A Data-Driven Approach to Enhance Resilience and Sustainability in Agricultural Waste Management Network Facility Location

محورهای موضوعی : Supply chain management and logistics

Seyyed Ali Mousavikhorram ¹ , Mohamad Mahdi Nasiri ² , Matineh Ziari ³

1 - School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
2 - School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
3 - School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

تاریخ دریافت : 1403/12/12 تاریخ پذیرش : 1404/07/23

کلید واژه: Waste Management, Sustainability, Resilience, Data-driven Models, Agricultural industry.,

چکیده مقاله :

This study focuses on one of the crucial issues in today’s world named the agricultural waste management by considering the sustainability and resilience features. In this regard, this research proposes a hybrid data-driven decision framework by combining the multiple-attribute decision-making and machine learning methods. In this way, first of all, the major indicators of the considered problem are specified and their weights are calculated using a recently developed method called the stochastic Best-Worst Method (BWM). In the next stage, the potential locations for establishing the collection centers and recycling centers are evaluated using a machine learning approach. Overall, this research has contributed to the literature by addressing the sustainable-resilient agricultural waste management problem using a data-driven model. The results show that land cost, impact on ecological landscape, pollution prevention and control, and capacity expansion capability are the most significant indicators for the research problem. Also, the achieved outputs confirm the effectiveness and robustness of the developed data-driven decision framework. The developed model is based on a hybrid method combining Data Envelopment Analysis (DEA) and the Gradient Boosting algorithm, achieving 98% accuracy in evaluating potential locations.

چکیده انگلیسی:

This study focuses on one of the crucial issues in today’s world named the agricultural waste management by considering the sustainability and resilience features. In this regard, this research proposes a hybrid data-driven decision framework by combining the multiple-attribute decision-making and machine learning methods. In this way, first of all, the major indicators of the considered problem are specified and their weights are calculated using a recently developed method called the stochastic Best-Worst Method (BWM). In the next stage, the potential locations for establishing the collection centers and recycling centers are evaluated using a machine learning approach. Overall, this research has contributed to the literature by addressing the sustainable-resilient agricultural waste management problem using a data-driven model. The results show that land cost, impact on ecological landscape, pollution prevention and control, and capacity expansion capability are the most significant indicators for the research problem. Also, the achieved outputs confirm the effectiveness and robustness of the developed data-driven decision framework. The developed model is based on a hybrid method combining Data Envelopment Analysis (DEA) and the Gradient Boosting algorithm, achieving 98% accuracy in evaluating potential locations.

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