超越绿色边界:供应链中可持续运输的创新模式

Sifaoui Thiziri, Méziane Aider
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引用次数: 0

摘要

考虑到近年来运输活动的大幅增长,而且预计这种增长还将继续,现代要求必须建立可持续的运输系统。企业正面临着建立符合绿色原则的系统运输模型的挑战。可持续交通依赖于不同利益相关者的参与,尤其是科学研究的参与。有鉴于此,要保持可持续运输的质量,就必须对一项创新研究进行深入调查,该研究的重点是在绿色供应链的预算限制和安全措施范围内,针对多阶段、多目标、多产品运输挑战建立不确定区间编程模型。人类语言通常包含不完善或未知的信息,本质上缺乏确定性;要精确描述现有状态或未来结果往往难以实现。在概率论中,充分的历史信息对估计概率分布至关重要;而在模糊理论中,确定可靠的成员函数具有挑战性;因此,对每个条件发生的可信度的估计往往犹豫不决。针对这种不确定性,不确定区间理论被证明是非常有价值的。鉴于上述考虑,指定问题的要素被视为不确定区间。为了处理这种不确定性,我们融合了区间理论和不确定编程的方法,建立了两个不同的模型:期望值模型和机会约束模型。然后,利用加权求和法、模糊程序设计和目标程序设计来制定和求解等效的确定性模型。之后,利用一个数值示例来评估模型的性能,并对得到的结果进行比较。最后,本文以敏感性分析作为结束语,并概述了未来的发展方向。
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Beyond green borders: an innovative model for sustainable transportation in supply chains
Modern requirements necessitate the establishment of sustainable transportation systems, considering the substantial growth in transportation activities over recent years, which is expected to continue. Companies are facing the challenge of modeling their system transport to align with green principles. Sustainable transport relied on involving diverse stakeholders, particularly scientific research, in the development of this field. In light of this, maintaining sustainable transport quality involves conducting thorough investigations into an innovative study focusing on an uncertain interval programming model for a multi-stage, multiobjective, multi-product transportation challenge within budget constraints and safety measures in a green supply chain. Human languages often contain imperfect or unknown information, inherently lacking certainty; achieving precision in describing existing states or future outcomes is frequently unattainable. In probability theory, sufficient historical information is crucial for estimating probability distributions; while in fuzzy theory, determining a reliable membership function proves challenging; hence, there is often a hesitant estimation of the degree of belief in the occurrence of each condition. Addressing such uncertainties, the theory of uncertain intervals proves highly valuable. Given these considerations, the elements of the specified problem are recognized as uncertain intervals. To manage this lack of assurance, a fusion of interval theory and methods from uncertain programming is used to formulate two distinct models: an expected value model and a chance-constrained model. The equivalent deterministic models are then formulated and solved utilizing Weighted Sum Method, fuzzy programming, and goal programming. Following this, a numerical example is utilized to assess the model’s performance, and the results obtained are compared. Finally, the document concludes with a sensitivity analysis and outlines future directions
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