A Robust Green multi-Channel Sustainable Supply Chain based on RFID technology with considering pricing strategy and subsidizing policies

محورهای موضوعی : Mathematical Optimization

Elham Kouchaki Tajani ¹ (Department of Industrial Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran)
Armin Ghane Kanafi ² (Department of Mathematics, Lahijan Branch, Islamic Azad University, Lahijan, Iran)
Maryam Daneshmand-Mehr ³ (Department of Industrial Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran)
Ali-Asghar Hosseinzadeh ⁴ (Department of Mathematics, Lahijan Branch, Islamic Azad University, Lahijan, Iran)

تاریخ دریافت : 1401/02/26 تاریخ پذیرش : 1401/10/12 تاریخ انتشار : 1401/09/10

کلید واژه: Pricing, Government Subsidy, Low-Carbon, Multi-Channel Closed-Loop Supply Chain, Radio Frequency Identification System,

چکیده مقاله :

In recent years, increasing carbon emissions and relatively unfavorable climate change have led to paying attention to the concepts of sustainability as well as the imposition of strict government regulations on manufacturers and service providers. This has caused all parts of society, containing consumers, governments, and companies, to pay greater attention to low-carbon manufacturing in the supply chain (SC). To this end, this paper has designed a sustainable, multi-echelon, multi-product, multi-period, and multi-objective closed-loop supply chain (CLSC) network, with different distribution and collection channels and Radio Frequency Identification (RFID) technology, in addition, in order to produce low-carbon products, low-carbon products and subsidizing policies, and also deals with pricing strategy. This model, at the same time, maximizes profits and the social responsibility of the SC network, while it minimizes the overall delay in delivery time and environmental pollution. To cope with the parameters' uncertainty, a Robust scenario-based Stochastic programming (RSSP) approach has been used, and to solve and validate, the small-size model the Augmented Epsilon Constraint (AEC) method, and to solve large-sized ones, the third edition of the Non-dominated Sorting Genetic Algorithm (NSGA-III) and Multi-Objective Grey Wolf Optimizer Algorithm (MOGWO) are used. According to the computational results, the suggested model can provide efficient decisions and the MOGWO algorithm yields 14.5% improvement in execution time compared to the NSGA-III algorithm. Also suggested model can be a great tool for managers and professionals with a wide range of strategic applications.

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