Application of fuzzy logic to shopfloor scheduling

Nahavandi, Saeid; Solomon, Pamela H.

doi:10.1109/annes.1995.499509

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…Studies about some extensive applications of the Grey Fuzzy Optimization Methodology have been thoroughly studied. Nahavandi et al (1995) illustrated the fuzzy logic toolbox to develop a system where processing time had fuzziness [10]. Job prioritization was done through fuzzy logic in the factory controller queue was carried out by choosing a slack time, waiting time of work order and external priority of a job as fuzzy variables.…”

Section: Literature Reviewmentioning

confidence: 99%

Study of Grey Fuzzy Optimization Techniques for FMS Scheduling Problem

Kaushik¹,

Deswal²,

Dudpuri³

et al. 2020

IJAPIE

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Deep The research study focuses on the concept of Fuzzy Logic and its applications in diverse sectors like Behavioural Science, Machining, Business Operations, Medicinal Science, Water Quality Management, Manufacturing, Anti-Braking systems, etc. Fuzzy Logic is regarded as the best option to deal with complex problems which arise due to uncertainty, incomplete information, and lack of preciseness. The major focus of this research is then divided into a case study on the application of Grey Fuzzy Optimization in the Manufacturing and Machining sectors, using the Fuzzy Logic Toolbox of MATLAB. The case study deals with a Job-Shop Scheduling Problem in a Flexible Manufacturing System (FMS). Scheduling of Jobs is a crucial task for meeting and exceeding the market demands. The decision of which part will be the next to be processed considers some criteria that influence the performance of a job shop system. The job shop system is taken from previous literature and the proposed approach is applied to get the optimal scheduling of products. Within the vast field of production and operation, its applicability was tested in scheduling conflicts in flexible manufacturing systems (FMS). All the output values were in the range of single-digit percentage error owing to different sets of rule formations but nonetheless, the proximity of the actual and calculated values reaffirmed the confidence that the verification was justified.

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Section: Literature Reviewmentioning

confidence: 99%

Study of Grey Fuzzy Optimization Techniques for FMS Scheduling Problem

Kaushik¹,

Deswal²,

Dudpuri³

et al. 2020

IJAPIE

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“…This weighted cost is allocated according to a set of rules that use fuzzy logic to determine the best a value and therefore the weight of the given node. This weighted cost is based on the concepts described in [16] and [17].…”

Section: Fuzzy Weighted Cost Decisionmentioning

confidence: 99%

Using Autonomous Mobile Agents for Efficient Data Collection in Sensor Networks

Black

Mak

Pathirana

et al. 2006

2006 World Automation Congress

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Sensor networks are emerging as the new frontier in sensing technology, however there are still issues that need to be addressed. Two such issues are data collection and energy conservation. We consider a mobile robot, or a mobile agent, traveling the network collecting information from the sensors themselves before their onboard memory storage buffers are full. A novel algorithm is presented that is an adaptation of a local search algorithm for a special case of the Asymmetric Traveling Salesman Problem with Time-windows (ATSPTW) for solving the dynamic scheduling problem of what nodes are to be visited so that the information collected is not lost. Our algorithms are given and compared to other work.

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“…Sih and Lee [16] introduced a new compile-time scheduling heuristic called declustering to solve multiprocessor scheduling. Of late, the integration of a fuzzy set and a neural network has been investigated to solve a combinatorial optimisation problem [17,18].…”

Section: Introductionmentioning

confidence: 99%

Multiprocessor Task Assignment with Fuzzy Hopfield Neural Network Clustering Technique

Chen

Huang

2001

Neural Computing & Applications

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Most scheduling applications have been demonstrated as NP-complete problems. A variety of schemes are introduced in solving those scheduling applications, such as linear programming, neural networks, and fuzzy logic. In this paper, a new approach of first analogising a scheduling problem to a clustering problem and then using a fuzzy Hopfield neural network clustering technique to solve the scheduling problem is proposed. This fuzzy Hopfield neural network algorithm integrates fuzzy c-means clustering strategies into a Hopfield neural network. This investigation utilises this new approach to demonstrate the feasibility of resolving a multiprocessor scheduling problem with no process migration and constrained times (execution time and deadline). Each process is regarded as a data sample, and every processor is taken as a cluster. Simulation results illustrate that imposing the fuzzy Hopfield neural network onto the proposed energy function provides an appropriate approach to solving this class of scheduling problem.

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Application of fuzzy logic to shopfloor scheduling

Cited by 7 publications

References 6 publications

Study of Grey Fuzzy Optimization Techniques for FMS Scheduling Problem

Study of Grey Fuzzy Optimization Techniques for FMS Scheduling Problem

Using Autonomous Mobile Agents for Efficient Data Collection in Sensor Networks

Multiprocessor Task Assignment with Fuzzy Hopfield Neural Network Clustering Technique

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