A state of the art review of intelligent scheduling

Zarandi, M. H. Fazel; Asl, Ali Akbar Sadat; Sotudian, Shahabeddin; Castillo, Oscar

doi:10.1007/s10462-018-9667-6

Cited by 78 publications

(34 citation statements)

References 432 publications

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“…Zadeh introduced the FST which utilises fuzzy numbers number which is more suitable for addressing problems that are very complex and not well defined than the crisp number [22,23]. The FST is the most important tool in modelling uncertainty and the area of production management it has aided research [24]. The tool has also made life easier today due to its ability in using the linguistic variables to model human reasoning thereby providing the solution to the problem in the past without a satisfactory solution [25].…”

Section: Fuzzy Topsis Methodsmentioning

confidence: 99%

FUZZY TOPSIS Application in Materials Analysis for Economic Production of Cashew Juice Extractor

Emovon

Aibuedefe

2020

Fuzzy Information and Engineering

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In this paper, a Multi-Criteria Decision-Making (MCDM) tool which combines Fuzzy Set Theory (FST) with TOPSIS (Fuzzy TOPSIS) is presented for selecting optimal material for the different components of the cashew juice extractor. The technique proposed utilise a broad multiple criteria methodology in finding optimal material from among alternative materials. The alternative materials are mild steel, stainless steel, galvanised steel, and alloy steel. To illustrate the applicability of the technique, a case study of the Auger material selection problem was used. The Auger was applied for the demonstration of the proposed method because it is the most critical component of the cashew juice extractor. The result of the analysis indicated that galvanised steel is the optimal material for the Auger. To validate the FUZZY TOPSIS method, the results obtained from it were compared with results obtained from FUZZY MOORA and FUZZY SAW methods. The comparative analysis indicated that FUZZY TOPSIS produces completely same result with the FUZZY SAW method and very similar results with the FUZZY MOORA method. This is an indication of the suitability of the proposed technique in resolving the material selection problem of the cashew juice extractor.

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Section: Fuzzy Topsis Methodsmentioning

confidence: 99%

FUZZY TOPSIS Application in Materials Analysis for Economic Production of Cashew Juice Extractor

Emovon

Aibuedefe

2020

Fuzzy Information and Engineering

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“…, 9, being excluded. For this numerical example, the results of the algorithm for a fixed pair (6,8) are presented. For the other pairs, a similar process should be repeated.…”

Section: Fuzzy Examplementioning

confidence: 99%

“…Nowadays, the application of robots in manufacturing systems is becoming an interesting area of research. A robotic cell consists of an input station, several machines arranged in series, an output station, and one or more robots for handling the parts between the stations and machines [6]. Since the robotic cell scheduling problem is an NP-hard problem, enumeration of all possible solutions is compu-the steps of the proposed fuzzy Gilmore and Gomory algorithm.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Gilmore and Gomory algorithm: Application in robotic flow shops with the effects of job-dependent transportation and set-ups

2021

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This paper addresses the scheduling of robotic cells with job-dependent transportation and set-up effects with fuzzy methodology. Since transportation and set-up times are a large portion of the production time in a flexible manufacturing cell, ignoring these parameters may cause significant errors in determining the optimal makespan. Furthermore, determining the exact values of these time parameters is a challenging task. To overcome this problem, we represent these parameters using fuzzy L-R numbers. Using the capability of fuzzy numbers to model approximate values, we can represent these parameters without losing valuable information. For generating the optimal part sequencing in the cells, the Gilmore and Gomory algorithm is modified, and instead, a fuzzy Gilmore and Gomory algorithm is developed. We compare the results of the proposed fuzzy method with those of crisp ones. The results indicate the superiority of the proposed algorithm in terms of robustness, flexibility, and reduction of makespan.

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“…Various studies that addressed scheduling problems based on machine learning have been carried out to overcome the drawback of rule-based methods [13]. Foo and Takefuji [14] first proposed to use Hopfield model to solve job shop scheduling problem, and then a large number of researchers conducted improvement studies.…”

Section: Introductionmentioning

confidence: 99%