Department of Mechanical Engineering, Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram, Chennai, 600127, Tamil Nadu, India.
Departamento de Ingeniería Industrial, Tecnologico de Monterrey, Puebla Campus, 72453, Mexico.
J Environ Manage. 2022 Feb 1;303:114252. doi: 10.1016/j.jenvman.2021.114252. Epub 2021 Dec 8.
Many companies and organizations are pursuing "carbon footprint" projects to estimate their own contribution due to growing concerns about global climate change and carbon emissions. Measures such as carbon taxes are the most powerful means of dealing with the threats of climate change. In recent years, researchers have shown a particular interest in modelling supply chain networks under this scheme. Disorganized disposal of by-products from sugarcane mills is the inspiration of this research. In order to connect the problem with the real world, the proposed sustainable sugarcane supply chain network considers carbon taxes on the emission from industries and during transportation of goods. The presented mixed-integer linear programming modelling is a location-allocation problem and, due to the inherent complexity, it is considered a Non-Polynomial hard (NP-hard) problem. To deal with the model, three superior metaheuristics Genetic Algorithm (GA), Simulated Annealing (SA), Social Engineering Optimizer (SEO) and hybrid methods based on these metaheuristics, namely, Genetic-Simulated Annealing (GASA) and Genetic-Social Engineering Optimizer (GASEO), are employed. The control parameters of the algorithms are tuned using the Taguchi approach. Subsequently, one-way ANOVA is used to elucidate the performance of the proposed algorithms, which compliments the performance of the proposed GASEO.
许多公司和组织都在进行“碳足迹”项目，以估计其自身因日益关注全球气候变化和碳排放而产生的影响。碳税等措施是应对气候变化威胁的最有力手段。近年来，研究人员对该方案下的供应链网络建模表现出了特别的兴趣。甘蔗厂副产品的无序处理激发了这项研究。为了将问题与现实世界联系起来，所提出的可持续甘蔗供应链网络考虑了对工业排放和货物运输过程中的碳税。所提出的混合整数线性规划模型是一个选址-分配问题，由于其固有的复杂性，被认为是一个非多项式难题（NP-hard）。为了解决该模型，采用了三种优越的元启发式算法：遗传算法（GA）、模拟退火算法（SA）、社会工程优化器（SEO）和基于这些元启发式算法的混合方法，即遗传-模拟退火算法（GASA）和遗传-社会工程优化器（GASEO）。使用田口方法调整算法的控制参数。随后，使用单向方差分析来阐明所提出算法的性能，这对所提出的 GASEO 算法的性能进行了补充。
