Cluster-based Hyper-Heuristic for Large-Scale Vehicle Routing Problem

Costa, Joao Guilherme Cavalcanti; Mei, Yi; Zhang, Mengjie

doi:10.1109/cec48606.2020.9185831

Cited by 7 publications

(4 citation statements)

References 27 publications

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“…This two-phase paradigm is usually referred to as the "routing first clustering second" approach [222]. Correspondingly, "clustering first routing second" is another two-phase approach, which is now commonly used in solving large-scale VRPs [51,60]. It separates customers into several clusters, then constructs a route for each cluster.…”

Section: Greedy Acceptancementioning

confidence: 99%

Heuristics for Vehicle Routing Problem: A Survey and Recent Advances

Liu¹,

Lu²,

Gui³

et al. 2023

Preprint

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Vehicle routing is a well-known optimization research topic with significant practical importance. Among different approaches to solving vehicle routing, heuristics can produce a satisfactory solution at a reasonable computational cost. Consequently, much effort has been made in the past decades to develop vehicle routing heuristics. In this article, we systematically survey the existing vehicle routing heuristics, particularly on works carried out in recent years. A classification of vehicle routing heuristics is presented, followed by a review of their methodologies, recent developments, and applications. Moreover, we present a general framework of state-of-the-art methods and provide insights into their success. Finally, three emerging research topics with notable works and future directions are discussed.

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Section: Greedy Acceptancementioning

confidence: 99%

Heuristics for Vehicle Routing Problem: A Survey and Recent Advances

Liu¹,

Lu²,

Gui³

et al. 2023

Preprint

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show abstract

“…The hyper-heuristic algorithm has already been used to solve clustering problems. Costa et al [20] proposed a cluster-based hyper-heuristic for large-scale vehicle routing problems. The proposed hyper-heuristic used 11 LLHs to find the optimal routes.…”

Section: ) Hyper-heuristicmentioning

confidence: 99%

A Multi-Objective Hyper-Heuristic Clustering Algorithm for Formulas in Traditional Chinese Medicine

Shi,

Zhang,

et al. 2023

IEEE Access

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Syndrome types are important for diagnosis and treatment in traditional Chinese medicine. Syndrome types can be summarized by domain experts as formula clusters. In this paper, we propose seven feature models for the formula clustering problem based on categories, subcategories, functional tendencies and names of Chinese materia medica. A novel multi-objective clustering hyper-heuristic algorithm is obtained. In our proposed algorithm, 12 low-level heuristics are used for clustering solution perturbation by merging clusters, dividing clusters or moving points between clusters based on received solutions from the high-level heuristic. The high-level heuristic evaluates the received solutions from low-level heuristics, updates the solution pool, and selects initial solutions for the next iteration via roulette wheel selection on the Pareto front. Experimental results demonstrate that the proposed algorithm outperforms other clustering algorithms in most datasets. The initial number of clusters has less influence on the final clustering solutions for our proposed algorithm than for other clustering algorithms. For most datasets, the roulette wheel selection mechanism on the Pareto front shows higher convergence rates and accuracy than a random selection mechanism. Accuracy was higher for feature models based on functional tendencies than for the other feature models.

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“…The general structure of the proposed approaches is based on the combination of k-means clustering and genetic operators. The k-means clustering process was proved to be efficient in simplifying logistic networks and improving the routing solution [32] , [33] . Furthermore, in order to prevent the HGA from being trapped into local optimum, we developed the MA that incorporates a local search (LS) procedure to improve the solution in local search scope.…”

Section: Solution Approachesmentioning

confidence: 99%

Towards an efficient collection and transport of COVID-19 diagnostic specimens using genetic-based algorithms

Tlili

Masri

Krichen

2022

Applied Soft Computing

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The speed by which the COVID-19 pandemic spread throughout the world makes the emergency services unprepared to answer all the patients’ requests. The Tunisian ministry of health established a protocol planning the sample collection from the patients at their location. A triage score is first assigned to each patient according to the symptoms he is showing, and his health conditions. Then, given the limited number of the available ambulances in each area, the location of the patients and the capacity of the nearby hospitals for receiving the testing samples, an ambulance scheduling and routing plan needs to be established so that specimens can be transferred to hospitals in short time. In this paper, we propose to model this problem as a Multi-Origin-Destination Team Orienteering Problem (MODTOP). The objective is to find the optimal one day tour plan for the available ambulances that maximizes the collected scores of visited patients while respecting duration and capacity constraints. To solve this NP-hard problem, two highly effective approaches are proposed which are Hybrid Genetic Algorithm (HGA) and Memetic Algorithm (MA). The HGA combines (i) a k-means construction method for initial population generation and (ii) a one point crossover operator for solution recombination. The MA is an improvement of HGA that integrates an effective local search based on three different neighborhood structures. Computational experiments, supported by a statistical analysis on benchmark data sets, illustrate the efficiency of the proposed approaches. HGA and MA reached the best known solutions in 54.7% and 73.5% of instances, respectively. Likewise, MA reached a relative error of 0.0675% and performed better than four existing approaches. Real-case instances derived from the city of Tunis were also solved and compared with the results of an exact solver Cplex to validate the effectiveness of our algorithm.

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Cluster-based Hyper-Heuristic for Large-Scale Vehicle Routing Problem

Cited by 7 publications

References 27 publications

Heuristics for Vehicle Routing Problem: A Survey and Recent Advances

Heuristics for Vehicle Routing Problem: A Survey and Recent Advances

A Multi-Objective Hyper-Heuristic Clustering Algorithm for Formulas in Traditional Chinese Medicine

Towards an efficient collection and transport of COVID-19 diagnostic specimens using genetic-based algorithms

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