scholarly journals A Comprehensive Fuzzy Decision-Making Method for Minimizing Completion Time in Manufacturing Process in Supply Chains

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2919
Author(s):  
Fahad Kh. A.O.H. Alazemi ◽  
Mohd Khairol Anuar Bin Mohd Ariffin ◽  
Faizal Bin Mustapha ◽  
Eris Elianddy bin Supeni
Keyword(s):  
Supply Chains ◽  
Manufacturing Process ◽  
Completion Time ◽  
Fuzzy Inference ◽  
Fuzzy Topsis ◽  
Minimum Variance ◽  
Fuzzy Decision Making ◽  
Production Environment ◽  
Inference System ◽  
Fuzzy Weights

In manufacturing firms, there are many factors that can affect product completion time in production lines. However, in a real production environment, such factors are uncertain and increase the adverse effects on product completion time. This research focuses on the role of internal factors in small- and medium-scale supply chains in developing countries, enhancing product completion time during the manufacturing process in fuzzy conditions. In the first step of this research, a list of factors was found clustered into six main groups: technology, human resources, machinery, material, facility design, and social factors. In the next step, fuzzy weights of each group factor were determined by a fuzzy inference system to reflect the uncertainty of the factors in utilizing product completion time. Then, a hybrid fuzzy–TOPSIS-based heuristic is proposed to generate and select the best production alternative. The outcomes showed that the proposed method could generate and select the alternative with a 10.13% lower product completion time. The findings also indicated that using the proposed fuzzy method will cause less minimum variance compared to the crisp mode.

scholarly journals A fuzzy decision making system for building damage map creation using high resolution satellite imagery

2013 ◽  
Vol 13 (2) ◽  
pp. 455-472 ◽  
Author(s):  
H. Rastiveis ◽  
F. Samadzadegan ◽  
P. Reinartz
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Damage Assessment ◽  
Fuzzy Inference ◽  
Earthquake Damage ◽  
Fuzzy Decision Making ◽  
Inference System ◽  
High Resolution Satellite Imagery ◽  
Roof Part ◽  
Earthquake Damage Assessment

Abstract. Recent studies have shown high resolution satellite imagery to be a powerful data source for post-earthquake damage assessment of buildings. Manual interpretation of these images, while being a reliable method for finding damaged buildings, is a subjective and time-consuming endeavor, rendering it unviable at times of emergency. The present research, proposes a new state-of-the-art method for automatic damage assessment of buildings using high resolution satellite imagery. In this method, at the first step a set of pre-processing algorithms are performed on the images. Then, extracting a candidate building from both pre- and post-event images, the intact roof part after an earthquake is found. Afterwards, by considering the shape and other structural properties of this roof part with its pre-event condition in a fuzzy inference system, the rate of damage for each candidate building is estimated. The results obtained from evaluation of this algorithm using QuickBird images of the December 2003 Bam, Iran, earthquake prove the ability of this method for post-earthquake damage assessment of buildings.

STEREOVISION-BASED FUZZY OBSTACLE AVOIDANCE METHOD

2011 ◽  
Vol 08 (01) ◽  
pp. 169-183 ◽  
Author(s):  
LAZAROS NALPANTIDIS ◽  
ANTONIOS GASTERATOS
Keyword(s):  
Obstacle Avoidance ◽  
Efficient Solution ◽  
Fuzzy Inference ◽  
Autonomous Mobile Robots ◽  
Fuzzy Decision Making ◽  
Fuzzy Decision ◽  
Inference System ◽  
Depth Maps ◽  
Complex Processes ◽  
Stereo Algorithm

This work presents a stereovision-based obstacle avoidance method for autonomous mobile robots. The decision about the direction on each movement step is based on a fuzzy inference system. The proposed method provides an efficient solution that uses a minimum of sensors and avoids computationally complex processes. The only sensor required is a stereo camera. First, a custom stereo algorithm provides reliable depth maps of the environment in frame rates suitable for a robot to move autonomously. Then, a fuzzy decision making algorithm analyzes the depth maps and deduces the most appropriate direction for the robot to avoid any existing obstacles. The proposed methodology has been tested on a variety of self-captured outdoor images and the results are presented and discussed.

scholarly journals Integrating Sustainability in the Quality Assessment of EHEA Institutions: A Hybrid FDEMATEL-ANP-FIS Model

