An improved hybrid artificial bee colony algorithm for a multi-supplier closed-loop location inventory problem with customer returns DOI Creative Commons
Hao Guo, Xinfeng Lai, Guo Ju

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(5), P. e0324343 - e0324343

Published: May 22, 2025

Customer returns are an unavoidable and increasingly costly challenge in business operations, especially online marketplaces. This study addresses this issue by introducing a practical multi-supplier closed-loop location-inventory problem (CLLIP) that incorporates customer returns. The objective of the CLLIP is to minimize overall supply chain costs optimizing facility location inventory management strategies. To solve complex problem, improved hybrid artificial bee colony algorithm (IHABC) proposed, which integrates two novel search equations generate candidate solutions during employed onlooker phases, effectively balancing exploration exploitation. performance IHABC evaluated against various variants as well commercial solver Lingo. results numerical experiments demonstrate consistently outperforms competing methods, achieving superior with lowest mean values optimal total cost results, while also requiring less computation time. up 29.97% improvement solution quality over standard ABC algorithm. These findings confirm highly effective efficient tool for solving proposed CLLIP. Furthermore, sensitivity analysis conducted provide actionable insights, enabling managers make informed strategic decisions real-world operations.

Language: Английский

Recent Advancements in Automotive Engineering by Using Evolutionary Algorithms and Nature-Inspired Heuristic Optimization DOI Creative Commons
Morteza Mollajafari, Salman Ebrahimi‐Nejad

IntechOpen eBooks, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 30, 2025

The integration of evolutionary algorithms and nature-inspired heuristic optimization has marked a significant advancement in automotive engineering. These methods, drawing inspiration from biological processes, have been instrumental optimizing complex engineering problems, leading to more efficient, reliable, high-performing designs. application such particularly transformative areas as vehicle routing, predictive maintenance, design optimization. advancements not only signify leap the computational capabilities within industry but also pave way for development autonomous vehicles smart transportation systems. future is poised be heavily influenced by continued evolution these sophisticated algorithms, which promise bring about even groundbreaking innovations field. potential technologies revolutionize immense, they offer solutions some most pressing challenges faced engineers today. As evolve, will undoubtedly unlock new possibilities efficiency, safety, performance, marking era

Language: Английский

Citations

1
An improved hybrid artificial bee colony algorithm for a multi-supplier closed-loop location inventory problem with customer returns DOI Creative Commons
Hao Guo, Xinfeng Lai, Guo Ju

et al.

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(5), P. e0324343 - e0324343

Published: May 22, 2025

Customer returns are an unavoidable and increasingly costly challenge in business operations, especially online marketplaces. This study addresses this issue by introducing a practical multi-supplier closed-loop location-inventory problem (CLLIP) that incorporates customer returns. The objective of the CLLIP is to minimize overall supply chain costs optimizing facility location inventory management strategies. To solve complex problem, improved hybrid artificial bee colony algorithm (IHABC) proposed, which integrates two novel search equations generate candidate solutions during employed onlooker phases, effectively balancing exploration exploitation. performance IHABC evaluated against various variants as well commercial solver Lingo. results numerical experiments demonstrate consistently outperforms competing methods, achieving superior with lowest mean values optimal total cost results, while also requiring less computation time. up 29.97% improvement solution quality over standard ABC algorithm. These findings confirm highly effective efficient tool for solving proposed CLLIP. Furthermore, sensitivity analysis conducted provide actionable insights, enabling managers make informed strategic decisions real-world operations.

Language: Английский

Citations

0