A branch-cut-and-price algorithm for the cumulative capacitated vehicle routing problem

Damião, Caio Marinho; Silva, João Marcos Pereira; Uchôa, Eduardo

doi:10.1007/s10288-021-00498-7

Cited by 8 publications

(3 citation statements)

References 38 publications

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“…[ Bulhões et al, 2018] apply the methodology to a VRP with service levels and visit selection, while [Homsi et al, 2020] use it in a ship routing problem featuring visit selection, vehicle to cargo compatibility constraints and vehicle-dependent starting locations. [Pessoa et al, 2020] have developed a generic solver for VRPs using Branch-Cut-and-Price called VRPSolver, and [Damião et al, 2021] have implemented the Cumulative Capacitated Vehicle Routing Problem within this package. Even though implementing the present problem with the VRPSolver package was a promising starting point in our research, we could not model the cumulative aspect of our objective function within this framework in its current state.…”

Section: Solution Methods For Vrpsmentioning

confidence: 99%

“…Even though implementing the present problem with the VRPSolver package was a promising starting point in our research, we could not model the cumulative aspect of our objective function within this framework in its current state. The approach used by [Damião et al, 2021] would not work because it relies on the fact that customers have the same weight; furthermore, it depends on reasonable estimates of the maximum number of customers serviced in a single route.…”

Section: Solution Methods For Vrpsmentioning

confidence: 99%

“…We refer to for a general survey on VRP variants. Its objective function accumulates when servicing consecutive requests, similar to the Time Dependent Traveling Salesman Problem (TDTSP) studied in [Abeledo et al, 2013], the Cumulative Capacitated Vehicle Routing Problem studied in [Ngueveu et al, 2010] and [Damião et al, 2021] or the Traveling Repairman Problem studied in [Afrati et al, 1986]. [Abeledo et al, 2013] states that the TDTSP is harder to solve than its non-cumulative TSP counterpart, suggesting that our problem may also be more difficult than a non-cumulative version of it.…”

Section: Dynamic Dispatching and Relocation Modelsmentioning

confidence: 99%

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A Branch and Price Algorithm for a Static Ambulance Routing Problem

MAZAL KRAUSS

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Emergency Medical Service (EMS) systems provide life-saving support to people in emergency situations via first aid treatment and emergency transport to medical facilities. Such systems must strive to make the best use of their limited resources; they have thus been studied in the context of static and dynamic vehicle routing problems. In this work, we study a static ambulance routing problem aiming to minimize the weighted sum of patients' waiting time while considering ambulance compatibility, patients' priorities, ambulance redirection, and ambulance reassignment. We implement an exact Branch-and-Price algorithm over a Set Partitioning Formulation, study the results of this algorithm, and compare them to previously studied online heuristics using data from Rio de Janeiro's public SAMU system. The results obtained allow us to assess the value of perfect information in such systems, providing a comparative baseline for subsequent developments of online algorithms.

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Section: Solution Methods For Vrpsmentioning

confidence: 99%

Section: Solution Methods For Vrpsmentioning

confidence: 99%

Section: Dynamic Dispatching and Relocation Modelsmentioning

confidence: 99%

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A Branch and Price Algorithm for a Static Ambulance Routing Problem

MAZAL KRAUSS

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Genetic Algorithm Optimization with Selection Operator Decider

Meniz,

Tiryaki

2024

Arab J Sci Eng

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Genetic Algorithm (GA) is a powerful and flexible meta-heuristic tool to deal with the complexity of optimization problems, as they are directly related to real-life situations. The primary goal of an optimization problem could be to obtain a solution with less effort and near-optimal rather than slow, improbable optimal. GAs serve this purpose by broadly exploring the possible solution space and using genetic operators. The performance of GAs can vary significantly depending on the genetic operators. Although each operator type has upsides and downsides, the selection operator greatly influences the GA’s performance. Conventional GAs initialize with predetermined genetic operators and continue with the same throughout all iterations. In this paper, dynamically adjusting the selection operators to the current progress of the iteration will be shown to be a crucial strategy to improve the performance of the GA. This study aims to propose a novel GA capable of harnessing multiple selection operators by a self-deciding operator structure, which is more advantageous at the current iteration. For this, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), which is known as a simple and effective multi-criteria decision-making method, will be integrated into the GA by a proposed dynamic decision matrix. The proposed Selection Operator Decider Genetic Algorithm (SODGA) has unique properties with varying selection processes and is capable of using TOPSIS as a decider of the operator inside the iterations. The effectiveness of the presented SODGA framework will be analyzed by a Capacitated Vehicle Routing Problems (CVRPs) benchmark set.

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