Local and random searches for dispatch and conflict-free routing problem of capacitated AGV systems

Murakami, Toshiyuki; Inoue, Kyo

doi:10.1016/j.cie.2015.10.017

Cited by 117 publications

(58 citation statements)

References 37 publications

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“…TSN models have been popularly in transportation and logistics to model the routing problem when time and space constraints are both considered 10,21 . For route planning of AGVs, Nishi's TSN model 8 and Miyamoto's TSN model 10 typically consider fixed transport times and therefore energy consumption cannot be further reduced. Therefore, in this article, a TSN model including flexible transport times, 16 which has the potential to reduce energy consumption, is revisited.…”

Section: Modelingmentioning

confidence: 99%

“…For path planning of AGVs, existing TSN models 10,16 plan the routes in a global way. The global planning problem corresponds to a multicommodity network flow problem using integer variables.…”

Section: Receding Horizon Planningmentioning

confidence: 99%

“…A well‐known classical method is Dijkstra ' s algorithm to obtain the shortest path of a single vehicle in the road network. Recently, time‐space network (TSN) models 8‐10 have been proposed for planning collision‐free paths of multiple AGVs. For these TSN formulations, Lagrangian relaxation and hybrid heuristic methods were developed to address the formulated mixed integer programming problems.…”

Section: Introductionmentioning

confidence: 99%

“…The TSN models 8,10 are regarded as a powerful tool for describing the path planning and routing of multiple AGVs in the mesh roadmap. The TSN models include location and time constraints, which enable to avoid collisions between vehicles.…”

Section: Introductionmentioning

confidence: 99%

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Receding horizon path planning of automated guided vehicles using a time‐space network model

Xin

Wei

Wang

et al. 2020

Optim Control Appl Methods

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Summary Time‐space network (TSN) models have been widely used for collision‐free path planning of automated guided vehicles. However, existing TSN models are planned globally. The global method suffers from computational complexity and uncertainties cannot be dealt with in the dynamic environment. To address these limitations, this article proposes a new methodology to decompose the global planning problem into smaller local planning problems, which are planned in a receding horizon way. For the local problem, new decision variables and constraints are incorporated into the TSN framework. Extensive simulation experiments are carried out to show the potential of the proposed methodology. Simulation results show that the proposed method obtains competitive performances and computational times are considerably reduced, compared with the global method.

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Section: Modelingmentioning

confidence: 99%

Section: Receding Horizon Planningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Receding horizon path planning of automated guided vehicles using a time‐space network model

Xin

Wei

Wang

et al. 2020

Optim Control Appl Methods

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“…In the storage system, the goods are large and the road condition is complicated. In order to quickly find target goods, more and more warehouses began to use the automated guided vehicle [1][2]. Path planning is an important subject in the research of automated guided vehicle related technologies.…”

Section: Introductionmentioning

confidence: 99%

Improved Hybrid Algorithm of Path Planning for Automated Guided Vehicle in Storage System

Wang¹,

Zhu

Guo³

et al. 2017

Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

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Abstract-Aiming at the problem of automated guided vehicle path planning in storage system, this paper provided a new method which combines global path planning algorithm and local path planning algorithm to achieve the goal of finding the optimal path. In this paper, the improved A* algorithm is used as the global path planning algorithm, and the improved APF algorithm is used as the local path planning algorithm. The hybrid algorithm not only makes full use of the known information to generate the global optimal path, but also can effectively avoid obstacles on the path. The advantages and effectiveness of the hybrid algorithm are proved by the results of simulations and applications.

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Intelligent Scheduling and Routing of a Heterogenous Fleet of Automated Guided Vehicles (AGVs) in a Production Environment with Partial Recharge

Akkaya

Gökçe

2022

Lecture Notes in Networks and Systems

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Local and random searches for dispatch and conflict-free routing problem of capacitated AGV systems

Cited by 117 publications

References 37 publications

Receding horizon path planning of automated guided vehicles using a time‐space network model

Receding horizon path planning of automated guided vehicles using a time‐space network model

Improved Hybrid Algorithm of Path Planning for Automated Guided Vehicle in Storage System

Intelligent Scheduling and Routing of a Heterogenous Fleet of Automated Guided Vehicles (AGVs) in a Production Environment with Partial Recharge

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