A Fuzzy Hybrid GA-PSO Algorithm for Multi-Objective AGV Scheduling in FMS

Mousavi, Maryam; Yap, Hwa Jen; Musa, Siti Nurmaya

doi:10.2507/ijsimm16(1)5.368

Cited by 44 publications

(25 citation statements)

References 31 publications

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“…Regarding common drawbacks of thresholding methods where images are corrupted with artefacts and noise, the authors in [22] proposed a new multiregion thresholding methodology by using fuzzy sets. Concerning the various implementations of the fuzzy C-means clustering (FCM) method, many researchers have combined this method with other methods such as region-based active contour [23], the level set method [24], self-organizing maps [25], region growing and particle swarm optimization method [26].…”

Section: Fuzzy Hybrid Methods For the Reconstruction Of 3d Models Basementioning

confidence: 99%

Fuzzy Hybrid Method for the Reconstruction of 3D Models Based on CT/MRI Data

Šokac¹,

Vukelić²,

Jakovljević³

et al. 2019

SV-JME

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This research proposes a hybrid method for improving the segmentation accuracy of reconstructed 3D models from computed tomography/ magnetic resonance imaging (CT/MRI) data. A semi-automatic hybrid method based on combination of Fuzzy C-Means clustering (FCM) and region growing (RG) is proposed. In this approach, FCM is used in the first stage as a preprocessing step in order to classify and improve images by assigning pixels to the clusters for which they have the maximum membership, and manual selection of the membership intensity map with the best contrast separation. Afterwards, automatic seed selection is performed for RG, for which a new parameter standard deviation (STD) of pixel intensities, is included. It is based on the selection of an initial seed inside a region with maximum value of STD. To evaluate the performance of the proposed method, it was compared to several other segmentation methods. Experimental results show that the proposed method overall provides better results compared to other methods in terms of accuracy. The average sensitivity and accuracy rates for cone-beam computed tomography CBCT

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Section: Fuzzy Hybrid Methods For the Reconstruction Of 3d Models Basementioning

confidence: 99%

Fuzzy Hybrid Method for the Reconstruction of 3D Models Based on CT/MRI Data

Šokac¹,

Vukelić²,

Jakovljević³

et al. 2019

SV-JME

View full text Add to dashboard Cite

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“…Here, Equation (15) defines the ES of all machines by completing all jobs. Equations (16) and (17) define the calculation methods of esw and ese, which compose ES. In Equation (16), otw(i,j)×wesu(k) means the M k is chosen to executing the operation number j of job number i.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…Baruwa et al proposed a simultaneous scheduling method of machines and AGVs based on a timed colored petri net approach [12]. Besides, there are also some practical conditions in the workshop that have been taken into consideration by researchers in their studies, such as battery charging of AGVs, cost of operation, time of operation, and so on [13][14][15][16]. Cai et al developed a mixed regional control model for production tasks to solve the problem of task scheduling and coordination control presented by a multi-AGV system [17].…”

Section: Introductionmentioning

confidence: 99%

“…In the aspect of optimization objectives of AGVs scheduling in FMS, earlier studies have mainly concentrated on minimizing the makespan of all related jobs, and have been effectively applied on increasing productivity and the resource utilization rate [8,15,16]. Sometimes, the makespan is presented in the form of the travel distance of all AGVs [26].…”

Section: Introductionmentioning

confidence: 99%

“…Some studies have been carried out to meet the demand for better optimal scheduling of AGVs in FMS. Mousavi et al proposed an optimal scheduling model for AGVs that the objectives of which are the minimization of the makespan and number of AGVs while considering the AGVs battery charge; two optimization algorithms were also developed in the model [15,16]. Vivaldini et al presented a methodology for the estimation of the minimum number of AGVs required to execute a given transportation order within a specific time-window [33].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Multi-Objective and Multi-Dimensional Optimization Scheduling Method Using a Hybrid Evolutionary Algorithms with a Sectional Encoding Mode

Guo

2019

Sustainability

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Aimed at the problem of the green scheduling problem with automated guided vehicles (AGVs) in flexible manufacturing systems (FMS), the multi-objective and multi-dimensional optimal scheduling process is defined while considering energy consumption and multi-function of machines. The process is a complex and combinational process, considering this characteristic, a mathematical model was developed and integrated with evolutionary algorithms (EAs), which includes a sectional encoding genetic algorithm (SE-GA), sectional encoding discrete particle swarm optimization (SE-DPSO) and hybrid sectional encoding genetic algorithm and discrete particle swarm optimization (H-SE-GA-DPSO). In the model, the encoding of the algorithms was divided into three segments for different optimization dimensions with the objective of minimizing the makespan and energy consumption of machines and the number of AGVs. The sectional encoding described the sequence of operations of related jobs, the matching relation between transfer tasks and AGVs (AGV-task), and the matching relation between operations and machines (operation-machine) respectively for multi-dimensional optimization scheduling. The effectiveness of the proposed three EAs was verified by a typical experiment. Besides, in the experiment, a comparison among SE-GA, SE-DPSO, H-SE-GA-DPSO, hybrid genetic algorithm and particle swarm optimization (H-GA-PSO) and a tabu search algorithm (TSA) was performed. In H-GA-PSO and TSA, the former just takes the sequence of operations into account, and the latter takes both the sequence of operations and the AGV-task into account. According to the result of the comparison, the superiority of H-SE-GA-DPSO over the other algorithms was proved.

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Research on global optimization method for multiple AGV collision avoidance in hybrid path

Cao

Meng

2021

Optim Control Appl Methods

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Due to the increasing number of automated guided vehicles (AGVs) in the multi-AGV system and the limitation of working environment, path conflicts often occur in the working process of AGVs, which affects the working efficiency of the multi-AGV system. Thus, a optimization method by arranging the AGVs' traffic sequence is proposed in this paper. First, an AGV working map is reconstructed with graph theory, and then the corresponding collision avoidance rules are formulated for different types of conflicts. In multi-AGV system, each collision avoidance decision has an impact on the efficiency of the system, so it is crucial to adopt appropriate decisions. To optimize the decisions, the system fitness of different collision avoidance decisions are calculated based on the global state of the system, and the particle swarm optimization (PSO) algorithm is used to optimize the decisions. Furthermore, the PSO algorithm is improved by planning the direction of particle motion in the solution space and introducing mutation operation, so as to improve the search ability of the particle in the solution space. To verify the feasibility and effectiveness of the improved particle swarm optimization (IPSO) algorithm, an experiment system is built based on.NET platform. Results show that the IPSO algorithm than the traditional algorithms experimental performs better. The IPSO algorithm can effectively reduce congestion caused by path conflict and enhance the efficiency of the multi-AGV system.

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A Fuzzy Hybrid GA-PSO Algorithm for Multi-Objective AGV Scheduling in FMS

Cited by 44 publications

References 31 publications

Fuzzy Hybrid Method for the Reconstruction of 3D Models Based on CT/MRI Data

Fuzzy Hybrid Method for the Reconstruction of 3D Models Based on CT/MRI Data

A Multi-Objective and Multi-Dimensional Optimization Scheduling Method Using a Hybrid Evolutionary Algorithms with a Sectional Encoding Mode

Research on global optimization method for multiple AGV collision avoidance in hybrid path

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