2020 ◽  
Vol 12 (5) ◽  
pp. 1707 ◽  
Author(s):  
Javier Puente ◽  
Isabel Fernandez ◽  
Alberto Gomez ◽  
Paolo Priore
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Quality Assessment ◽  
Conceptual Model ◽  
Fuzzy Inference ◽  
Multicriteria Decision Making ◽  
Analytic Network Process ◽  
Fuzzy Decision Making ◽  
Inference System ◽  
European Higher Education

This paper proposes the design of a conceptual model of quality assessment in European higher education institutions (HEIs) that takes into account some of the critical reflections made by certain authors in the literature regarding standards and guidelines suggested for this purpose by the European Higher Education Area (EHEA). In addition, the evaluation of the conceptual model was carried out by means of the reliable hybrid methodology MCDM-FIS (multicriteria decision making approach–fuzzy inference system) using FDEMATEL and FDANP methods (fuzzy decision-making trial and evaluation laboratory and FDEMATEL-based analytic network process). The choice of these methodologies was justified by the existing interrelationships among the criteria and dimensions of the model and the degree of subjectivity inherent in its evaluation processes. Finally, it is suggested to include sustainability as a determining factor in the university context due to its great relevance in the training of future professionals.

scholarly journals An Adaptive Handover Prediction Scheme for Seamless Mobility Based Wireless Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Ali Safa Sadiq ◽  
Norsheila Binti Fisal ◽  
Kayhan Zrar Ghafoor ◽  
Jaime Lloret
Keyword(s):  
Decision Making ◽  
Wireless Networks ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Weight Vector ◽  
Point Load ◽  
Fuzzy Decision Making ◽  
Inference System ◽  
Handover Decision ◽  
Seamless Mobility

We propose an adaptive handover prediction (AHP) scheme for seamless mobility based wireless networks. That is, the AHP scheme incorporates fuzzy logic with AP prediction process in order to lend cognitive capability to handover decision making. Selection metrics, including received signal strength, mobile node relative direction towards the access points in the vicinity, and access point load, are collected and considered inputs of the fuzzy decision making system in order to select the best preferable AP around WLANs. The obtained handover decision which is based on the calculated quality cost using fuzzy inference system is also based on adaptable coefficients instead of fixed coefficients. In other words, the mean and the standard deviation of the normalized network prediction metrics of fuzzy inference system, which are collected from available WLANs are obtained adaptively. Accordingly, they are applied as statistical information to adjust or adapt the coefficients of membership functions. In addition, we propose an adjustable weight vector concept for input metrics in order to cope with the continuous, unpredictable variation in their membership degrees. Furthermore, handover decisions are performed in each MN independently after knowing RSS, direction toward APs, and AP load. Finally, performance evaluation of the proposed scheme shows its superiority compared with representatives of the prediction approaches.

Sustainability Assessment Methodology for Concrete Manufacturing Process: A Fuzzy Inference System Approach

2013 ◽  
Vol 845 ◽  
pp. 814-818 ◽  
Author(s):  
Pouyan Rezvan ◽  
Amir Hossein Azadnia ◽  
Mohd Yusof Noordin ◽  
Seyed Navid Seyedi
Keyword(s):  
Portland Cement ◽  
Fuzzy Inference System ◽  
Manufacturing Process ◽  
Sustainability Assessment ◽  
Fuzzy Inference ◽  
Hybrid Approach ◽  
Three Dimensions ◽  
Manufacturing Processes ◽  
Slag Cement ◽  
Inference System

Sustainability assessment of concrete manufacturing processes has recently received great attention among scholars and practitioners. While most of the studies on sustainability assessment of concrete manufacturing processes focus on economic and environmental issues, those which consider all three dimensions of sustainability (social, economic, and environmental) simultaneously are rather limited. In this study, a hybrid approach of fuzzy inference system and analytical hierarchy process (AHP) is proposed in order to evaluate the sustainability level of concrete manufacturing processes based on Life Cycle Assessment (LCA) principals. AHP is applied to weight the selected sustainability elements and sub elements. Afterward, fuzzy inference system is used to evaluate the sustainability level of concrete manufacturing processes. The practicality and applicability of the proposed approach are examined by conducting sustainability assessments of four different concrete manufacturing processes: (1) 100% of Portland cement (2) 35 % slag cement and 65% Portland cement (3) 50% slag cement and 50% Portland cement (4) 20% fly ash and 80% Portland cement. The results disclose the more sustainable concrete manufacturing process which is 50 % of Slag cement and 50% Portland cement.

