Abstract
The latency location routing problem (LLRP), a combination of the facility location problem and the cumulative capacitated vehicle routing problem, is a recently proposed variant of location routing problems. It corresponds to a customer-centric problem, in which the aim is to minimize the sum of the arrival times at the customers. This paper proposes three novel metaheuristic algorithms to solve the LLRP. They use a simulated annealing framework, which after each temperature reduction is intensified through a variable neighborhood descent procedure. Each algorithm uses a different search strategy as intensification. Results on 76 benchmark instances indicate that the proposed metaheurstics outperform the state-of-the-art algorithms, finding new best solutions for all the large-sized instances (over 100 customers), or the currently known optimal ones for most of the small- and medium-sized instances, in comparable computing times. Furthermore, in more than 80% of the instances the average value of the solutions found by the proposed algorithms is better than or equal to that of the current best known solution.
Original language | English |
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Pages (from-to) | 3801-3832 |
Journal | International Transactions in Operational Research |
Volume | 30 |
Issue number | 6 |
DOIs | |
Publication status | Published - 2023 |
Bibliographical note
Funding Information:This work is partially supported by the National Agency for Research and Development (ANID)/Scholarship Program/Doctorado Becas Chile/2020‐72210275, and ANID/Scholarship Program/Doctorado Becas Chile/2018‐72190600. We also acknowledge Air Force Office of Scientific Research Grant FA8655‐20‐1‐7019, AFOSR Grant no. A9550‐17‐1‐0234, and Grant no. FA8655‐21‐1‐7046. The authors are extremely grateful to the anonymous referees for their very helpful comments.
Publisher Copyright:
© 2023 The Authors. International Transactions in Operational Research published by John Wiley & Sons Ltd on behalf of International Federation of Operational Research Societies.
Funding
This work is partially supported by the National Agency for Research and Development (ANID)/Scholarship Program/Doctorado Becas Chile/2020‐72210275, and ANID/Scholarship Program/Doctorado Becas Chile/2018‐72190600. We also acknowledge Air Force Office of Scientific Research Grant FA8655‐20‐1‐7019, AFOSR Grant no. A9550‐17‐1‐0234, and Grant no. FA8655‐21‐1‐7046. The authors are extremely grateful to the anonymous referees for their very helpful comments. This work is partially supported by the National Agency for Research and Development (ANID)/Scholarship Program/Doctorado Becas Chile/2020-72210275, and ANID/Scholarship Program/Doctorado Becas Chile/2018-72190600. We also acknowledge Air Force Office of Scientific Research Grant FA8655-20-1-7019, AFOSR Grant no. A9550-17-1-0234, and Grant no. FA8655-21-1-7046. The authors are extremely grateful to the anonymous referees for their very helpful comments. Open Access Funding provided by Universita degli Studi di Bologna within the CRUI-CARE Agreement.
Funders | Funder number |
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Universita degli Studi di Bologna | |
Air Force Office of Scientific Research | A9550‐17‐1‐0234, FA8655‐21‐1‐7046, FA8655‐20‐1‐7019 |
Agenția Națională pentru Cercetare și Dezvoltare | |
Agencia Nacional de Investigación y Desarrollo | 72190600, 72210275 |
Keywords
- cumulative routing
- LLRP
- location routing
- simulated annealing
- variable neighborhood descent