Efficient Model for Waste Load and Route Optimization
Urbanization frequently gives rise to substantial environmental issues, namely in waste management and water quality maintenance. Gross Pollutant Traps (GPTs) are essential in urban stormwater management as they effectively capture substantial pollutants before they ente...
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| Format: | Article |
| Language: | English |
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INTI International University
2024
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| Online Access: | http://eprints.intimal.edu.my/1953/ http://eprints.intimal.edu.my/1953/1/495 |
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| author | Achmad, Nopransyah Tri Basuki, Kurniawan Misinem, . Muhammad Izman, Herdiansyah Edi Surya, Negara |
| author_facet | Achmad, Nopransyah Tri Basuki, Kurniawan Misinem, . Muhammad Izman, Herdiansyah Edi Surya, Negara |
| author_sort | Achmad, Nopransyah |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | Urbanization frequently gives rise to substantial environmental issues, namely in waste management and water quality maintenance. Gross Pollutant Traps (GPTs) are essential in urban stormwater management as they effectively capture substantial pollutants before they enterthe centralwater bodies. Nevertheless, the irregular buildup of trash caused by fluctuating rainfall intensity hinders the effective transfer of garbage from GPTs to their ultimate disposal locations. This research presents a holistic approach toenhancing the efficiency of waste transportation by improving route and load planning. The model utilizes machine learning techniques to forecast the quantity of waste collected by GPTs. We have created an optimization algorithm that usesthe forecast outcome from a prior research dataset. This algorithm is designed to efficiently plan the routes and loads for trucks responsible for transporting waste to its final disposal location. The optimization process consideredthe estimated amounts of garbage, the capacities of the vehicles, and the locations of the disposal sitesto reduce transportation expenses and save time. The system adaptively optimized routes using real-time data on the vehicle'sorigin and destination, ensuring effective allocation of resources and prompt garbage removal. Installingthis approach resulted in a substantial decrease in transportation expenses and enhanced compliance with waste pickup timetables. The integration of predictive modelingand route optimization is enhancing urban trash management. Accurate garbage quantity forecasts and optimized transportation logistics can enable municipalities to deploy resources more effectively, decrease operational costs, and improve environmental protection. We chose a subset of 7 days, equivalent to one week, from the projected dataset for our experiment.Subsequently, we conductednumerous trials involving various waste disposalfrequencies. The findings suggest that waste disposalevery four(4) days is the most advantageous approach. Still, itperforms similarlyto waste disposalevery three (3)days and has negligible environmental consequences. Hence, we select to execute the optimal solution for three(3) days, as it provides exceptional performancewhen consideringthe influence of natural pollution |
| first_indexed | 2025-11-14T11:58:11Z |
| format | Article |
| id | intimal-1953 |
| institution | INTI International University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:58:11Z |
| publishDate | 2024 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-19532024-08-02T03:24:56Z http://eprints.intimal.edu.my/1953/ Efficient Model for Waste Load and Route Optimization Achmad, Nopransyah Tri Basuki, Kurniawan Misinem, . Muhammad Izman, Herdiansyah Edi Surya, Negara QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) Urbanization frequently gives rise to substantial environmental issues, namely in waste management and water quality maintenance. Gross Pollutant Traps (GPTs) are essential in urban stormwater management as they effectively capture substantial pollutants before they enterthe centralwater bodies. Nevertheless, the irregular buildup of trash caused by fluctuating rainfall intensity hinders the effective transfer of garbage from GPTs to their ultimate disposal locations. This research presents a holistic approach toenhancing the efficiency of waste transportation by improving route and load planning. The model utilizes machine learning techniques to forecast the quantity of waste collected by GPTs. We have created an optimization algorithm that usesthe forecast outcome from a prior research dataset. This algorithm is designed to efficiently plan the routes and loads for trucks responsible for transporting waste to its final disposal location. The optimization process consideredthe estimated amounts of garbage, the capacities of the vehicles, and the locations of the disposal sitesto reduce transportation expenses and save time. The system adaptively optimized routes using real-time data on the vehicle'sorigin and destination, ensuring effective allocation of resources and prompt garbage removal. Installingthis approach resulted in a substantial decrease in transportation expenses and enhanced compliance with waste pickup timetables. The integration of predictive modelingand route optimization is enhancing urban trash management. Accurate garbage quantity forecasts and optimized transportation logistics can enable municipalities to deploy resources more effectively, decrease operational costs, and improve environmental protection. We chose a subset of 7 days, equivalent to one week, from the projected dataset for our experiment.Subsequently, we conductednumerous trials involving various waste disposalfrequencies. The findings suggest that waste disposalevery four(4) days is the most advantageous approach. Still, itperforms similarlyto waste disposalevery three (3)days and has negligible environmental consequences. Hence, we select to execute the optimal solution for three(3) days, as it provides exceptional performancewhen consideringthe influence of natural pollution INTI International University 2024-07 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/1953/1/495 Achmad, Nopransyah and Tri Basuki, Kurniawan and Misinem, . and Muhammad Izman, Herdiansyah and Edi Surya, Negara (2024) Efficient Model for Waste Load and Route Optimization. Journal of Data Science, 2024 (21). pp. 1-17. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) Achmad, Nopransyah Tri Basuki, Kurniawan Misinem, . Muhammad Izman, Herdiansyah Edi Surya, Negara Efficient Model for Waste Load and Route Optimization |
| title | Efficient Model for Waste Load and Route Optimization |
| title_full | Efficient Model for Waste Load and Route Optimization |
| title_fullStr | Efficient Model for Waste Load and Route Optimization |
| title_full_unstemmed | Efficient Model for Waste Load and Route Optimization |
| title_short | Efficient Model for Waste Load and Route Optimization |
| title_sort | efficient model for waste load and route optimization |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) |
| url | http://eprints.intimal.edu.my/1953/ http://eprints.intimal.edu.my/1953/ http://eprints.intimal.edu.my/1953/1/495 |