A Multi-objective integrated approach to address sustainability in a meat supply chain
Mehdi Najafi and
Hossein Zolfagharinia
Omega, 2024, vol. 124, issue C
Abstract:
Many supply chains have taken action to consider the principles of sustainability in their business over the past two decades. However, addressing sustainability is still a challenge due to imposing some restrictions. This could be more challenging in a food supply chain because of consuming a large amount of natural resources and its connection to public health. Regarding this significance, this research develops a mechanism to integrate all the primary functions of a meat supply chain given three pillars of sustainability: (i) cost, (ii) social aspect, and (iii) environmental aspect. To this end, we develop a three-objective mathematical formulation to integrate supplier selection, production planning, and product distribution in a meat supply chain. This study also identifies criteria representing suppliers’ impact on the environment and utilizes a Stochastic Analytic Hierarchy Process (SAHP) and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) methods to gauge that impact and evaluate suppliers’ scores. Furthermore, a solution method is designed based on the Normalized Normal Constraint method to generate Pareto-optimal solutions. Lastly, we apply the developed mathematical formulation and the solution methodology using a numerical study inspired by a real case in Ontario, Canada. The results demonstrate that the developed solution methodology can generate diverse and practical Pareto-optimal solutions in which social and environmental aspects could be improved significantly by incurring a minor extra cost.
Keywords: Meat supply chain; Sustainability; Multi-objective; Normalized normal constraint; Green supplier selection; Stochastic analytic hierarchy process; TOPSIS (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jomega:v:124:y:2024:i:c:s0305048323001755
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DOI: 10.1016/j.omega.2023.103011
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