A Biased–Randomized Iterated Local Search with Round-Robin for the Periodic Vehicle Routing Problem
The periodic vehicle routing problem (PVRP) is a well-known challenge in real-life logistics, requiring the planning of vehicle routes over multiple days while enforcing visitation frequency constraints. Although numerous metaheuristic and exact methods have tackled various PVRP extensions, real-wor...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-08-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/15/2488 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849770492239544320 |
|---|---|
| author | Juan F. Gomez Antonio R. Uguina Javier Panadero Angel A. Juan |
| author_facet | Juan F. Gomez Antonio R. Uguina Javier Panadero Angel A. Juan |
| author_sort | Juan F. Gomez |
| collection | DOAJ |
| description | The periodic vehicle routing problem (PVRP) is a well-known challenge in real-life logistics, requiring the planning of vehicle routes over multiple days while enforcing visitation frequency constraints. Although numerous metaheuristic and exact methods have tackled various PVRP extensions, real-world settings call for additional features such as depot configurations, tight visitation frequency constraints, and heterogeneous fleets. In this paper, we present a two-phase biased–randomized algorithm that addresses these complexities. In the first phase, a round-robin assignment quickly generates feasible and promising solutions, ensuring each customer’s frequency requirement is met across the multi-day horizon. The second phase refines these assignments via an iterative search procedure, improving route efficiency and reducing total operational costs. Extensive experimentation on standard PVRP benchmarks shows that our approach is able to generate solutions of comparable quality to established state-of-the-art algorithms in relatively low computational times and stands out in many instances, making it a practical choice for real life multi-day vehicle routing applications. |
| format | Article |
| id | doaj-art-ea7db511ef034532ba77e175756ec70d |
| institution | DOAJ |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-ea7db511ef034532ba77e175756ec70d2025-08-20T03:02:58ZengMDPI AGMathematics2227-73902025-08-011315248810.3390/math13152488A Biased–Randomized Iterated Local Search with Round-Robin for the Periodic Vehicle Routing ProblemJuan F. Gomez0Antonio R. Uguina1Javier Panadero2Angel A. Juan3Research Center on Production Management and Engineering, Universitat Politècnica de València, 03801 Alcoy, SpainResearch Center on Production Management and Engineering, Universitat Politècnica de València, 03801 Alcoy, SpainDepartment of Computer Architecture & Operating Systems, Universitat Autònoma de Barcelona, 08193 Bellaterra, SpainResearch Center on Production Management and Engineering, Universitat Politècnica de València, 03801 Alcoy, SpainThe periodic vehicle routing problem (PVRP) is a well-known challenge in real-life logistics, requiring the planning of vehicle routes over multiple days while enforcing visitation frequency constraints. Although numerous metaheuristic and exact methods have tackled various PVRP extensions, real-world settings call for additional features such as depot configurations, tight visitation frequency constraints, and heterogeneous fleets. In this paper, we present a two-phase biased–randomized algorithm that addresses these complexities. In the first phase, a round-robin assignment quickly generates feasible and promising solutions, ensuring each customer’s frequency requirement is met across the multi-day horizon. The second phase refines these assignments via an iterative search procedure, improving route efficiency and reducing total operational costs. Extensive experimentation on standard PVRP benchmarks shows that our approach is able to generate solutions of comparable quality to established state-of-the-art algorithms in relatively low computational times and stands out in many instances, making it a practical choice for real life multi-day vehicle routing applications.https://www.mdpi.com/2227-7390/13/15/2488combinatorial optimizationmetaheuristicsperiodic vehicle routing problemlocal search |
| spellingShingle | Juan F. Gomez Antonio R. Uguina Javier Panadero Angel A. Juan A Biased–Randomized Iterated Local Search with Round-Robin for the Periodic Vehicle Routing Problem Mathematics combinatorial optimization metaheuristics periodic vehicle routing problem local search |
| title | A Biased–Randomized Iterated Local Search with Round-Robin for the Periodic Vehicle Routing Problem |
| title_full | A Biased–Randomized Iterated Local Search with Round-Robin for the Periodic Vehicle Routing Problem |
| title_fullStr | A Biased–Randomized Iterated Local Search with Round-Robin for the Periodic Vehicle Routing Problem |
| title_full_unstemmed | A Biased–Randomized Iterated Local Search with Round-Robin for the Periodic Vehicle Routing Problem |
| title_short | A Biased–Randomized Iterated Local Search with Round-Robin for the Periodic Vehicle Routing Problem |
| title_sort | biased randomized iterated local search with round robin for the periodic vehicle routing problem |
| topic | combinatorial optimization metaheuristics periodic vehicle routing problem local search |
| url | https://www.mdpi.com/2227-7390/13/15/2488 |
| work_keys_str_mv | AT juanfgomez abiasedrandomizediteratedlocalsearchwithroundrobinfortheperiodicvehicleroutingproblem AT antonioruguina abiasedrandomizediteratedlocalsearchwithroundrobinfortheperiodicvehicleroutingproblem AT javierpanadero abiasedrandomizediteratedlocalsearchwithroundrobinfortheperiodicvehicleroutingproblem AT angelajuan abiasedrandomizediteratedlocalsearchwithroundrobinfortheperiodicvehicleroutingproblem AT juanfgomez biasedrandomizediteratedlocalsearchwithroundrobinfortheperiodicvehicleroutingproblem AT antonioruguina biasedrandomizediteratedlocalsearchwithroundrobinfortheperiodicvehicleroutingproblem AT javierpanadero biasedrandomizediteratedlocalsearchwithroundrobinfortheperiodicvehicleroutingproblem AT angelajuan biasedrandomizediteratedlocalsearchwithroundrobinfortheperiodicvehicleroutingproblem |