scholarly journals Proactive risk assessment via fuzzy approach in a decisional process of consignment stock program adoption

2021 ◽  
Vol 343 ◽  
pp. 07012
Author(s):  
Monica Faur ◽  
Constantin Bungău
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Supply Chains ◽  
Fuzzy Inference ◽  
Assessment Instrument ◽  
Fuzzy Approach ◽  
Inference System ◽  
Program Adoption ◽  
The Matrix ◽  
The Right

The idea of adopting the consignment stock concept has enriched the landscape of efficient supply chains and their organizations, due to its major benefits in reducing inventory, compressing delivery time and increasing flexibility towards achieving agility and enhanced market responsiveness. The decision making process is a complex one, as besides the benefits and the economical and administrative aspects, there are also risks that must be identified, measured, assessed and managed. There is little research in the literature concerning the risks and constraints of consignment inventory implementation, while consignment contracts are widely applied in both physical and virtual supply chains. This paper introduces a model of proactive risk assessment via a fuzzy approach, allowing a sensitivity analysis of the identified risks in the matrix, in terms of probability to happen, degree of severity, impact and potential consequences, as well as mitigation. A fuzzy inference system is used to serve as assessment instrument, to fairly and more rigorously evaluate the risks, in order to avoid critical situations during or after program adoption, or even implementation failure. Fuzzy logic theory has been chosen to capture the uncertainty that usually occurs when dealing with risks and decision making. We believe that having these risk assessment insights at hand, managers and practitioners can achieve a better understanding of the challenges that come along with a new consignment program adoption, while allowing them to make the right and justified decision, in accordance with both benefit and risk considerations.

A novel hybrid ICA-ANFIS model for prediction of manufacturing processes performance

2016 ◽  
Vol 231 (2) ◽  
pp. 181-190 ◽  
Author(s):  
Hamid Baseri ◽  
Moosa Belali-Owsia
Keyword(s):  
Manufacturing Process ◽  
Prediction Error ◽  
Fuzzy Inference ◽  
Manufacturing Processes ◽  
Combined Method ◽  
Anfis Model ◽  
Process Variability ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Imperialistic Competitive Algorithm

In order to reduce the manufacturing process variability and improve the production yield, it is important to predict the performance of manufacturing process. The adaptive neuro-fuzzy inference system (ANFIS) is a powerful network which can predict the output parameters of manufacturing process. However, design of ANFIS needs trial and errors to select the best structure. In this study, an imperialistic competitive algorithm has been used to determine the ANFIS architecture to reach minimum values of the prediction error. In order to evaluate the performance of this combined method, two illustrative examples of manufacturing processes have been used. Results indicated that the combined method has superiority in prediction of output, rather than previous developed ANFIS models and so it can be applied for modeling of the other manufacturing processes.

scholarly journals Optimal estimating the project completion time and diagnosing the fault in the project

DYNA ◽  
2016 ◽  
Vol 83 (195) ◽  
pp. 121-127 ◽  
Author(s):  
M. T. Hajali-Mohamad ◽  
M. R. Mosavi ◽  
K. Shahanaghi
Keyword(s):  
Fuzzy System ◽  
Prediction Error ◽  
Fuzzy Systems ◽  
Completion Time ◽  
Fuzzy Inference ◽  
Parallel Structure ◽  
Earned Value ◽  
Inference System ◽  
Total Cost ◽  
Project Completion

The main objective of the project management team is to implement the project taking into consideration the Budget, schedule and constraints. In addition, project accomplishment, especially with large projects, requires the project to be correctly envisaged. Earned value (EV) management is a valuable technique for analyzing and controlling the performance of the project and predicting the total cost before its completion. Thus, fuzzy systems such as Adaptive Network based on the Fuzzy Inference System (ANFIS) and Parallel Structure based on the Fuzzy System (PSFS) are used to predict the project completion time. In this paper, the plan value diagram is used to predict the earn value diagram using three methods. These three methods are based on the PSFS and Neural Networks (NNs), which help with the implementation of the projects in organizations. The results of these three methods decreased the prediction error of the EV diagram by up to 2%.

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