A genetic algorithm with two-step rank-based encoding for closed-loop supply chain network design
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Publication:2686735
DOI10.3934/mbe.2022277OpenAlexW4226100288MaRDI QIDQ2686735
Publication date: 28 February 2023
Published in: Mathematical Biosciences and Engineering (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.3934/mbe.2022277
constrained optimizationlarge-scale optimizationgenetic algorithmclosed-loop supply chain network design
Transportation, logistics and supply chain management (90B06) Approximation methods and heuristics in mathematical programming (90C59)
Cites Work
- A social learning particle swarm optimization algorithm for scalable optimization
- A genetic algorithm-based heuristic for the dynamic integrated forward/reverse logistics network for 3PLs
- Planning of complex supply chains: a performance comparison of three meta-heuristic algorithms
- An online-learning-based evolutionary many-objective algorithm
- A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks
- A genetic algorithm for two-stage transportation problem using priority-based encoding
- An adaptive polyploid memetic algorithm for scheduling trucks at a cross-docking terminal
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