Journal of Intelligent & Fuzzy Systems - Volume 42, issue 1

Authors: Bezdan, Timea | Zivkovic, Miodrag | Bacanin, Nebojsa | Strumberger, Ivana | Tuba, Eva | Tuba, Milan

Article Type: Research Article

Abstract: Cloud computing represents relatively new paradigm of utilizing remote computing resources and is becoming increasingly important and popular technology, that supports on-demand (as needed) resource provisioning and releasing in almost real-time. Task scheduling has a crucial role in cloud computing and it represents one of the most challenging issues from this domain. Therefore, to establish more efficient resource employment, an effective and robust task allocation (scheduling) method is required. By using an efficient task scheduling algorithm, the overall performance and service quality, as well as end-users experience can be improved. As the number of tasks increases, the problem complexity rises …as well, which results in a huge search space. This kind of problem belongs to the class of NP-hard optimization challenges. The objective of this paper is to propose an approach that is able to find approximate (near-optimal) solution for multi-objective task scheduling problem in cloud environment, and at the same time to reduce the search time. In the proposed manuscript, we present a swarm-intelligence based approach, the hybridized bat algorithm, for multi-objective task scheduling. We conducted experiments on the CloudSim toolkit using standard parallel workloads and synthetic workloads. The obtained results are compared to other similar, metaheuristic-based techniques that were evaluated under the same conditions. Simulation results prove great potential of our proposed approach in this domain. Show more

Keywords: Cloud computing, task scheduling, multi-objective optimization, bat algorithm, hybridization

DOI: 10.3233/JIFS-219200

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 1, pp. 411-423, 2022

Authors: Omerali, Mete | Kaya, Tolga

Article Type: Research Article

Abstract: Digitalization is the key trend of the Industry 4.0 revolution. Industrial companies are transforming the way they design and maintain their products and solutions. The user requirements become more demanding. Competition among the manufacturing companies is at its limits and transforms the products to be more complex. Yet, other challenges such as faster time to market, higher quality requirements and legislation force enterprises to provide new ways of design, manufacture and service their end products. Product Lifecycle Management (PLM) is a key solution to track the entire lifespan of the product from idea to design, design to manufacture and manufacture …to service. Besides the complexity of products and production, the selection of the right PLM solution which will become the backbone of enterprises is an open problem. In this paper, a thorough literature review is conducted to analyze the most important features for selecting the right PLM solution for manufacturing firms. Moreover, to overcome the challenge of decision makers’ (DM) subjective judgments, a novel interval value spherical fuzzy COPRAS (IVSF-COPRAS) multi-criteria decision making (MCDM) method is introduced. The paper aims to help enterprises rapidly identify the best alternative vendor/solution to be selected based on the need of the organization. In order to show the applicability, DM inputs are collected from a leading defense company where the PLM selection process is ongoing. The industrial case study is provided to demonstrate the success of the proposed selection framework. Show more

Keywords: Digitalization, industry 4.0, product lifecycle management (PLM), multi-criteria decision making (MCDM), IVSF-COPRAS

DOI: 10.3233/JIFS-219201

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 1, pp. 425-438, 2022

Authors: Baysal, M. Emin | Sarucan, Ahmet | Büyüközkan, Kadir | Engin, Orhan

Article Type: Research Article

Abstract: The distributed permutation flow shop scheduling (DPFSS) is a permutation flow shop scheduling problem including the multi-factory environment. The processing times of the jobs in a real life scheduling problem cannot be precisely know because of the human factor. In this study, the process times and due dates of the jobs are considered triangular and trapezoidal fuzzy numbers for DPFSS environment. An artificial bee colony (ABC) algorithm is developed to solve the multi-objective distributed fuzzy permutation flow shop (DFPFS) problem. First, the proposed ABC algorithm is calibrated with the well-known DPFSS instances in the literature. Then, the DPFSS instances are …fuzzified and solved with the algorithm. According to the results, the proposed ABC algorithm performs well to solve the DFPFS problems. Show more

Keywords: Distributed fuzzy permutation flow-shop, artificial bee colony, multi-objective, fuzzy completion time, agreement index

DOI: 10.3233/JIFS-219202

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 1, pp. 439-449, 2022

Authors: Engin, Orhan | Yılmaz, Mustafa Kerim

Article Type: Research Article

Abstract: In the conventional scheduling problem, the parameters such as the processing time for each job and due dates are usually assumed to be known exactly, but in many real-world applications, these parameters may very dynamically due to human factors or operating faults. During the last decade, several works on scheduling problems have used a fuzzy approach including either uncertain or imprecise data. A fuzzy logic based tool for multi-objective Hybrid Flow-shop Scheduling with Multi-processor Tasks (HFSMT) problem is presented in this paper. In this study, HFSMT problems with a fuzzy processing time and a fuzzy due date are formulated, taking …Oğuz and Ercan’s benchmark problems in the literature into account. Fuzzy HFSMT problems are formulated by three-objectives: the first is to maximize the minimum agreement index and the second is to maximize the average agreement index, and the third is to minimize the maximum fuzzy completion time. An efficient genetic algorithm(GA) is proposed to solve the formulated fuzzy HFSMT problems. The feasibility and effectiveness of the proposed method are demonstrated by comparing it with the simulated annealing (SA) algorithm in the literature. Show more

