Variable neighborhood decomposition for Large Scale Capacitated Arc Routing Problem

Mei, Yi; Li, Xiaodong; Yao, Xin

doi:10.1109/cec.2014.6900305

Cited by 13 publications

(8 citation statements)

References 28 publications

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“…The results obtained depend on the transformation used so that instance dimension does not increase too much. Recently, Foulds et al [87] propose a compact transformation, ensuring the number of nodes in 27,28,40,41,42,55,87,128,129,138,139,140,142,143,157,160,161,174,178,191,194,195,200] With split deliveries -an edge demand can be serviced by several vehicles [29] Multi-compartment vehicles (MCV) -tasks have a demand for different products and are serviced by MCV [150] Multi-compartment vehicles; Multi-days; Semi-Periodic; With/without split deliveries; Coordinated vehicles…”

Section: Exact and Lower Bound Methodsmentioning

confidence: 99%

An updated annotated bibliography on arc routing problems

Mourão

Pinto

2017

Networks

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The number of arc routing publications has increased significantly in the last decade. Such an increase justifies a second annotated bibliography, a sequel to Corberán and Prins (Networks 56 (2010), 50–69), discussing arc routing studies from 2010 onwards. These studies are grouped into three main sections: single vehicle problems, multiple vehicle problems and applications. Each main section catalogs problems according to their specifics. Section 2 is therefore composed of four subsections, namely: the Chinese Postman Problem, the Rural Postman Problem, the General Routing Problem (GRP) and Arc Routing Problems (ARPs) with profits. Section 3, devoted to the multiple vehicle case, begins with three subsections on the Capacitated Arc Routing Problem (CARP) and then delves into several variants of multiple ARPs, ending with GRPs and problems with profits. Section 4 is devoted to applications, including distribution and collection routes, outdoor activities, post‐disaster operations, road cleaning and marking. As new applications emerge and existing applications continue to be used and adapted, the future of arc routing research looks promising. © 2017 Wiley Periodicals, Inc. NETWORKS, Vol. 70(3), 144–194 2017

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Section: Exact and Lower Bound Methodsmentioning

confidence: 99%

An updated annotated bibliography on arc routing problems

Mourão

Pinto

2017

Networks

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“…We consider the results of MAENS* , of MAENS-RDG (Mei et al 2014a) and VND (Mei et al 2014b) and an algorithm combining iterate local search and variable neighbourhood descent (Martinelli et al 2013).…”

Section: Comparison With the State-of-the-artmentioning

confidence: 99%

“…The results are compared to those achieved by MAENS*-IIa and MAENS*-IIb included in Table 9. The algorithms considered are MAENS* , MAENS-RDG (Mei et al 2014a), VND (Mei et al 2014b) and ILS-RVND (Martinelli et al 2013). No statistical test was carried out due to the partial availability of the results of the compared algorithms ants that make use of the Proportional Reward (a and b) were tested against the oracle.…”

Section: Comparison With the State-of-the-artmentioning

confidence: 99%

Dynamic selection of evolutionary operators based on online learning and fitness landscape analysis

et al. 2016

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Self-adaptive mechanisms for the identification of the most suitable variation operator in evolutionary algorithms rely almost exclusively on the measurement of the fitness of the offspring, which may not be sufficient to assess the optimality of an operator (e.g., in a landscape with an high degree of neutrality). This paper proposes a novel adaptive operator selection mechanism which uses a set of four fitness landscape analysis techniques and an online learning algorithm, dynamic weighted majority, to provide more detailed information about the search space to better determine the most suitable crossover operator. Experimental analysis on the capacitated arc routing problem has demonstrated that different crossover operators behave differently during the search process, and selecting the proper one adaptively can lead to more promising results.

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“…Motivated by the above observation, Mei et al [22][23][24] proposed several approaches to tackle large-scale CARPs. These methods share a similar iterative search framework called Cooperative Co-evolution (CC) [25][26][27][28].…”

mentioning

confidence: 99%

“…The best-so-far complete solution is used to reset the decomposition in the next iteration. In these approaches, decomposition (i.e., grouping tasks) is conducted either randomly [22] or based on a predefined route distance matrix [23,24], and different optimization techniques can be adopted to solve the sub-problems. These CC-based approaches, e.g., the Route Distance Grouping scheme with Memetic Algorithm with Extended Neighborhood Search (RDG-MAENS) [23], perform significantly better than previous approaches on EGL-G instances.…”

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confidence: 99%

A Scalable Approach to Capacitated Arc Routing Problems Based on Hierarchical Decomposition

Tang

Wang

et al. 2017

IEEE Trans. Cybern.

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The capacitated arc routing problem (CARP) is a challenging optimization problem with lots of applications in the real world. Numerous approaches have been proposed to tackle this problem. Most of these methods, albeit showing good performance on CARP instances of small and median sizes, do not scale well to large-scale CARPs, e.g., taking at least a few hours to achieve a satisfactory solution on a CARP instance with thousands of tasks. In this paper, an efficient and scalable approach is proposed for CARPs. The key idea of the proposed approach is to hierarchically decompose the tasks involved in a CARP instance into subgroups and solve the induced subproblems recursively. The output of the subproblems at the lower layer in the hierarchy is treated as virtual tasks and new subproblems are formulated based on these virtual tasks using clustering techniques. By this means, the number of tasks (or virtual tasks) decreases rapidly from the bottom to the top layers of the hierarchy, and the sizes of all subproblems at each layer can be kept tractable even for very large-scale CARPs. Empirical studies are conducted on CARP instances with up to 3584 tasks, which are an order of magnitude larger than the number of tasks involved in all CARP instances investigated in the literature. The results show that the proposed approach significantly outperforms existing methods in terms of scalability. Since the proposed hierarchical decomposition scheme is designed to obtain a good permutation of tasks in a CARP instance, it may also be generalized to other hard optimization problems that can be formulated as permutation-based optimization problems.

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Variable neighborhood decomposition for Large Scale Capacitated Arc Routing Problem

Cited by 13 publications

References 28 publications

An updated annotated bibliography on arc routing problems

An updated annotated bibliography on arc routing problems

Dynamic selection of evolutionary operators based on online learning and fitness landscape analysis

A Scalable Approach to Capacitated Arc Routing Problems Based on Hierarchical Decomposition

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