Multi-load AGVs scheduling by application of modified memetic particle swarm optimization algorithm

Chawla, V. K.; Chanda, Arindam Kumar; Angra, Surjit

doi:10.1007/s40430-018-1357-4

Cited by 23 publications

(15 citation statements)

References 26 publications

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“…It is noteworthy that the AGVs in this research work still pertain to the ordinary single-load type. However, multiple-load AGVs are becoming increasingly popular in modern factories, owing to their less traffic congestion, smaller fleet size, and increased system throughput [37,38]. Hence, the integrated design and solution process oriented to multipleload AGVs will attract great interest in the future research work.…”

Section: Discussionmentioning

confidence: 99%

A CEGA-Based Optimization Approach for Integrated Designing of a Unidirectional Guide-Path Network and Scheduling of AGVs

Xiao

Zeng

et al. 2020

Mathematical Problems in Engineering

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In the current industrial fields, automatic guided vehicles (AGVs) are widely employed to constitute the flexible manufacturing system (FMS), owing to their great advantages of routing flexibility and high efficiency. However, one main challenge lies in the coupling process of the design problem of the unidirectional guide-path network (UGN) and the task scheduling problem of AGVs. To reduce the complexity, most pertinent literatures only handle these problems one by one, based on the stepwise design methods, thereby neglecting the constraint conditions and the optimization objectives caused by the FMS environment. The motivation of the paper is to bring the coupling factors into the integrated design and solution process. Firstly, an integrated design model of designing UGN and scheduling AGVs simultaneously is proposed, with the objective of minimizing the makespan (i.e., the maximum completion time of all handling tasks), in the consideration of the practical constraints, e.g., the job handling and processing sequence constraints and the AGV number constraint. Secondly, a dual-population collaborative evolutionary genetic algorithm (CEGA) is developed to solve the problems of designing and scheduling in a parallel way. The solutions of the integrated model, i.e., the potential strongly connected UGN and the feasible processing and handling sequence, are, respectively, coded as two different subpopulations with independent and concurrent evolution processes. The neighbourhood search operation, the niche technique, and the elitism strategy are combined to improve the convergence speed and maintain the population diversity. The experimental results show that the integrated design model can formulate the problem more accurately, and the CEGA algorithm is computationally efficient with high solution quality.

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Section: Discussionmentioning

confidence: 99%

A CEGA-Based Optimization Approach for Integrated Designing of a Unidirectional Guide-Path Network and Scheduling of AGVs

Xiao

Zeng

et al. 2020

Mathematical Problems in Engineering

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“…The application of AGVs can be widely observed for sustainable material handling operation in the FMS. The use of AGVs increases throughput and lowers makespan in the FMS [5,[8][9][10]. The performance of AGVs for material handling operations significantly depends on the appropriate selection of AGV flow path between pickup and delivery points of various work centers in the FMS [6,8,11,12].…”

Section: Literature Reviewmentioning

confidence: 99%

“…An analytical solution procedure for the loading and unloading problem of FMS with minimum computational time was proposed by Singh and Khan [34]. Simultaneous development of schedules for AGVs and FMS under different heuristic algorithms, namely clonal selection algorithm (CSA), modified memetic particle swarm optimization (MMPSO) algorithm and gray wolf optimization (GWO) algorithm, is attempted by the Chawla et al [5][6][7] and Chanda et al [9] and also optimized the AGV fleet size for the different FMS configurations. The output of AGVs in the FMS while transferring material from one work center to others under different conventional priority dispatching rules was compared by the Angra et al [8].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In order to achieve high throughput and low makespan from the FMS, appropriate measures must be taken to improve performance and efficiency of AGVs which are employed for material handling activities in the FMS. The integrated scheduling can be performed between production schedule and material handling schedule so as to simultaneously minimize the value of distance travel and backtracking of AGV in the FMS [5][6][7]9]. The authors applied nature-inspired algorithms and PHDRs in their research work, namely modified memetic particle swarm optimization [5], clonal selection algorithm [6] and gray wolf optimization [7], to integrate production schedule with material handling schedule in the FMS.…”

Section: Introductionmentioning

confidence: 99%

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Effect of nature-inspired algorithms and hybrid dispatching rules on the performance of automatic guided vehicles in the flexible manufacturing system

Chawla

Chanda

Angra

et al. 2019

J Braz. Soc. Mech. Sci. Eng.

