018). Carbon footprint based green supplier selection under dynamic environment. Journal of Cleaner Production, 170(Supplement C), 880-889.
    Yu, P.-L. (1973). A class of solutions for group decision problem. Management Science, 19(8), 936-946.
    Yu, P.-L., & Chen, Y.-C. (2010a). Dynamic MCDM, habitual domains and competence set analysis for effective decision making in changeable spaces. In Trends in multiple criteria decision analysis, 1-35.
    Yu, P.-L., & Chen, Y.-C. (2010b). Dynamic multiple criteria decision making in changeable spaces: from habitual domains to innovation dynamics. Annals of Operations Research, 197(1), 201-220.
    Yu, P.-L., & Chen, Y.-C. (2012). Dynamic multiple criteria decision making in changeable spaces: from habitual domains to innovation dynamics. Annals of Operations Research, 197(1), 201-220.
    YU, P.-L., & Chiang, C. (2002). Decision making, habitual domains and information technology. International Journal of Information Technology & Decision Making, 1(01), 5-26.
    Yu, P. L. (1991). Habitual Domains. Operations Research, 39(6), 869-876.
    Yu, P. L., & Larbani, M. (2009). Two-person second-order games, Part 1: formulation and transition anatomy. Journal of Optimization Theory and Applications, 141(3), 619-639.
    Yu, X., Shi, Y., Zhang, L., Nie, G., & Huang, A. (2014). Intelligent Knowledge Beyond Data Mining: Influences of Habitual Domains. CAIS, 34, 53.
    Zeballos, L., Quiroga, O., & Henning, G. P. (2010). A constraint programming model for the scheduling of flexible manufacturing systems with machine and tool limitations. Engineering Applications of Artificial Intelligence, 23(2), 229-248.
    Zeleny, M. (1982). Multi criteria decision making. McGraw-Hills, New York.
    Zeleny, M. (1997a). From Maximization to Optimization: MCDM and the Eight Models of Optimality. In Essays In Decision Making, 107-119.
    Zeleny, M. (1997b). Towards the tradeoffs-free optimality in MCDM. In Multicriteria Analysis, 596-601.
    Zeleny, M. (1998). Multiple criteria decision making: Eight concepts of optimality. Human Systems Management, 17(2), 97-107.
    Zeleny, M. (2005). Human systems management: Integrating knowledge, management and systems: World Scientific.
    ZelenÝ, M. (1990). Optimizing given systems vs. designing optimal systems: The De Novo programming approach. International journal of general systems, 17(4), 295-307.
    Zeleny, M., & Cochrane, J. L. (1982). Multiple criteria decision making (Vol. 25): McGraw-Hill New York.
    Zeydan, M., Çolpan, C., & Çobanoğlu, C. (2011). A combined methodology for supplier selection and performance evaluation. Expert Systems with Applications, 38(3), 2741-2751.
    Zhang, Y. M., Huang, G. H., & He, L. (2011). An inexact reverse logistics model for municipal solid waste management systems. Journal of Environmental Management, 92(3), 522-530.
    Zhao, R., Liu, Y., Zhang, N., & Huang, T. (2017). An optimization model for green supply chain management by using a big data analytic approach. Journal of Cleaner Production, 142, 1085-1097.
    Zhikang, L. (2017). Research on Development Strategy of Automobile Reverse Logistics Based on SWOT Analysis. Procedia Engineering, 174, 324-330.
    Zhu, Q., & Sarkis, J. (2004). Relationships between operational practices and performance among early adopters of green supply chain management practices in Chinese manufacturing enterprises. Journal of operations management, 22(3), 265-289.
    Zhu, Q., Sarkis, J., Cordeiro, J., & Lai, K. (2008). Firm-level correlates of emergent green supply chain management practices in the Chinese context☆. Omega, 36(4), 577-591.
    Zikopoulos, C., & Tagaras, G. (2015). Reverse supply chains: Effects of collection network and returns classification on profitability. European Journal of Operational Research, 246(2), 435-449

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