Integrated tasks assignment and routing for the estimation of the optimal number of AGVS

Vivaldini, Kelen Cristiane Teixeira; Rocha, Luís F.; Martarelli, Nádia Junqueira; Becker, Marcelo; Moreira, António Paulo

doi:10.1007/s00170-015-7343-4

Cited by 60 publications

(34 citation statements)

References 23 publications

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“…By encounter the AGV control problems, the system gaining in completed the transportation demand in a fast way with conflict-free among AGVs. Researchers have given significant attention in studying various AGV related problems, which are task assignment problem, vehicle dispatching problem and vehicle scheduling routing [3] ; [1] ; [8]; [9]; [16]; [14]; [5]. Most of the AGV related problems focusing on the performance of implement system compare with the conventional method and the majority of them are for multicapacity AGV.…”

Section: Problem Backgroundmentioning

confidence: 99%

“…They are important to determine the aspects of the AGV system implementation, which are the efficiency of task schedule and routing system of transportation in order to minimize the time movement and avoiding deadlock and collision. Another aspect is the estimation of AGV quantity, required for the tasks assignment [16]. Furthermore, [13] proposed a modified GA to determine the optimal solutions with an acceptable solution of times.…”

Section: Problem Backgroundmentioning

confidence: 99%

See 1 more Smart Citation

Optimization of Material Transportation Assignment for Automated Guided Vehicle (AGV) System

Mohamad¹,

Fauadi²,

Zainudin³

et al. 2018

IJET

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This article focuses on Material Transportation Assignment problem that is identified as an Automated Guided Vehicles (AGV) multi-load task assignment. The primary goal of this paper is to determine the factors needed to optimize material transportation system. This study also explores the optimization and performance enhancement of the Flexible Manufacturing System (FMS) environment. The implementation of Genetic Algorithm (GA) in this model is to obtain the optimal solution for FMS layout. The combination of delivery and pickup task are addressed by modified algorithm for advancement in multiple loads AGV. The result obtained depicts that the proposed task assignment method with a modified genetic algorithm can produce acceptable performance compared to conventional task assignment method.

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Section: Problem Backgroundmentioning

confidence: 99%

Section: Problem Backgroundmentioning

confidence: 99%

Optimization of Material Transportation Assignment for Automated Guided Vehicle (AGV) System

Mohamad¹,

Fauadi²,

Zainudin³

et al. 2018

IJET

View full text Add to dashboard Cite

show abstract

“…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]. With the development of green manufacturing, studies have been done for a green workshop by considering the energy consumption of the machines, which is also an important running Sustainability 2019, 11, 1329 3 of 24 cost [34][35][36].…”

Section: Introductionmentioning

confidence: 99%

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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“…The applications of AGV systems face several important issues: AGVs number determining [25–27], path planning [28–30] and constraint exerting [31–33], etc.…”

Section: Introductionmentioning

confidence: 99%

Multi-AGV path planning with double-path constraints by using an improved genetic algorithm

et al. 2017

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This paper investigates an improved genetic algorithm on multiple automated guided vehicle (multi-AGV) path planning. The innovations embody in two aspects. First, three-exchange crossover heuristic operators are used to produce more optimal offsprings for getting more information than with the traditional two-exchange crossover heuristic operators in the improved genetic algorithm. Second, double-path constraints of both minimizing the total path distance of all AGVs and minimizing single path distances of each AGV are exerted, gaining the optimal shortest total path distance. The simulation results show that the total path distance of all AGVs and the longest single AGV path distance are shortened by using the improved genetic algorithm.

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Integrated tasks assignment and routing for the estimation of the optimal number of AGVS

Cited by 60 publications

References 23 publications

Optimization of Material Transportation Assignment for Automated Guided Vehicle (AGV) System

Optimization of Material Transportation Assignment for Automated Guided Vehicle (AGV) System

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

Multi-AGV path planning with double-path constraints by using an improved genetic algorithm

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