A robust bi-objective programming approach to environmental closed-loop supply chain network design under uncertainty

Homayouni, Zahra; Pishvaee, Mir Saman

doi:10.1504/ijmor.2020.105857

Cited by 3 publications

(2 citation statements)

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“…To fill this gap, the current study proposes a bi-objective robust optimization model for designing a GCLSC network based on the "Interval + Polyhedral" uncertainty set while considering presorting and heterogeneous transportation system to maximize the profit and minimize the carbon emissions in the entire network. FMIP Darestani and Hemmati [28] MINLP Ghahremani Nahr et al [27] MINLP Homayouni and Pishvaee [29] MILP The proposed study MILP IRO: interval-based robust optimization; SMIP: stochastic integer programming; FMIP: fuzzy mixed-integer programming.…”

Section: Uncertain Multi-objective Clsc Networkmentioning

confidence: 99%

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Robust Optimization for a Bi-Objective Green Closed-Loop Supply Chain with Heterogeneous Transportation System and Presorting Consideration

et al. 2022

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In this study, we propose a robust bi-objective optimization model of the green closed-loop supply chain network considering presorting, a heterogeneous transportation system, and carbon emissions. The proposed model is an uncertain bi-objective mixed-integer linear optimization model that maximizes profit and minimizes carbon emissions by considering uncertain costs, selling price, and carbon emissions. The robust optimization approach is implemented using the combined interval and polyhedral, “Interval+ Polyhedral,” uncertainty set to develop the robust counterpart of the proposed model. Robust Pareto optimal solutions are obtained using a lexicographic weighted Tchebycheff optimization approach of the bi-objective model. Intensive computational experiments are conducted and a robust Pareto optimal front is obtained with a probability guarantee that the constraints containing uncertain parameters are not violated (constraint satisfaction).

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Section: Uncertain Multi-objective Clsc Networkmentioning

confidence: 99%

“…As far as we are aware, few studies [27][28][29] have focused on the RO-based multiobjective optimization of CLSC networks. In these investigations, the implemented uncertainty set is the box uncertainty set with a homogeneous transportation system and no presorting centers in client zones.…”

Section: Introductionmentioning

confidence: 99%

Robust Optimization for a Bi-Objective Green Closed-Loop Supply Chain with Heterogeneous Transportation System and Presorting Consideration

et al. 2022

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Multi-objective spare parts supply network optimization : model formulation and optimal decision-making

Guo

Shi

2021

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)

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Sustainable Design of a Multi-Echelon Closed Loop Supply Chain under Uncertainty for Durable Products

2021

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The increased awareness of environmental sustainability has led to increasing attention to closed loop supply chains (CLSC). The main objective of the CLSC is to capture values from end-of-life (EOL) products in a way that ensures a business to be economically and environmentally sustainable. The challenge is the complexity that occurrs due to closing the loop. At the same time, considering stochastic variables will increase the realism of the obtained results as well as the complexity of the model. This study aims to design a CLSC for durable products using a multistage stochastic model in mixed-integer linear programming (MILP) while considering uncertainty in demand, return rate, and return quality. Demand was described by a normal distribution whereas return rate and return quality were represented by a set of discrete possible outcomes with a specific probability. The objective function was to maximize the profit in a multi-period and multi-echelon CLSC. The multistage stochastic model was tested on a real case study at an air-conditioning company. The computational results identified which facilities should be opened in the reversed loop to optimize profit. The results showed that the CLSC resulted in a reduction in purchasing costs by 52%, an annual savings of 831,150 USD, and extra annual revenue of 5459 USD from selling raw material at a material market. However, the transportation cost increased by an additional annual cost of 6457 USD, and the various recovery processes costs were annually about 152,897 USD. By running the model for nine years, the breakeven point will be after three years of establishing the CLSC and after the annual profit increases by 1.92%. In conclusion, the results of this research provide valuable analysis that may support decision-makers in supply chain planning regarding the feasibility of converting the forward chain to closed loop supply chain for durable products.

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A robust bi-objective programming approach to environmental closed-loop supply chain network design under uncertainty

Cited by 3 publications

References 0 publications

Robust Optimization for a Bi-Objective Green Closed-Loop Supply Chain with Heterogeneous Transportation System and Presorting Consideration

Robust Optimization for a Bi-Objective Green Closed-Loop Supply Chain with Heterogeneous Transportation System and Presorting Consideration

Multi-objective spare parts supply network optimization : model formulation and optimal decision-making

Sustainable Design of a Multi-Echelon Closed Loop Supply Chain under Uncertainty for Durable Products

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