Abstract
In this research, an agro-supply chain in the context of both economic and environmental issues has been investigated. To this end, a bi-objective model is formulated as a mixed-integer linear programming that aims to minimize the total costs and CO2 emissions. It generates the integration between purchasing, transporting, and storing decisions, considering specific characteristics of agro-products such as seasonality, perishability, and uncertainty. This study provides a different set of temperature conditions for preserving products from spoilage. In addition, a robust optimization approach is used to tackle the uncertainty in this paper. Then, \(\varepsilon\)-constraint method is used to convert the bi-objective model to a single one. To solve the problem, Lagrangian relaxation algorithm is applied as an efficient approach giving lower bounds for the original problem and used for estimating upper bounds. At the end, a real case study is presented to give valuable insight via assessing the impacts of uncertainty in system costs.
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Keshavarz-Ghorbani, F., Pasandideh, S.H.R. A Lagrangian relaxation algorithm for optimizing a bi-objective agro-supply chain model considering CO2 emissions. Ann Oper Res 314, 497–527 (2022). https://doi.org/10.1007/s10479-021-03936-1
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DOI: https://doi.org/10.1007/s10479-021-03936-1