Keywords: Hybrid flow shop scheduling, multi-processor tasks problems, fuzzy processing time, fuzzy due date, efficient genetic algorithm, simulated annealing

DOI: 10.3233/JIFS-219203

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 1, pp. 451-463, 2022

Authors: Bayturk, Engin | Esnaf, Sakir | Kucukdeniz, Tarik

Article Type: Research Article

Abstract: Facility location selection is a vital decision for companies that affects both cost and delivery time over the years. However, determination of the facility location is a NP-hard problem. A hybrid algorithm that combines revised weighted fuzzy c-means with Nelder Mead (RWFCM-NM) performs well when compared with well-known algorithms for the facility location problem (FLP) with deterministic customer demands and positions. The motivation of the study is both analyzing performance of the RWFCM-NM algorithm with probabilistic customer demands and positions and proposing a new approach for this problem. This paper develops two new algorithms for FLP when customer demands and …positions are probabilistic. The proposed algorithms are a probabilistic fuzzy c-means algorithm and Nelder-Mead (Probabilistic FCM-NM), a probabilistic revised weighted fuzzy c-means algorithm and Nelder Mead (Probabilistic RWFCM-NM) for the un-capacitated planar multi-facility location problem when customer positions and customer demands are probabilistic with predetermined service level. Proposed algorithms performances were tested with 13 data sets and comparisons were made with four well known algorithms. According to the experimental results, probabilistic RWFCM-NM algorithm demonstrates superiority on compared algorithms in terms of total transportation costs. Show more

Keywords: Multi-facility location problem, Nelder-Mead, probabilistic fuzzy c-means, probabilistic demand and position

DOI: 10.3233/JIFS-219204

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 1, pp. 465-475, 2022

Authors: Haktanır, Elif | Kahraman, Cengiz

Article Type: Research Article

Abstract: Process capability analysis (PCA) is a tool for measuring a process’s ability to meet specification limits (SLs), which the customers define. Process capability indices (PCIs) are used for establishing a relationship between SLs and the considered process’s ability to meet these limits as an index. PCA compares the output of a process with the SLs through these capability indices. If the customers’ needs contain vague or imprecise terms, the classical methods are inadequate to solve the problem. In such cases, the information can be processed by the fuzzy set theory. Recently, ordinary fuzzy sets have been extended to several new …types of fuzzy sets such as intuitionistic fuzzy sets, Pythagorean fuzzy sets, picture fuzzy sets, and spherical fuzzy sets. In this paper, a new extension of intuitionistic fuzzy sets, which is called penthagorean fuzzy sets, is proposed, and penthagorean fuzzy PCIs are developed. The design of production processes for COVID-19 has gained tremendous importance today. Surgical mask production and design have been chosen as the application area of the penthagorean fuzzy PCIs developed in this paper. PCA of the two machines used in surgical mask production has been handled under the penthagorean fuzzy environment. Show more

Keywords: Process capability analysis, process capability indices, penthagorean fuzzy sets, surgical mask, COVID-19

DOI: 10.3233/JIFS-219205

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 1, pp. 477-489, 2022

Authors: Tunc, Ali | Tasdemir, Sakir | Koklu, Murat | Cinar, Ahmet Cevahir

Article Type: Research Article

Abstract: Biometry is the science that enables living things to be distinguished by examining their physical and behavioral characteristics. The facial recognition system (FCS) is a kind of biometric system. FCS provides a unique mathematical model by determining the distance between the cheekbones, chin, nose, eyes, jawline, and similar positions using the facial features of the persons. Determining the gender and age group of chosen persons’ from face images is the main purpose of this study. It is targeted to distinguish the gender of the person and to obtain information about the person is children or adults by making essential works …on the images. Convolutional neural network (CNN) is one of the deep face recognition algorithms that widely used to recognize facial images. This study is suggested as a study that detects noise in images using the fuzzy logic-based filter method and classifies this cleared data by gender using the matrix completion and CNN. TensorFlow which is a machine learning library that used to train and tests deep learning methods is used for experiments. The customer photographs taken during using the system are transformed into a matrix expression through a system trained using this algorithm. The obtained results indicated that the offered technique detects age and gender with a 96% accuracy value and 1.145 seconds time. Show more

Keywords: Age classification, convolutional neural network, deep learning, fuzzy logic, gender classification

