A self-adaptive evolutionary algorithm for dynamic vehicle routing problems with traffic congestion

Sabar, Nasser R.; Bhaskar, Ashish; Chung, Edward; Turky, Ayad; Song, Andy

doi:10.1016/j.swevo.2018.10.015

Cited by 76 publications

(32 citation statements)

References 27 publications

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“…Then, we describe some representative approaches to DVRPs. Based on different dynamic attributes in real world, three types of DVRPs are commonly used in DVRP modeling, i.e., VRP with stochastic customer request or demand [19,32], VRP with uncertain service time [7,37], and VRP with uncertain traveling time [5,35]. Jia et al [19] developed a dynamic logistic dispatching system where the underlying model is the DVRP that allows new customer requests being received as a working day processes.…”

Section: Related Work On Dvrpsmentioning

confidence: 99%

“…Chen et al [7] considered the uncertainty of service time in DVRPs, and thus intended to find solutions that have not only low traveling cost but also low sensitivity to deviations of service times. Sabar et al [35] investigated DVRP with uncertain traveling time where the traveling time between a pair of customers may vary because of possible traffic congestion.…”

Section: Related Work On Dvrpsmentioning

confidence: 99%

“…To solve various DVRPs, a considerable number of approaches have been proposed and can be roughly categorized into three types [3,24,25,45]. The first type of approaches to DVRPs is to maintain or enhance population diversity in optimization for adapting to dynamic changes in DVRPs, in case that some non-optimal solutions outperform the optimal solutions when dynamic changes occur [1, 35,41]. Wang et al [41] proposed an ensemble learning-based multi-objective evolutionary algorithm (EL-DMOEA) to solve DVRPs with time windows, where a population-based prediction strategy, an immigrant strategy, and a random strategy are employed to enhance the population diversity and accelerate the convergence of the evolutionary algorithm.…”

Section: Related Work On Dvrpsmentioning

confidence: 99%

See 2 more Smart Citations

A responsive ant colony optimization for large-scale dynamic vehicle routing problems via pheromone diversity enhancement

Liu

Xiang

et al. 2021

Complex Intell. Syst.

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Large-scale dynamic vehicle routing problem (LSDVRP) is exhibiting extensive application prospect with the rapid growth of online logistics, whereas a few approaches have been developed to address LSDVRPs. The difficulty in solving LSDVRPs lies in that it requires quick response and high adaptability to numerous newly appeared customers in LSDVRPs. To overcome this difficulty, in this paper, we propose a responsive ant colony optimization algorithm, termed as RACO, for efficiently addressing LSDVRPs. In the proposed RACO, a pheromone diversity enhancing method is suggested to generate diverse pheromone matrices for quickly responding to newly appeared customer requests in solving LSDVRPs. A pheromone ensemble technique is further designed to produce a high-quality initial population that well adapts to the new customer requests by making use of diverse pheromone matrices. Empirical results on a set of 12 LSDVRP test instances demonstrate the effectiveness of the suggested pheromone diversity enhancing method in quickly responding to newly appeared customer requests for solving LSDVRPs. Moreover, we investigate the computational cost and the traveling cost obtained by the proposed RACO to evaluate responsiveness and adaptability of the proposed RACO, respectively. Comparison with four state-of-the-art approaches to DVRPs validates the superiority of the proposed RACO in addressing LSDVRPs in terms of responsiveness and adaptability.

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Section: Related Work On Dvrpsmentioning

confidence: 99%

Section: Related Work On Dvrpsmentioning

confidence: 99%

Section: Related Work On Dvrpsmentioning

confidence: 99%

See 1 more Smart Citation

A responsive ant colony optimization for large-scale dynamic vehicle routing problems via pheromone diversity enhancement

Liu

Xiang

et al. 2021

Complex Intell. Syst.

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“…The vehicles return to the same depot once the routes have been completed. The objective of VRPs is to optimize the routes by minimizing the total travelled distance, therefore cutting the costs while respecting all constraints [45,[47][48][49][50].…”

Section: Starting Point: the Dynamic Vehicle Routing Problemmentioning

confidence: 99%

Analysing the Police Patrol Routing Problem: A Review

Dewinter

Vandeviver

Beken

et al. 2020

IJGI

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Police patrol is a complex process. While on patrol, police officers must balance many intersecting responsibilities. Most notably, police must proactively patrol and prevent offenders from committing crimes but must also reactively respond to real-time incidents. Efficient patrol strategies are crucial to manage scarce police resources and minimize emergency response times. The objective of this review paper is to discuss solution methods that can be used to solve the so-called police patrol routing problem (PPRP). The starting point of the review is the existing literature on the dynamic vehicle routing problem (DVRP). A keyword search resulted in 30 articles that focus on the DVRP with a link to police. Although the articles refer to policing, there is no specific focus on the PPRP; hence, there is a knowledge gap. A diversity of approaches is put forward ranging from more convenient solution methods such as a (hybrid) Genetic Algorithm (GA), linear programming and routing policies, to more complex Markov Decision Processes and Online Stochastic Combinatorial Optimization. Given the objectives, characteristics, advantages and limitations, the (hybrid) GA, routing policies and local search seem the most valuable solution methods for solving the PPRP.

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“…Li et al [28] proposed an improved ant colony optimization algorithm to solve the multi-depot green vehicle routing problem with multiple objectives. Various other methods that have been developed in existing literature are such as the evolutionary algorithms [29], genetic algorithm [30], tabu search algorithm [31], fruit fly optimization algorithm [32] and iterated local search algorithm [33].…”

Section: Introductionmentioning

confidence: 99%

An Effective Discrete Artificial Bee Colony Algorithm for Scheduling an Automatic-Guided-Vehicle in a Linear Manufacturing Workshop

2020

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This paper deals with a new automatic guided vehicle (AGV) scheduling problem from the material handling process in a linear manufacturing workshop. The problem is to determine a sequence of Cells for AGV to travel to minimize the standard deviation of the waiting time of the Cells and the total travel distance of AGV. For this purpose, we first propose an integer linear programming model based on a comprehensive investigation. Then, we present an improved nearest-neighbor-based heuristic so as to fast generate a good solution in view of the problem-specific characteristics. Next, we propose an effective discrete artificial bee colony algorithm with some novel and advanced techniques including a heuristic-based initialization, six neighborhood structures and a new evolution strategy in the onlooker bee phase. Finally, the proposed algorithms are empirically evaluated based on several typical instances from the real-world linear manufacturing workshop. A comprehensive and thorough experiment shows that the presented algorithm produces superior results which are also demonstrated to be statistically significant than the existing algorithms. INDEX TERMS Automated guided vehicle, heuristic, discrete artificial bee colony algorithm, scheduling, linear manufacturing workshop.

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A self-adaptive evolutionary algorithm for dynamic vehicle routing problems with traffic congestion

Cited by 76 publications

References 27 publications

A responsive ant colony optimization for large-scale dynamic vehicle routing problems via pheromone diversity enhancement

A responsive ant colony optimization for large-scale dynamic vehicle routing problems via pheromone diversity enhancement

Analysing the Police Patrol Routing Problem: A Review

An Effective Discrete Artificial Bee Colony Algorithm for Scheduling an Automatic-Guided-Vehicle in a Linear Manufacturing Workshop

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