A Clonal Selection Algorithm for Minimizing Distance Travel and Back Tracking of Automatic Guided Vehicles in Flexible Manufacturing System

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

doi:10.1007/s40032-018-0447-5

Cited by 17 publications

(12 citation statements)

References 28 publications

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“…The appropriate selection and application of the material handling system's equipment is a strategic decision (Sen et al, 2017). Chanda et al (2018) and Chawla et al (2018aChawla et al ( , 2018bChawla et al ( , 2018d) applied the modified memetic particle swarm optimization (MMPSO) algorithm, clonal selection (CS) algorithm and grey wolf optimization (GWO) algorithm for the simultaneous scheduling of AGVs and optimization of AGVs fleet size in the FMS. Angra et al (2018) evaluated the performance of different priority dispatching rules when applied to multi-load AGVs in variable sized FMS configurations.…”

Section: Literature Reviewmentioning

confidence: 99%

The scheduling of automatic guided vehicles for the workload balancing and travel time minimi-zation in the flexible manufacturing system by the nature-inspired algorithm

Chawla¹,

Chanda²,

Angra³

2019

10.5267/j.jpm

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The real-time scheduling of automatic guided vehicles (AGVs) in flexible manufacturing system (FMS) is observed to be highly critical and complex due to the dynamic variations of production requirements such as an imbalance of AGVs loading, the high travel time of AGVs, variation in jobs, and AGV routes to name a few. The output from FMS considerably depends on the efficient scheduling of AGVs in the FMS. The multi-objective scheduling decisions for AGVs by nature inspired algorithms yield a considerable reduction throughput time in the FMS. In this paper, investigations are carried out for the multi-objective scheduling of AGVs to simultaneously balance the workload of AGVs and to minimize the travel time of AGVs in the FMS. The multi-objective scheduling is carried out by the application of nature-inspired grey wolf optimization algorithm (GWO) to yield a balanced work-load for AGVs and also to minimize the travel time of AGVs simultaneously in the FMS. The output yield of the GWO algorithm is compared with the results of benchmark problems from the literature. The resulting yield of the proposed algorithm for the multi-objective scheduling of AGVs is observed to outperform the existing algorithms for scheduling of AGVs.

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Section: Literature Reviewmentioning

confidence: 99%

The scheduling of automatic guided vehicles for the workload balancing and travel time minimi-zation in the flexible manufacturing system by the nature-inspired algorithm

Chawla¹,

Chanda²,

Angra³

2019

10.5267/j.jpm

Self Cite

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“…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]. The selection and assignment of jobs to AGVs and work centers are significant tasks and should be carried out after considering potential conflict and deadlocks while transferring the materials by the AGVs [13][14][15].…”

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%

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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A Clonal Selection Algorithm for Minimizing Distance Travel and Back Tracking of Automatic Guided Vehicles in Flexible Manufacturing System

Cited by 17 publications

References 28 publications

The scheduling of automatic guided vehicles for the workload balancing and travel time minimi-zation in the flexible manufacturing system by the nature-inspired algorithm

The scheduling of automatic guided vehicles for the workload balancing and travel time minimi-zation in the flexible manufacturing system by the nature-inspired algorithm

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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