Optimization of Heterogeneous Last-Mile Delivery of Fresh Products Considering Traffic Congestions and Other Real-World Parameters
Logistics and transport are the core of many industrial and business processes. One of the most promising segments in the field is the optimization of vehicle routes. Scientific effort is focused primarily on algorithms developed in simplified environments and covers a fraction of real industrial ap...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11003084/ |
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| author | Nikica Peric Slaven Begovic Vinko Lesic |
| author_facet | Nikica Peric Slaven Begovic Vinko Lesic |
| author_sort | Nikica Peric |
| collection | DOAJ |
| description | Logistics and transport are the core of many industrial and business processes. One of the most promising segments in the field is the optimization of vehicle routes. Scientific effort is focused primarily on algorithms developed in simplified environments and covers a fraction of real industrial applications due to complex combinatorial algorithms required to be promptly executed. In this paper, a real-world case study in all its complexity is observed and formulated as a real-world vehicle routing problem (VRP). To cope with the complexity computationally, we propose a new procedure based on an adaptive memory metaheuristic combined with local search. The initial solution is obtained with the Clarke-Wright savings algorithm extended here by introducing a dropout factor to include a stochastic attribute. The procedure and corresponding algorithms are tested first on the two existing scientific state-of-art benchmarks and further on the real industrial case study, which considers capacities, time windows, soft time windows, heterogeneous vehicles, dynamic fuel consumption, multi-trip delivery, crew skills, split delivery and, finally, time-dependent routes as the most significant factor. In comparison with the current state-of-the-art algorithms for vehicle routing problem with a large number of constraints, we obtain an average savings of 2.03% in delivery time and 20.98% in total delivery costs. |
| format | Article |
| id | doaj-art-9370324df7c14b6190fc8dcb532f40f7 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-9370324df7c14b6190fc8dcb532f40f72025-08-20T03:54:07ZengIEEEIEEE Access2169-35362025-01-0113871938720710.1109/ACCESS.2025.356957811003084Optimization of Heterogeneous Last-Mile Delivery of Fresh Products Considering Traffic Congestions and Other Real-World ParametersNikica Peric0https://orcid.org/0000-0002-2476-6305Slaven Begovic1https://orcid.org/0000-0002-3583-3489Vinko Lesic2Laboratory for Renewable Energy Systems, Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, CroatiaLaboratory for Renewable Energy Systems, Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, CroatiaLaboratory for Renewable Energy Systems, Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, CroatiaLogistics and transport are the core of many industrial and business processes. One of the most promising segments in the field is the optimization of vehicle routes. Scientific effort is focused primarily on algorithms developed in simplified environments and covers a fraction of real industrial applications due to complex combinatorial algorithms required to be promptly executed. In this paper, a real-world case study in all its complexity is observed and formulated as a real-world vehicle routing problem (VRP). To cope with the complexity computationally, we propose a new procedure based on an adaptive memory metaheuristic combined with local search. The initial solution is obtained with the Clarke-Wright savings algorithm extended here by introducing a dropout factor to include a stochastic attribute. The procedure and corresponding algorithms are tested first on the two existing scientific state-of-art benchmarks and further on the real industrial case study, which considers capacities, time windows, soft time windows, heterogeneous vehicles, dynamic fuel consumption, multi-trip delivery, crew skills, split delivery and, finally, time-dependent routes as the most significant factor. In comparison with the current state-of-the-art algorithms for vehicle routing problem with a large number of constraints, we obtain an average savings of 2.03% in delivery time and 20.98% in total delivery costs.https://ieeexplore.ieee.org/document/11003084/Time dependent VRPreal-world VRPmetaheuristicadaptive memory |
| spellingShingle | Nikica Peric Slaven Begovic Vinko Lesic Optimization of Heterogeneous Last-Mile Delivery of Fresh Products Considering Traffic Congestions and Other Real-World Parameters IEEE Access Time dependent VRP real-world VRP metaheuristic adaptive memory |
| title | Optimization of Heterogeneous Last-Mile Delivery of Fresh Products Considering Traffic Congestions and Other Real-World Parameters |
| title_full | Optimization of Heterogeneous Last-Mile Delivery of Fresh Products Considering Traffic Congestions and Other Real-World Parameters |
| title_fullStr | Optimization of Heterogeneous Last-Mile Delivery of Fresh Products Considering Traffic Congestions and Other Real-World Parameters |
| title_full_unstemmed | Optimization of Heterogeneous Last-Mile Delivery of Fresh Products Considering Traffic Congestions and Other Real-World Parameters |
| title_short | Optimization of Heterogeneous Last-Mile Delivery of Fresh Products Considering Traffic Congestions and Other Real-World Parameters |
| title_sort | optimization of heterogeneous last mile delivery of fresh products considering traffic congestions and other real world parameters |
| topic | Time dependent VRP real-world VRP metaheuristic adaptive memory |
| url | https://ieeexplore.ieee.org/document/11003084/ |
| work_keys_str_mv | AT nikicaperic optimizationofheterogeneouslastmiledeliveryoffreshproductsconsideringtrafficcongestionsandotherrealworldparameters AT slavenbegovic optimizationofheterogeneouslastmiledeliveryoffreshproductsconsideringtrafficcongestionsandotherrealworldparameters AT vinkolesic optimizationofheterogeneouslastmiledeliveryoffreshproductsconsideringtrafficcongestionsandotherrealworldparameters |