A generic two-stage fuzzy inference system for dynamic prioritization of customers

MahmoumGonbadi, Azar; Katebi, Yalda; Doniavi, Ali

doi:10.1016/j.eswa.2019.04.059

Cited by 19 publications

(9 citation statements)

References 20 publications

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“…Therefore, the fuzzy expert system has acceptable accuracy in detecting the rice weed and it was successful. The findings of the present study were in line with Ergun hatir (2020), Hāmedān et al, (2020), Hosseini Galezan et al, (2020), Troussas, ChrysaÞadi & Virvou (2019), Vama, Sudheer, & Ghaubey (2019 and Mahmoom Gonbadi, Kātebi, & Donyāvi (2019). All of them emphasized on the successful performance of the fuzzy expert system in detection and they considered it as valid and usable.…”

Section: Discussionsupporting

confidence: 89%

A Fuzzy Delphi Based Inference System for Detecting and Controlling Rice Weeds

Nafchi¹,

Asemi²,

Tahaei³

2021

Preprint

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In this research, the purpose was to design a fuzzy expert system based on fuzzy delphi method to detect and control the rice weed. The statistical population was elites and experts with regard to the science, experience and field of activity; 15 experts were selected as the sample. Two questionnaires were used to design the desired fuzzy expert: i) Fuzzy Delphi Technique Weed Detection Questionnaire, ii) Delphi Technique Weed Control Questionnaire. The design of the desired expert system was done with MATLAB software and the fuzzy logic tool box. That is, after obtaining an appropriate range of factors, through attributing the fuzzy trapezoidal membership functions to these ranges and generating the input functions, designing the rule base of this system and combining the output results of each factor, a system was designed whose input was the weed factor and the output was scores assigned to weeds. MATLAB guide was also used to design the graphical user interface. Then, for validation the designed system was tested. The answers of system and individual expert were then analyzed using paired t-test. Root Mean Square Error and Middle Absolute Value Deviation tests were used to calculate the system errors. The results were 0.12 and 0.01, respectively. This indicates that the designed fuzzy expert system has sufficient accuracy. Finally, given that all but two of the examined rules are the same as the diagnosis of an individual expert, then in 94% of the cases, the diagnosis of the system is the same as the diagnosis of an individual expert.

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Section: Discussionsupporting

confidence: 89%

A Fuzzy Delphi Based Inference System for Detecting and Controlling Rice Weeds

Nafchi¹,

Asemi²,

Tahaei³

2021

Preprint

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“…The efficiency of clustering in reducing the complexity of logistics networks and improving computational efficiency has well been demonstrated when dealing with large-scale logistics network optimization [49,50]. K-means and multidimensional clusterings have been used to classify customers on the basis of multiple characteristics and obtain initial solutions for optimal vehicle routes [51,52]. The 3D K-means clustering approach adopted in this paper focuses on reassigning each customer to their new nearest depot in accordance with the geographic coordinates and time windows, and generates the initial population of optimized vehicle routes for the next multiobjective optimization approach.…”

Section: Three-dimensional (3d) Clusteringmentioning

confidence: 99%

Collaboration and Resource Sharing in the Multidepot Multiperiod Vehicle Routing Problem with Pickups and Deliveries

Wang

Guan

et al. 2020

Sustainability

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In this work, a multidepot multiperiod vehicle routing problem with pickups and deliveries (MDPVRPPD) is solved by optimizing logistics networks with collaboration and resource sharing among logistics service providers. The optimal solution can satisfy customer demands with periodic time characteristics and incorporate pickup and delivery services with maximum resource utilization. A collaborative mechanism is developed to rearrange both the open and closed vehicle routes among multiple pickup and delivery centers with improved transportation efficiency and reduced operational costs. The effects of resource sharing strategies combining customer information sharing, facility service sharing, and vehicle sharing are investigated across multiple service periods to maximize resource utilization and refine the resource configuration. A multiobjective optimization model is developed to formulate the MDPVRPPD so that the minimum total operational costs, waiting time, and the number of vehicles are obtained. A hybrid heuristic algorithm incorporating a 3D clustering and an improved multiobjective particle swarm optimization (IMOPSO) algorithm is introduced to solve the MDPVRPPD and find Pareto optimal solutions. The proposed hybrid heuristic algorithm is based on a selective exchange mechanism that enhances local and global searching capabilities. Results demonstrate that the proposed IMOPSO outperforms other existing algorithms. We also study profit allocation issues to quantify the stability and sustainability of long-term collaboration among logistics participants, using the minimum costs remaining savings method. The proposed model and solution methods are validated by conducting an empirical study of a real system in Chongqing City, China. This study contributes to the development of efficient urban logistics distribution systems, and facilitates the expansion of intelligent and sustainable supply chains.

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“…These labels were defined by considering paired scales, rounding the arithmetic mean from the values assigned by the experts to give 3 labels [low (L), medium (M), high (H)] for the input variables, and 5 labels for the final evaluation [very low (MB), low (L), medium (M), high (H), very high (VH)]. The semantic representation of the labels was associated with the trapezoidal fuzzy numbers, as they were sufficiently robust to represent the vagueness of the linguistic evaluations provided by primary and secondary research sources of information [27]. In addition, this type of fuzzy partition demonstrates better results in terms of understanding and satisfaction of significant semantic restrictions, such as distinction, normalization, coverage and superposition.…”

Section: Plos Onementioning

confidence: 99%

Evaluation of educational quality performance on virtual campuses using fuzzy inference systems

et al. 2020

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The general objective of this study is to analyze student satisfaction with the use of virtual campuses in university teaching in order to discover the main variables influencing the overall online teaching-learning process that give quality to the virtual educational process. To this end, an ex-post-facto research methodology was applied to 1084 university students, who completed an ad hoc designed questionnaire, which allowed us to carry out descriptive analysis, classification trees and fuzzy inference systems using SPSS and Matlab software. The results suggest that four variables predominantly influence the quality of the teachinglearning processes in virtual campuses: satisfactory teacher responses to student questions and observations, the positive attitude of teachers towards the use of information and communication technologies, students having appropriate digital skills, and activities that encourage ideas and debate.

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A generic two-stage fuzzy inference system for dynamic prioritization of customers

Cited by 19 publications

References 20 publications

A Fuzzy Delphi Based Inference System for Detecting and Controlling Rice Weeds

A Fuzzy Delphi Based Inference System for Detecting and Controlling Rice Weeds

Collaboration and Resource Sharing in the Multidepot Multiperiod Vehicle Routing Problem with Pickups and Deliveries

Evaluation of educational quality performance on virtual campuses using fuzzy inference systems

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