Abstract
Reverse logistics network design (RLND) effectiveness has an important impact on the effectiveness of the whole supply network coordination. Considering that, in this study, the RLND problem is investigated and a hybrid genetic algorithms and simulated annealing (HGASA) methodology is proposed. This problem is applied to a preceding study which utilized genetic algorithms (GA) for the optimization. HGASA and GA results are tested with Wilcoxon rank-sum test for hundred runs and the results prove the difference between two approaches. Additionally, the averages and the standard deviations support that, the HGASA algorithm increases the probability of obtaining better solutions.
Keywords
Simulated annealing
Wilcoxon signed-rank test
Genetic algorithm
Computer science
Adaptive simulated annealing
Mathematical optimization
Algorithm
Standard deviation
Mathematics
Machine learning
Statistics
Mann–Whitney U test
Subject Areas
Sustainable Supply Chain Management
·Strategy and Management ·Social Sciences
Quality and Supply Management
·Management Information Systems ·Social Sciences
Product Development and Customization
·Management of Technology and Innovation ·Social Sciences
Citations by Year
2018: 1
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