Multiobjective Vehicle Routing Problems With Simultaneous Delivery and Pickup and Time Windows: Formulation, Instances, and Algorithms

Wang, Jiahai; Zhou, Ying; Wang, Yong; Zhang, Mengjie; Chen, C. L. Philip; Zheng, Zibin

doi:10.1109/tcyb.2015.2409837

Cited by 169 publications

(47 citation statements)

References 51 publications

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“…These solutions will then be applied to form the initial swarm so as to "warmly trigger" the evolutionary process of the proposed algorithm. Following that, we can reduce the computing power of the proposed method to a relatively-small scale (e.g., [ j , max ] = [20,15]) and use it as a near-optimal solution generator in the online process. Significant reduction with respect to the computation time can be achieved if an optimal control solver can start its solution-finding process ar a near-optimal solution (e.g., solutions shown in Fig.5).…”

Section: G Online Performance Of the Proposed Motion Plannermentioning

confidence: 99%

“…For example, we can model the obstacle to be overtaken as a dynamic object, and this will certainly bring more challenges to the constraint handling process. Moreover, another potential direction could be applying the proposed FAMOPSO algorithm to address more realworld engineering optimization problems, such as the multi-objective vehicle routing problem [15], [21], the multi-agent path planning problem [35], etc.…”

Section: Concluding Remarkmentioning

confidence: 99%

“…Nature-inspired optimization (NIO) methods are a wide range of various algorithms which are often used to address complex engineering optimization problems that cannot be effectively solved by traditional optimization techniques [15]- [17]. There are a large amount of recently published multi-objective NIO methods which are available in the literature [18]- [20].…”

Section: Introductionmentioning

confidence: 99%

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Multiobjective Overtaking Maneuver Planning for Autonomous Ground Vehicles

Chai

Tsourdos

Chai

et al. 2021

IEEE Trans. Cybern.

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Constrained autonomous vehicle overtaking trajectories are usually difficult to be generated due to certain practical requirements and complex environmental limitations. This problem becomes more challenging when multiple contradicting objectives are required to be optimized and the on-road objects to be overtaken are irregularlyplaced. In this paper, a novel swarm intelligence-based algorithm is proposed for producing the multi-objective optimal overtaking trajectory of autonomous ground vehicles. The proposed method solves a multiobjective optimal control model in order to optimize the maneuver time duration, the trajectory smoothness, and the vehicle visibility, while taking into account different types of mission-dependent constraints. However, one problem that could have an impact on the optimization process is the selection of algorithm control parameters. To desensitize the negative influence, a novel fuzzy adaptive strategy is proposed and embedded in the algorithm framework. This allows the optimization process can dynamically balance the local exploitation and global exploration, thereby exploring the trade-off between objectives more effective. The performance of using the designed fuzzy adaptive multi-objective method is analyzed and validated by executing a number of simulation studies. The results confirm the effectiveness of applying the proposed algorithm to produce multi-objective optimal overtaking trajectories for the autonomous ground vehicles. Moreover, the comparison to other stateof-the-art multi-objective optimization schemes shows that the designed strategy tends to be more capable in terms of producing a set of widespread and high-quality pareto-optimal solutions.

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Section: G Online Performance Of the Proposed Motion Plannermentioning

confidence: 99%

Section: Concluding Remarkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multiobjective Overtaking Maneuver Planning for Autonomous Ground Vehicles

Chai

Tsourdos

Chai

et al. 2021

IEEE Trans. Cybern.

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“…In this paper, the idea of MOLS was inspired from the paper of Wang et al (2016) in which the method of resolution is based on a single -objective local search. Therefore, we adopt the same strategy of resolution, by applying two local search algorithms, each search in one direction at a time to minimize one objective.…”

Section: Multiobjective Locale Searchmentioning

confidence: 99%

Multiobjective optimization in delivering pharmaceutical products with disrupted vehicle routing problem

Bouchra¹,

Dkhissi²,

Cherkaoui³

2020

10.5267/j.ijiec

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This paper is interested in pharmaceuticals distribution which is one of the most important activities and ensures the availability of drug products to a set of customers (pharmacies). The study introduces the Disrupted Vehicle Routing problem with Soft Time Windows since pharmaceutical distributors should respond to increased demands for products to ensure timely and efficient delivery to dynamic demands. We also propose an improved multiobjective local search (IMOLS), which uses methods of neighborhood search such as large neighborhood search (LNS) and variable neighborhood search (VNS) based on a hybrid approach in the optimization of vehicle routes. The algorithm is expected to achieve competitive results compared with previously published studies.

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“…In the field of multiobjective optimization, the set of all the Pareto-optimal solutions is usually called the Pareto set (PS) and the image of (PS) on the objective vector space is called the Pareto front (PF) [30]. Over the past decades, multiobjective evolutionary algorithms (MOEAs) have been recognized as a major methodology for approximating the PF [5], [9], [10], [12], [18], [36]- [38].…”

Section: Introductionmentioning

confidence: 99%