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The application of nature-inspired algorithms and priority hybrid dispatching rules for simultaneous scheduling and dispatching of automatic guided vehicles (AGVs) are observed to be highly significant for the best utilization of the flexible manufacturing system (FMS) facility. The earlier studies on the use of AGVs in the FMS have generally focused on minimizing the complexity of AGV operations by optimizing their material handling schedule and their routing in different types of FMS configurations. However, this is achieved only by using an appropriate optimizing algorithm and dispatching rule under different FMS operating conditions. The aim of this paper is to use the simulation methodology so as to compare and analyze the combined effect of four experimental factors, namely four types of priority hybrid dispatching rules, three different nature-inspired algorithms, two levels of loading/unloading times and two levels of machine failures, on the different performance parameters of the FMS. The performance parameters of the FMS analyzed are simultaneous minimization in distance travel and backtracking of AGV, the total production rate of the FMS, mean AGV utilization and mean work center utilization in the FMS. Additionally, in order to find the mean and interaction effect of the experimental factors on the aforementioned performance parameters of the FMS, an analysis of variance (ANOVA) is also carried out. From the results, it is observed that the interaction between experimental factors, namely nature-inspired algorithms and loading-unloading time, has a significant effect on the performance measures, namely simultaneous reduction in distance travel and backtracking, mean work center utilization (%) and total production rate of the FMS. The interaction between aforesaid experimental factors has no significant effect on mean AGV utilization (%) in the FMS. However, the interaction between priority hybrid dispatching rules and loading/unloading times is found to have significant effect on the mean work center utilization (%) in the FMS facility.

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“…In the present era of technology, the infusion of machine learning algorithms into different machine tools has emerged as an innovative way to minimize the human involvement for different machining operations and also to improve accuracy and productivity in various machining operations. The use of machine learning algorithms into the machine tools promotes the development of artificial intelligence into the different machines by experienced-based learning (Sadjadi & Makui, 2002;Sadrabadi & Sadjadi, 2009;Moghaddam et al, 2012;Angra et al, 2018;Balic et al, 2006;Chanda et al, 2018;Deb et al, 2006;Chawla et al, 2017;Chawla et al, 2018aChawla et al, , 2018bChawla et al, , 2018cChawla et al, , 2018dChawla et al, , 2019aChawla et al, , 2019bChawla et al, , 2019cChawla et al, , 2019d. Commonly the machine learning algorithm is provided with some data sets or machining programs which are used for training of the algorithm (Warwick, 2013;Russell & Norvig, 2016).…”

Section: Introductionmentioning

confidence: 99%

Comparison of machine learning algorithms for the automatic programming of computer numerical control machine

Sharma¹,

Chawla²,

Ram³

2020

10.5267/j.ijdns

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The computer numerical control (CNC) machines are chiefly used for the production of jobs with high accuracy and high speed. The CNC machining centers perform the machining operations according to the given program instructions which are commonly programmed by a CNC programmer. In this paper, a procedure to develop an automatic CNC program for machining of different types of holes by using different machine learning algorithms is developed. The machine learning algorithms namely support vector machine (SVM) and restricted boltzmann machine algorithm (RBM) with deep belief network (DBN) are used for the automatic development of CNC machining programs of different types of holes. Initially, the position and other parameters of machining operations are identified and thereafter the CNC machining program is developed by using the MATLAB application. The automatically developed CNC programs are tested on a CNC simulator. It is found that the application of RBM machine learning algorithm with DBN outperforms the SVM machine learning algorithm for the development of automatic CNC machining program for the machining of blind holes, through holes, counterbores and countersink operations.

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Multi-load AGVs scheduling by application of modified memetic particle swarm optimization algorithm

Cited by 23 publications

References 26 publications

A CEGA-Based Optimization Approach for Integrated Designing of a Unidirectional Guide-Path Network and Scheduling of AGVs

A CEGA-Based Optimization Approach for Integrated Designing of a Unidirectional Guide-Path Network and Scheduling of AGVs

Effect of nature-inspired algorithms and hybrid dispatching rules on the performance of automatic guided vehicles in the flexible manufacturing system

Comparison of machine learning algorithms for the automatic programming of computer numerical control machine

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