An Enhanced Two-Phase Fuzzy Programming Model for Multi-Objective Supplier Selection Problem

Fatrias, Dicky; Shimizu, Yoshiaki

doi:10.7232/iems.2012.11.1.001

Cited by 3 publications

(2 citation statements)

References 25 publications

(13 reference statements)

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“…The selection of the best values for the hyperparameters used in the models was performed using the GridsearchCV technique (Narzisi 2008;Friedrichs and Igel 2005) with k-fold = 3 cross-validation (Burman 1989), as it is the most commonly used approach for hyperparameter selection in the literature (Faceli et al 2011).…”

Section: Models Used For Lwd Estimationmentioning

confidence: 99%

Estimation of Leaf Wetness Duration Using Machine Learning Models

Silva,

Santos,

Rolim

et al. 2024

Preprint

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The leaf wetness duration (LWD) is one of the most critical parameters related to the infection rate and development of plant diseases, as many pathogens require the presence of free water on plant organs to infect leaf tissue. For this reason, this study evaluated three machine learning models: Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network Multilayer Perceptron (MLP), using hourly surface meteorological inputs to estimate LWP. The models were trained and tested using 20 years of meteorological data for seven agricultural locations situated in the southern region of the state of Minas Gerais and the Triângulo Mineiro region. The models were compared with an empirical model based on observed relative humidity (NHUR>90%). The results indicated that machine learning models are capable of estimating LWD; however, among all methods tested, the MLP model demonstrated the best performance, with high accuracy (mean R² = 0.98) and low errors (mean RMSE = 27.6 minutes and mean MAE = 19.8 minutes). The results also showed that the models are sensitive to the study locations, with models for the southern Minas Gerais locations performing better than those for the Triângulo Mineiro region. This discrepancy is due to the lower LWD in the Triângulo Mineiro locations, which were underestimated by the models, resulting in lower performance.

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Section: Models Used For Lwd Estimationmentioning

confidence: 99%

Estimation of Leaf Wetness Duration Using Machine Learning Models

Silva,

Santos,

Rolim

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…So far, the domestic research results about this little, mostly review article, the literature [4] proposed a genetic algorithm for solving positioning line, a two-phase heuristic algorithm is proposed in literature [5]. To solve the problem in the logistics distribution service level, low quality, control the logistics cost, influence the urban traffic problem, this paper discussed the logistics distribution center of the path optimization problem, the corresponding mathematical model is established, and combined with simulated annealing algorithm to solve the shortage of the genetic algorithm in solving such problems, make the vehicle route problem get ideal solution, solve the problem of city logistics distribution optimization scheduling [6].…”

Section: Introductionmentioning

confidence: 99%

The Research on Mutil-Objective Location Routing Problem Based on Genetic Simulated Annealing Algorithm

Du¹,

Xue²

2014

AMM

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This paper analysis the basic principles of the genetic algorithm (GA) and simulated annealing algorithm (SA) thoroughly. According to the characteristics of mutil-objective location routing problem, the paper designs the hybrid genetic algorithm in various components, and simulate achieved the GSAA (Genetic Simulated Annealing Algorithm).Which architecture makes it possible to search the solution space easily and effectively without overpass computation. It avoids effectively the defects of premature convergence in traditional genetic algorithm, and enhances the algorithms global convergence. Also it improves the algorithms convergence rate to some extent by using the accelerating fitness function. Still, after comparing with GA and SA, the results show that the proposed Genetic Simulated Annealing Algorithm has better search ability. And the emulation experiments show that this method is valid and practicable.

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Innovative Multi-Criteria Decision-Making Approach for Supplier Evaluation: Combining TLF, Fuzzy BWM, and VIKOR

Arief,

Fatrias,

Jie

et al. 2023

J. Optimasi Sist. Ind.

Self Cite

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When confronted with underperforming suppliers, the need to evaluate and improve supplier performance becomes apparent. However, the inherent inaccuracies in information introduce complexity, especially when subjective human judgment is involved in the supplier evaluation process. Associated with such problem, this study presents a novel methodology for supplier performance evaluation in the crumb rubber industry, integrating the Taguchi Loss Function (TLF), fuzzy Best-Worst Method (BWM), and VIKOR technique in group decision-making environment. Aimed at addressing the challenges in industries with variable supplier quality and performance, such as the crumb rubber industry in Indonesia, the methodology was empirically tested to demonstrate its practical utility. The process involved identifying evaluation criteria through literature review tailored to the needs of decision makers (DMs), applying TLF to quantify losses from supplier performance deviations, using fuzzy BWM to determine criteria weights based on the DMs judgment, and employing the VIKOR technique for comprehensive supplier ranking. The findings underscore the methodology's effectiveness in enhancing decision-making, offering a unified metric that accommodates diverse criteria and balances precise data with subjective assessments. This approach simplifies the evaluation process, particularly in situations with conflicting interests among decision-makers. Demonstrating its practical application in the crumb rubber industry, the study highlights the methodology's potential for broader industrial applicability. Future research could explore comparative analyses with other analytical methods, further establishing the methodology's robustness and adaptability in different management contexts.

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An Enhanced Two-Phase Fuzzy Programming Model for Multi-Objective Supplier Selection Problem

Cited by 3 publications

References 25 publications

Estimation of Leaf Wetness Duration Using Machine Learning Models

Estimation of Leaf Wetness Duration Using Machine Learning Models

The Research on Mutil-Objective Location Routing Problem Based on Genetic Simulated Annealing Algorithm

Innovative Multi-Criteria Decision-Making Approach for Supplier Evaluation: Combining TLF, Fuzzy BWM, and VIKOR

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