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企业绿色逆向物流决策方法研究
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摘要
随着全球资源稀缺压力的逐步增大,人们的环保意识日渐加强,各国对资源利用的法律约束越来越完善,尤其近年来,生产者责任延伸制度的推行,使作为现代绿色物流与供应链管理重要组成部分的逆向物流成为学术界与企业界共同关注的焦点,大家期待着挖掘到这块“黑暗大陆”的“第三利润源泉”。
     遵循理论研究服务于实践需要的原则,详尽分析了现有的传统逆向物流模式、生产商自营模式、生产商联营模式、外包模式各自的优点、缺点、适用条件和现实应用案例,从经济效益、企业学习与成长、竞争环境、法律约束和社会责任五个维度构建了科学、合理、全面的逆向物流决策指标体系,为企业清晰分析自身条件,做出合理逆向物流决策提供依据。
     对多属性决策方法进行了深入研究,充分认识到单纯的主观赋权方法、客观赋权方法存在众多弊端,简单的和法/乘法组合赋权对于消除主观赋权方法和客观赋权方法的作用并不明显,于是,在充分考虑决策者对主观赋权和客观赋权偏好的前提下,将密切值法与组合赋权思想融合,综合数学、管理学、运筹学、系统论、决策论等学科知识,提出一种多属性决策方法——基于密切值的组合赋权多属性决策方法,并通过算例验证其科学合理性,为解决多属性决策问题提供方法支持。
     对铁矿矿山企业的尾矿处理问题进行调查,尾矿处理问题直接关系到环境保护、企业形象、企业利润和生产者责任,作为矿山企业的决策者,运用什么方法对尾矿处理问题做出合理决策是一个战略性问题。根据文章中对逆向物流决策问题构建的指标体系,运用基于密切值的多属性决策方法,对尾矿处理问题进行实证分析,不仅帮助决策者做出合理决策,也在实践运作中实现了很好的经济效益与社会效益,与此同时,证明了基于密切值的组合赋权多属性决策方法的科学、合理、易用、可执行性。
     以循环经济和可持续发展理论为原则,构建闭合的逆向物流网络,仍然是未来研究的主要趋势,尤其是关于逆向物流管理发展标准化问题、逆向物流体系结构问题、逆向物流供应商的选择问题、决策方法等问题都需要投入更多的精力进行深入研究。
Along with global resources scarce pressure gradual enlargement, people's environmental consciousness strengthens day after day, national laws on the use of resources constraints become more perfect, especially in recent years, extended producer responsibility systems carries out. As Modern Logistics and Supply Chain Management important component, the reverse logistics becomes the focal point of the academic circle and the business community. Everybody is anticipating unearthed this "the dark mainland" of "the third source".
     Follows the principle that theoretical research services in the practice needs, in the detailed analysis of the existing traditional reverse logistics model, self-operation reverse logistics model, joint-operation reverse logistics model, outsourcing reverse logistics model and their advantages, disadvantages, the suitable condition and the reality application case. Considered five dimensions that the economic benefits, enterprise learning and growth, competitive environment, legal restriction, social responsbility have constructed the science, reasonably, comprehensive reverse logistics decision-making indicator system. Provide the basis for the enterprise to analysis their own conditions; make a reseasonable decision-making of reverse logistics.
     Based on the study of numerous scholars achievements in scientific research earnestly, concludes the deep research to the multi-objective decision making method. Understand fully to the pure weight of subjective and objective numerous malpractices, the simple law and / portfolio of combination weight is not obvious to eliminate the weight of subjective and objective. Integrates mathematics, management, operations research, systems theory, decision theory, and other academic knowledge, develop a method of combination weighting in multiple attribute decision making based on association value method. And confirms its scientific rationality through the example, provides the method support for the solution multi-objective decision making question.
     Carries on the investigation to the iron ore mine enterprise's debris processing question, the debris processing question directly relate to environmental protection, the enterprise image, the profit of enterprise and the producer responsibility, as a mining policy makers, the use of any method to deal with problems of tailings Rational decision-making is a strategic issue. According to the reverse logistics decision-making index system, use the method of combination weighting in multiple attribute decision-making based on association value method to have an empirical analysis. Achieve a very good Economic and social benefits, at the same time, it also proves that the method of combination weighting in multiple attribute decision making based on association value method is scientific, rational, easy-to-use, and enforceable.
     Take recycling economy and sustainable development theory as the principle, construct the closed reverse logistics network, is still the main trends in future research, especially on the reverse logistics management of the development standardization issues, reverse logistics system structural problems, reverse logistics supplier's choice, Decision-making methods and other issues need to invest more in-depth study of energy.
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