Self-learning differential evolution algorithm for scheduling of internal tasks in cross-docking
Soft Computing, ISSN: 1433-7479, Vol: 26, Issue: 21, Page: 11809-11826
2022
- 7Citations
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
A novel self-learning differential evolution (SLDE) algorithm for addressing large-scale internal tasks scheduling problems in cross-docking is proposed herein. The goal is to obtain an optimal schedule for working teams and transferring equipment for handling incoming containers at the inbound area and patient orders at the outbound area to minimise the total tardiness. The proposed SLDE aims to increase the search capability of its original differential evolution (DE). The key concept of SLDE is to allow a DE population to learn the capabilities of different search strategies and automatically adjust itself to potential search strategies. The performance of the proposed algorithms is evaluated on a set of generated data based on a real-case scenario of a medical product distribution centre; subsequently, the performance results are compared with results obtained from other metaheuristics. Numerical results demonstrate that the proposed SLDE outperforms other algorithms in terms of solution quality and convergence behaviour by providing superior solutions using fewer function evaluations.
Bibliographic Details
Springer Science and Business Media LLC
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