DOI: 10.3233/JIFS-219206

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 1, pp. 491-501, 2022

Authors: Namlı, Özge H. | Yanık, Seda | Nouri, Faranak | Serap Şengör, N. | Koyuncu, Yusuf Mertkan | Uçar, Ömer Berk

Article Type: Research Article

Abstract: In today’s competitive business environment, companies are striving to reduce costs and workload of call centers while improving customer satisfaction. In this study, a framework is presented that predicts and encourages taking proactive actions to solve customer problems before they lead to a call to the call center. Machine learning techniques are implemented and models are trained with a dataset which is collected from an internet service provider’s systems in order to detect internet connection problems of the customers proactively. Firstly, two classification techniques which are multi perceptron neural networks and radial basis neural networks are applied as supervised techniques …to classify whether the internet connection of customers is problematic or not. Then, by using unsupervised techniques, namely Kohonnen’s neural networks and Adaptive Resonance Theory neural networks, the same data set is clustered and the clusters are used for the customer problem prediction. The methods are then integrated with an ensemble technique bagging. Each method is implemented with bagging in order to obtain improvement on the estimation error and variation of the accuracy. Finally, the results of the methods applied for classification and clustering with and without bagging are evaluated with performance measures such as recall, accuracy and Davies-Bouldin index, respectively. Show more

Keywords: Call center problem prediction, classification, clustering, artificial neural networks, bagging

DOI: 10.3233/JIFS-219207

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 1, pp. 503-515, 2022

Authors: Cinar, Ulas | Cebi, Selcuk

Article Type: Research Article

Abstract: Risk management is the key factor to obtain safety in the working environment and its effectiveness increases with accuracy assessment and robust analysis. However, it is hard to succeed because of uncertainties in the working environment. Therefore, there are a lot of risk assessment methods in the literature to assess occupational health and safety risks. The traditional risk assessment methods handle each activity in the working environment separately and they do not consider the interactions among them. Furthermore, in these methods, potential outcomes of the risk parameters are considered based on the most possible outcome although there may be more …than one potential outcome. Differ from the traditional methods, The House of Safety method has been proposed to consider all potential outcomes and handle the interactions among the activities. In this study, an extension of The House of Safety is proposed to consider interactions among potential risks and to determine the most effective prevention method based on the potential risks. Hence, this extension provides an evaluation of the whole system. The proposed model has been developed by integrating Fuzzy Inference System (FIS), Fuzzy Analytical Hierarchy Process (FAHP), and DEMATEL into Quality Function Deployment (QFD). In this direction, FIS is used to determine activity-related probabilities, “FAHP” is utilized to identify all possible damage potentials of risks, and the DEMATEL is used to clarify interactions among risks. Finally, all information produced by these methods were aggregated to obtain total risk scores by using QFD. In addition, a second home has been created to link prevention and risks. Therefore, an effective prevention plan has been made to eliminate priority risks with all effective parameters. This stage provides the opportunity for optimum prevention plan against risk or risk groups dominating the system at the same time. In this study, unlike traditional methods including a partial risk assessment perspective, an integrated method that takes into account the risks on their own and the interactions between them is proposed in the literature, and the proposed approach has been applied to an open pit mine. Show more

Keywords: The house of safety, risk assessment method, occupational safety, fuzzy inference system, fuzzy AHP, DEMATEL, QFD

DOI: 10.3233/JIFS-219208

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 1, pp. 517-528, 2022

Authors: Aydın, Serhat | Yörükoğlu, Mehmet | Kabak, Mehmet

Article Type: Research Article

Abstract: The fourth party logistics (4PL) is an combiner that designs and implements the holistic supply chain solutions by using skills, knowledge, technology and resources of the service provider and its customer. A 4PL provider is also a technological service provider with eligible intellectual capital and the sufficient computer/software infrastructure. Defining the most appropriate 4PL service provider from the alternatives is not easy for companies, the solution can be addressed within the framework of the Multi-Criteria Decision Making (MCDM) problem, and subjective and uncertain data are required for this solution. “Fuzzy set theory” is a helpful tool for dealing with such …subjectivity and uncertainty. In recent times, extensions of fuzzy sets have been evolved to address and describe the subjectivities and uncertainties more widely. Neutrosophic sets are one of the extensions of fuzzy sets, and unlike other extensions, they use the independent indeterminacy-membership function, thereby extracting important information and improving the accuracy of the decision-making process. A neutronophic MCDM method was proposed for the assessment of 4PL providers’ performance. In the application part of the study, neutrosophic language scale was used by three experts to evaluate the performance of 4PL providers. Then the closeness coefficient of each alternative was computed and sequenced in descending order. We also presented a comparative analysis with neutrosphic TOPSIS method. The results determined that the proposed neutrosophic MCDM method could be used in the performance evaluation of 4PL providers and similar problems. Show more

Keywords: Supply chain management, 4PLs, multi criteria decision making, neutrosophic sets

DOI: 10.3233/JIFS-219209

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 1, pp. 529-539, 2022