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A hybrid NSGA-II algorithm for the closed-loop supply chain network design in e-commerce

Shayan Shafiee Moghadam et al.

RAIRO - Operations Research2021https://doi.org/10.1051/ro/2021068article
AJG 1
Weight
0.60

What the paper says

Designing the supply chain network is one of the significant areas in e-commerce business management. This concept plays a crucial role in e-commerce systems. For example, location-inventory-pricing-routing of an e-commerce supply chain is considered a crucial issue in this field. This field established many severe challenges in the modern world, like maintaining the supply chain for returned items, preserving customers’ trust and satisfaction, and developing an applicable supply chain with cost considerations. The research proposes a multi-objective mixed integer nonlinear programming model to design a closed-loop supply chain network based on the e-commerce context. The proposed model incorporates two objectives that optimize the business’s total profits and the customers’ satisfaction. Then, numerous numerical examples are generated and solved using the epsilon constraint method in GAMS optimization software. The validation of the given model has been tested for the large problems via a hybrid two-level non-dominated sort genetic algorithm. Finally, some sensitivity analysis has been performed to provide some managerial insights.

25 citations

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https://doi.org/https://doi.org/10.1051/ro/2021068

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@article{shayan2021,
  title        = {{A hybrid NSGA-II algorithm for the closed-loop supply chain network design in e-commerce}},
  author       = {Shayan Shafiee Moghadam et al.},
  journal      = {RAIRO - Operations Research},
  year         = {2021},
  doi          = {https://doi.org/https://doi.org/10.1051/ro/2021068},
}

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A hybrid NSGA-II algorithm for the closed-loop supply chain network design in e-commerce

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Evidence weight

0.60

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.65 × 0.4 = 0.26
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.