ACO-VRPTWRV: A New Algorithm for the Vehicle Routing Problems with Time Windows and Re-used Vehicles based on Ant Colony Optimization

Zhang, Tao; Wang, Shanshan; Tian, Wen-xin; Zhang, Yuejie

doi:10.1109/isda.2006.83

Cited by 14 publications

(3 citation statements)

References 12 publications

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“…Zhang et al [16] introduced a mathematical model for VRPTW and Reused vehicles. A new algorithm viz., ACO-VRPTWRV was presented by incorporating the good aspects of ACS, MMAS and ASRank (Rank-based Ant System) by optimizing the three objectives namely, vehicle travel time, customer time window and service time.…”

Section: Solving Vrptw Using Acomentioning

confidence: 99%

A Comprehensive Study of Vehicle Routing Problem With Time Windows Using Ant Colony Optimization Techniques

Neogi¹,

Mounika²,

Kalyani³

et al. 2018

IJET

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Ant Colony Optimization (ACO) is a nature-inspired swarm intelligence technique and a metaheuristic approach which is inspired by the foraging behavior of the real ants, where ants release pheromones to find the best and shortest route from their nest to the food source. ACO is being applied to various optimization problems till date and has been giving good quality results in the field. One such popular problem is known as Vehicle Routing Problem(VRP). Among many variants of VRP, this paper presents a comprehensive survey on VRP with Time Window constraints(VRPTW). The survey is presented in a chronological order discussing which of the variants of ACO is used in each paper followed by the advantages and limitations of the same.

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Section: Solving Vrptw Using Acomentioning

confidence: 99%

A Comprehensive Study of Vehicle Routing Problem With Time Windows Using Ant Colony Optimization Techniques

Neogi¹,

Mounika²,

Kalyani³

et al. 2018

IJET

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“…Swarm intelligence algorithms study the behavior of a collection of agents in self-organized societies, i.e. bees, ants, birds, and moths [13][14][15][16][17]. Techniques based on swarm intelligence have been widely used as a wrapper method for feature selection [18], for instance, bees algorithm [19], ant colony optimization (ACO) [20], butterfly optimization algorithm [21] and moth optimization algorithm [22].…”

Section: Introductionmentioning

confidence: 99%

Hybrid feature selection method based on particle swarm optimization and adaptive local search method

Alzaqebah

Jawarneh

Mohammad

et al. 2021

IJECE

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Machine learning has been expansively examined with data classification as the most popularly researched subject. The accurateness of prediction is impacted by the data provided to the classification algorithm. Meanwhile, utilizing a large amount of data may incur costs especially in data collection and preprocessing. Studies on feature selection were mainly to establish techniques that can decrease the number of utilized features (attributes) in classification, also using data that generate accurate prediction is important. Hence, a particle swarm optimization (PSO) algorithm is suggested in the current article for selecting the ideal set of features. PSO algorithm showed to be superior in different domains in exploring the search space and local search algorithms are good in exploiting the search regions. Thus, we propose the hybridized PSO algorithm with an adaptive local search technique which works based on the current PSO search state and used for accepting the candidate solution. Having this combination balances the local intensification as well as the global diversification of the searching process. Hence, the suggested algorithm surpasses the original PSO algorithm and other comparable approaches, in terms of performance.

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“…In this paper, the first kind of AGSS is studied, formulated as VRP with tight time windows, short travel time, capacity constraint and reused vehicles (VRPTSR). Zhang et al [4] presented an ACO to solve Vehicle Routing Problem with Time Windows and Reused Vehicles VRPTWRV in logistical industry, which is different from the features in airport ground services.…”

Section: Introductionmentioning

confidence: 99%

ACO-IH: An improved ant colony optimization algorithm for Airport Ground Service Scheduling

Zhang

Chen

2008

2008 IEEE International Conference on Industrial Technology

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Many Airport Ground Service Scheduling(AGSS) problems can be formulated as Vehicle Routing Problem with Tight time windows, Short travel time and Re-used Vehicles(VRPTSR). A model with multiple objectives to minimize the number of vehicles used, the total start time of serving flights and the total flow time of vehicles for VRPTSR is presented. Based on the facts that Ant Colony Optimization (ACO) has good performance in solving combinatorial optimization problems with multi objectives, the ACO algorithm with MAX-MIN and Rankbased Ant System is proposed; an efficient heuristic called Earliest Due Date First (EDD) is incorporated into ACO as a comparative ant in order to improve the performance of ACO. Numerical computations show that the proposed ACO can construct good solutions in reasonable time. At last, policies for dealing with Non-Homogeneous vehicles are presented, which makes it more applicable.

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ACO-VRPTWRV: A New Algorithm for the Vehicle Routing Problems with Time Windows and Re-used Vehicles based on Ant Colony Optimization

Cited by 14 publications

References 12 publications

A Comprehensive Study of Vehicle Routing Problem With Time Windows Using Ant Colony Optimization Techniques

A Comprehensive Study of Vehicle Routing Problem With Time Windows Using Ant Colony Optimization Techniques

Hybrid feature selection method based on particle swarm optimization and adaptive local search method

ACO-IH: An improved ant colony optimization algorithm for Airport Ground Service Scheduling

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