The multi-depot k-traveling repairman problem

Bruni, Maria Elena; Khodaparasti, Sara; Martínez-Salazar, Iris; Nucamendi-Guillén, Samuel

doi:10.1007/s11590-021-01845-7

Cited by 4 publications

(3 citation statements)

References 44 publications

(69 reference statements)

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“…The mathematical formulation of DRP-ShaBH is essentially developed based on the multi-layered network, which has been proved to be superior to other models, for a wide range of routing problems (for more information about the multi-layered network, please see [15][16][17][18][19]). The model takes the advantage of position-dependent binary variables: x r i which indicates if customer i ∈ C is visited in position r along the drone tour (meaning that customer i is the r th -last customer served by the drone within its trip), y r ij to show that customer j ∈ C is visited right after node i ∈ C ∪ D in position r and w r f j which tells if customer j ∈ C is the first one to be visited by a drone launched from BH f ∈ D and there are r − 1 customers left to be visited along the tour.…”

Section: Drone Routing Problem With Shared Beehivesmentioning

confidence: 99%

A Variable Neighborhood Descent Matheuristic for the Drone Routing Problem with Beehives Sharing

Bruni

Khodaparasti

2022

Sustainability

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In contemporary urban logistics, drones will become a preferred transportation mode for last-mile deliveries, as they have shown commercial potential and triple-bottom-line performance. Drones, in fact, address many challenges related to congestion and emissions and can streamline the last leg of the supply chain, while maintaining economic performance. Despite the common conviction that drones will reshape the future of deliveries, numerous hurdles prevent practical implementation of this futuristic vision. The sharing economy, referred to as a collaborative business model that foster sharing, exchanging and renting resources, could lead to operational improvements and enhance the cost control ability and the flexibility of companies using drones. For instance, the Amazon patent for drone beehives, which are fulfilment centers where drones can be restocked before flying out again for another delivery, could be established as a shared delivery systems where different freight carriers jointly deliver goods to customers. Only a few studies have addressed the problem of operating such facilities providing services to retail companies. In this paper, we formulate the problem as a deterministic location-routing model and derive its robust counterpart under the travel time uncertainty. To tackle the computational complexity of the model caused by the non-linear energy consumption rates in drone battery, we propose a tailored matheuristic combining variable neighborhood descent with a cut generation approach. The computational experiments show the efficiency of the solution approach especially compared to the Gurobi solver.

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Section: Drone Routing Problem With Shared Beehivesmentioning

confidence: 99%

A Variable Neighborhood Descent Matheuristic for the Drone Routing Problem with Beehives Sharing

Bruni

Khodaparasti

2022

Sustainability

Self Cite

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“…(2022) for the LLRP, and solving it using CPLEX 20.1, and the optimal solution value/best lower bound obtained after implementing and solving (with a time limit of 216,000 seconds) the first MILP model (Model 1) presented in Bruni et al. (2022) for the multidepot k ‐traveling repairman problem (MD k ‐TRP). The optimal solution value of the MD k ‐TRP is indeed a valid lower bound for the LLRP because it is a special case of LLRP in which the capacity constraints of the vehicles are not considered, and all the available depots can be opened.…”

Section: Computational Resultsmentioning

confidence: 99%

“…• LB: Lower bound. It corresponds to the largest value between LB1, LB2 (which are the lower bounds proposed in Moshref-Javadi and Lee, 2016), the optimal solution value of the linear relaxation found after implementing the first MILP model (Model 1) presented in Nucamendi-Guillén et al ( 2022) for the LLRP, and solving it using CPLEX 20.1, and the optimal solution value/best lower bound obtained after implementing and solving (with a time limit of 216,000 seconds) the first MILP model (Model 1) presented in Bruni et al (2022) for the multidepot ktraveling repairman problem (MDk-TRP). The optimal solution value of the MDk-TRP is indeed a valid lower bound for the LLRP because it is a special case of LLRP in which the capacity constraints of the vehicles are not considered, and all the available depots can be opened.…”

Section: Gobal Resultsmentioning

confidence: 99%

Effective metaheuristics for the latency location routing problem

Osorio-Mora

Rey

Toth

et al. 2023

Int Trans Operational Res

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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.

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Minimizing total weighted latency in home healthcare routing and scheduling with patient prioritization

et al. 2023

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We study a home healthcare routing and scheduling problem, where multiple healthcare service provider teams should visit a given set of patients at their homes. The problem involves assigning each patient to a team and generating the routes of the teams such that each patient is visited once. When patients are prioritized according to the severity of their condition or their service urgency, the problem minimizes the total weighted waiting time of the patients, where the weights represent the triage levels. In this form, the problem generalizes the multiple traveling repairman problem. To obtain optimal solutions for small to moderate-size instances, we propose a level-based integer programming (IP) model on a transformed input network. To solve larger instances, we develop a metaheuristic algorithm that relies on a customized saving procedure and a general variable neighborhood search algorithm. We evaluate the IP model and the metaheuristic on various small-, medium- and large-sized instances coming from the vehicle routing literature. While the IP model finds the optimal solutions to all the small- and medium-sized instances within three hours of run time, the metaheuristic algorithm achieves the optimal solutions to all instances within merely a few seconds. We also provide a case study involving Covid-19 patients in a district of Istanbul and derive insights for the planners by means of several analyses.

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The multi-depot k-traveling repairman problem

Cited by 4 publications

References 44 publications

A Variable Neighborhood Descent Matheuristic for the Drone Routing Problem with Beehives Sharing

A Variable Neighborhood Descent Matheuristic for the Drone Routing Problem with Beehives Sharing

Effective metaheuristics for the latency location routing problem

Minimizing total weighted latency in home healthcare routing and scheduling with patient prioritization

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