Modular Coordination of Vehicle Routing and Bin Packing Problems in Last Mile Logistics
-д хадгалсан:
| -д хэвлэсэн: | Logistics vol. 9, no. 2 (2025), p. 70-97 |
|---|---|
| Үндсэн зохиолч: | |
| Бусад зохиолчид: | , |
| Хэвлэсэн: |
MDPI AG
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| Нөхцлүүд: | |
| Онлайн хандалт: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Шошгууд: |
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
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MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3223923356 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2305-6290 | ||
| 024 | 7 | |a 10.3390/logistics9020070 |2 doi | |
| 035 | |a 3223923356 | ||
| 045 | 2 | |b d20250401 |b d20250630 | |
| 100 | 1 | |a Perić Nikica | |
| 245 | 1 | |a Modular Coordination of Vehicle Routing and Bin Packing Problems in Last Mile Logistics | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Background: Logistics and transport, core of many business processes, are continuously optimized to improve efficiency and market competitiveness. The paper describes a modular coordination of vehicle routing and bin packing problems that enables independent instances of the problems to be joined together, with the aim that the vehicle routing solution satisfies all the constraints from real-world applications. Methods: The vehicle routing algorithm is based on an adaptive memory procedure that also incorporates a simple, one-dimensional bin packing problem. This preliminary packing solution is refined by a complex, three dimensional bin packing for each vehicle to identify the infeasible packages. The method iteratively adjusts virtual volumes until reaching near-optimal routes that respect bin-packing constraints. Results: The coordination enables independent applications of an adaptive memory procedure to vehicle routing and a genetic algorithm approach to bin packing while joining them in a computationally tractable way. Such a coordinated approach is applied to a frequently used public benchmark and proven to provide commensurate costs while significantly lowering algorithm complexity. Conclusions: The proposed method is further validated on a real industrial case study and provided additional savings of 14.48% in average daily distance traveled compared to the current industrial standard. | |
| 653 | |a Packing problem | ||
| 653 | |a Cost control | ||
| 653 | |a Packaging | ||
| 653 | |a Heuristic | ||
| 653 | |a Energy consumption | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Optimization | ||
| 653 | |a LIFO | ||
| 653 | |a Vehicles | ||
| 700 | 1 | |a Kolak Anđelko | |
| 700 | 1 | |a Lešić Vinko | |
| 773 | 0 | |t Logistics |g vol. 9, no. 2 (2025), p. 70-97 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3223923356/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3223923356/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3223923356/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |