Use of neural networks to achieve dynamic task allocation: a flexible manufacturing system example

Lin, Ding-Yu; Hwang, Sheue‐Ling

doi:10.1016/s0169-8141(98)00037-7

Cited by 13 publications

(5 citation statements)

References 20 publications

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“…The back propagation network (BPN) is a commonly used ANN structure and has been successfully applied for system modelling, prediction, and classification (Lin and Hwang, 1999). It is therefore adopted to estimate the MDSG for each machine cluster, and the positive or negative sign of the MDSG determines the approach to be assigned to the machine cluster.…”

Section: Solving Cluster Scheduling Problems and Solution Integrationmentioning

confidence: 99%

Flexible flow shop scheduling with stochastic processing times: A decomposition-based approach

Choi

Wang

2012

Computers & Industrial Engineering

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Flexible flow shop scheduling problems are NP-hard and tend to become more complex when stochastic uncertainties are taken into consideration. Although some methods have been developed to address such problems, they remain inherently difficult to solve by any single approach. This paper presents a novel decomposition-based approach (DBA), which combines both the Shortest Processing Time (SPT) and the Genetic Algorithm (GA), to minimizing the makespan of a flexible flow shop (FFS) with stochastic processing times. In the proposed DBA, a neighbouring K-means clustering algorithm is developed to firstly group the machines of an FFS into an appropriate number of machine clusters, based on their stochastic nature. Two optimal back propagation networks (BPN), corresponding to the scenarios of simultaneous and non-simultaneous job arrivals, are then selectively adopted to assign either SPT or GA to each machine cluster for sub-schedule generation. Finally, an overall schedule is generated by integrating the sub-schedules of machine clusters.Computation results show that the DBA outperforms SPT and GA alone for FFS scheduling with stochastic processing times.

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Section: Solving Cluster Scheduling Problems and Solution Integrationmentioning

confidence: 99%

Flexible flow shop scheduling with stochastic processing times: A decomposition-based approach

Choi

Wang

2012

Computers & Industrial Engineering

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“…The back propagation network (BPN) has been successfully applied for system modelling, prediction, and classification due to its capability of identifying complex nonlinear relationships between input and output [13]. It is therefore adopted to estimate the MDSG for each machine cluster, and the positive or negative sign of the MDSG determines the approach to be assigned to the machine cluster.…”

Section: B Approach Assignmentmentioning

confidence: 99%

A decomposition-based approach to flexible flow shop scheduling under stochastic setup times

Wang

Choi

2010

2010 5th IEEE International Conference Intelligent Systems

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Research on production scheduling under uncertainty has recently received much attention. This paper presents a novel decomposition-based approach (DBA) to flexible flow shop (FFS) scheduling under stochastic setup times. In comparison with traditional methods using a single approach, the proposed DBA combines and takes advantage of two different approaches, namely the Genetic Algorithm (GA) and the Shortest Processing Time Algorithm (SPT), to deal with uncertainty. A neighbouring K-means clustering algorithm is developed to firstly decompose an FFS into an appropriate number of machine clusters. A back propagation network (BPN) is then adopted to assign either GA or SPT to generate a sub-schedule for each machine cluster. Finally, an overall schedule is generated by integrating the subschedules of the machine clusters. Computation results reveal that the DBA is superior to SPT and GA alone for FFS scheduling under stochastic setup times.

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“…(e.g., Karwowski, Jarvinen, and Zurada 1992;Arcand 1994;Jung and Park 1994;Lim, Fok, and Tan 1996;Harden, Crumpton, and Killough 1996;Zurada, Karwowski, and Marras 1997;Carnahan and Redfern 1998;Lin and Hwang 1999;Chen, Kaber, and Dempsey 2000;Chung, Lee, Inseok, Dohyung, and Sang 2002;Kiryu, Shibai, Hayashi, and Tanaka 2002;Kaya et al 2003;Lee, Karwowski, Marras, and Rodrick 2003;Chen, Kaber, and Dempsey 2004;Kolich, Seal, and Taboun 2004). Chen et al (2004) used neural networks for data classification problems.…”

Section: Literature On the Use Of Artificial Neural Network For Ergomentioning

confidence: 99%

“…Arcand (1994) examined neural networks as a tool for optimizing the placement of icons in an interface. Lin and Hwang (1999) used neural networks to achieve dynamic task allocation in a flexible manufacturing system. Chen et al (2000) modeled a neural network for prediction of musculoskeletal disorder risk such as low-back disorder risk associated with industrial jobs.…”

Section: Literature On the Use Of Artificial Neural Network For Ergomentioning

confidence: 99%

A Neural Network-Based System for Prediction of Computer User Comfort

Seçkiner

2009

Applied Artificial Intelligence

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This article proposes a neural network-based system for prediction of computer user comfort with respect to the existing settings of the workstation. In this context, anthropometric measures and the existing measures of a computer workstation were related to back-support comfort, distance comfort, keyboard comfort, monitor comfort, and seat comfort using two distinct modeling approaches-multiple linear regression and artificial neural network. The purpose of this article was to compare and contrast the resulting models. The data from 144 computer workstations were used and a total of 12 different data types such as shoulder to elbow, eye to buttock, pan height, monitor height, or distance from the chair were recorded. While multiple linear regression could not be used to adequately predict the computer workstation comfort, the neural network was deemed superior. This approach allows ergonomists to aid in the decisionmaking process of computer environment design and the prediction of the health risk in an occupational environment.Computer-related repetitive disorders have become one of the largest problems facing ergonomists and the medical community because it is developing in epidemic proportions within the occupational environment. In previous years, the prevalence of neck and upper extremity complaints among computer users is high (Tittiranonda, Burastero, and Rempel 1999;Flodgren, Heiden, Lyskov, and Crenshaw 2007). Studies have shown that factors such as repetitive and stereotype movements, fixed postures, static load, insufficient recovery time, muscular fatigue, and psychosocial stress increase the risk of developing musculoskeletal symptoms (Flodgren et al. 2007;Tittiranonda et al. 1999;Jensen 2003;Juul-Kristensen, Søgaard, Strøyer, and Jensen 2004).In the wake of the expanding use of computers, concerns have been expressed about their potential health effects. Researches have shown

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Use of neural networks to achieve dynamic task allocation: a flexible manufacturing system example

Cited by 13 publications

References 20 publications

Flexible flow shop scheduling with stochastic processing times: A decomposition-based approach

Flexible flow shop scheduling with stochastic processing times: A decomposition-based approach

A decomposition-based approach to flexible flow shop scheduling under stochastic setup times

A Neural Network-Based System for Prediction of Computer User Comfort

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