Optimizing palm oil biomass collection: genetic algorithm approaches in solving location-routing problem
The transition from traditional fossil fuels to renewable energy sources, such as biofuels derived from palm oil biomass, represents a promising avenue in sustainable energy development. However, managing biomass supply chain (BSC) can be challenging, with biomass collection being particularly compl...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | en |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2025
|
| Online Access: | http://journalarticle.ukm.my/26352/1/Paper_18%20-.pdf http://journalarticle.ukm.my/26352/ https://www.ukm.my/jqma/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1854093992026701824 |
|---|---|
| author | Foo, Fong Yeng Zaitul Marlizawati Zainuddin, Hang, See Pheng |
| author_facet | Foo, Fong Yeng Zaitul Marlizawati Zainuddin, Hang, See Pheng |
| author_sort | Foo, Fong Yeng |
| building | Tun Sri Lanang Library |
| collection | Institutional Repository |
| content_provider | Universiti Kebangsaan Malaysia |
| content_source | UKM Journal Article Repository |
| continent | Asia |
| country | Malaysia |
| description | The transition from traditional fossil fuels to renewable energy sources, such as biofuels derived from palm oil biomass, represents a promising avenue in sustainable energy development. However, managing biomass supply chain (BSC) can be challenging, with biomass collection being particularly complex. Hence, this research delves into the intricacy of a location-routing problem (LRP) within the context of palm oil biomass collection. The study aims to enhance the efficiency of palm oil biomass collection by identifying optimal locations for collection facilities and devising vehicle routing strategies at minimum costs. To achieve these objectives, the research employs genetic algorithm (GA) approaches, incorporating innovative strategies, namely automated mutation operator selection (AMOS) and elite child population (ECP). Three GA variations are proposed to inspect the impacts of strategies in GA solution methods. These approaches are evaluated through computational experiments to measure optimisation quality in solving the LRP within the palm oil BSC. The findings underscore the effectiveness of the proposed methods in addressing the LRP. The proposed solution methods could establish a framework for decision-making processes within the biomass energy industry, particularly concerning facility siting and vehicle routing. |
| format | Article |
| id | my-ukm.journal.26352 |
| institution | Universiti Kebangsaan Malaysia |
| language | en |
| publishDate | 2025 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| record_format | eprints |
| spelling | my-ukm.journal.263522026-01-09T06:51:37Z http://journalarticle.ukm.my/26352/ Optimizing palm oil biomass collection: genetic algorithm approaches in solving location-routing problem Foo, Fong Yeng Zaitul Marlizawati Zainuddin, Hang, See Pheng The transition from traditional fossil fuels to renewable energy sources, such as biofuels derived from palm oil biomass, represents a promising avenue in sustainable energy development. However, managing biomass supply chain (BSC) can be challenging, with biomass collection being particularly complex. Hence, this research delves into the intricacy of a location-routing problem (LRP) within the context of palm oil biomass collection. The study aims to enhance the efficiency of palm oil biomass collection by identifying optimal locations for collection facilities and devising vehicle routing strategies at minimum costs. To achieve these objectives, the research employs genetic algorithm (GA) approaches, incorporating innovative strategies, namely automated mutation operator selection (AMOS) and elite child population (ECP). Three GA variations are proposed to inspect the impacts of strategies in GA solution methods. These approaches are evaluated through computational experiments to measure optimisation quality in solving the LRP within the palm oil BSC. The findings underscore the effectiveness of the proposed methods in addressing the LRP. The proposed solution methods could establish a framework for decision-making processes within the biomass energy industry, particularly concerning facility siting and vehicle routing. Penerbit Universiti Kebangsaan Malaysia 2025-06 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/26352/1/Paper_18%20-.pdf Foo, Fong Yeng and Zaitul Marlizawati Zainuddin, and Hang, See Pheng (2025) Optimizing palm oil biomass collection: genetic algorithm approaches in solving location-routing problem. Journal of Quality Measurement and Analysis, 21 (2). pp. 277-288. ISSN 2600-8602 https://www.ukm.my/jqma/ |
| spellingShingle | Foo, Fong Yeng Zaitul Marlizawati Zainuddin, Hang, See Pheng Optimizing palm oil biomass collection: genetic algorithm approaches in solving location-routing problem |
| title | Optimizing palm oil biomass collection: genetic algorithm approaches in solving location-routing problem |
| title_full | Optimizing palm oil biomass collection: genetic algorithm approaches in solving location-routing problem |
| title_fullStr | Optimizing palm oil biomass collection: genetic algorithm approaches in solving location-routing problem |
| title_full_unstemmed | Optimizing palm oil biomass collection: genetic algorithm approaches in solving location-routing problem |
| title_short | Optimizing palm oil biomass collection: genetic algorithm approaches in solving location-routing problem |
| title_sort | optimizing palm oil biomass collection: genetic algorithm approaches in solving location-routing problem |
| url | http://journalarticle.ukm.my/26352/1/Paper_18%20-.pdf http://journalarticle.ukm.my/26352/ https://www.ukm.my/jqma/ |
| url_provider | http://journalarticle.ukm.my/ |