Integrating analytical network process and fuzzy logic to prioritize the strategies - A case study for tile manufacturing firm

SWOT analysis is the most important tool for decision makers in strategic management process, because it enables the managers to discover and collect the facts that resulted from internal and external analysis. This analysis can be a good base for strategy formulation, but it is not non-defect. It includes no means of analytically determining the importance of factors or assessing the fit between SWOTS factors and decision alternatives and is mainly based on the qualitative analysis. Many researchers develop different techniques to quantify the SWOT analysis, but these methods are not comprehensive. In this study, we develop a fuzzy method model for quantitative weighing of SWOT strategies. The decision maker’s imprecise information is regarded by fuzzy numbers in this model. To consider dependency among factors and sub-factors, we use ANP technique in our research. We demonstrate our model with a case study example and compare results of our research with prior research.     

Determining key capabilities in technology management using fuzzy analytic hierarchy process: A case study of Turkey

The importance of technology management is a vital determinant of long-run success or failure of organizations in today’s world. Since technology is a major driver of the global economic development, business professionals seek more effective ways to manage existing and emerging technology. This study proposes a model to understand the links between competitive advantages, competitive priorities and competencies of a firm in the context of the technology management. We use the fuzzy analytical hierarchy process (AHP) to analyze these links. Moreover, we examine the perception of a group of managers from different Turkish firms regarding the technology management. The main result of this paper is that the concept of the management of technology arises to be much more important than both the product technology and the process technology.     

Optimal fuzzy control system using the cross-entropy method. A case study of a drilling process

This paper focuses on the optimal tuning of fuzzy control systems using the cross-entropy precise mathematical framework. The design of an optimal fuzzy controller for cutting force regulation in a network-based application and applied to the drilling process is described. The key issue is to obtain optimal fuzzy controller parameters that yield a fast and accurate response with minimum overshoot by minimising the integral time absolute error (ITAE) performance index. Simulation results show that the cross-entropy method does find the optimal solution (i.e. input scaling factors) very accurately, and it can be programmed and implemented very easily (few setting parameters). The results of a comparative study demonstrate that optimal tuning with the cross-entropy method provides a good transient response (without overshoot) and a better error-based performance index than simulated annealing [17], the Nelder-Mead method [14] and genetic algorithms [33]. The experimental results demonstrate that the proposed optimal fuzzy control provides outstanding transient response without overshoot, a small settling time and a minimum steady-state error. The application of optimal fuzzy control reduces rapid drill wear and catastrophic drill breakage due to the increasing and oscillatory cutting forces that occur as the drill depth increases.     

A fuzzy analytic network process based approach to transportation-mode selection between Turkey and Germany: A case study

A case study examining the different modes for transportation of freight by a Turkish logistics-service provider company is presented herein. A number of conflicting qualitative and quantitative criteria exist for evaluating alternative modes of transport. Qualitative criteria are often accompanied by ambiguity and vagueness. To cope with ambiguity and vagueness problem, the fuzzy analytic network process (ANP) method has been used. A large number of detailed criteria that interact with each other have been evaluated and synthesized to obtain the most suitable transportation mode. This evaluation has been carried out by a group of decision makers coming from different management levels and functional areas in the sector of logistics and from the service company with intent to provide a more accurate and mutually acceptable solution. Furthermore, the model used here has been validated by comparing the results obtained with the current preferences of the company.     

Using the analytic network process (ANP) in a SWOT analysis - A case study for a textile firm

Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis does not provide an analytical means to determine the importance of the identified factors or the ability to assess decision alternatives according to these factors. Although the analysis successfully pinpoints the factors, individual factors are usually described briefly and very generally. For this reason, SWOT analysis possesses deficiencies in the measurement and evaluation steps. Although the analytic hierarchy process (AHP) technique removes these deficiencies, it does not allow for measurement of the possible dependencies among the factors. The AHP method assumes that the factors presented in the hierarchical structure are independent; however, this assumption may be inappropriate in light of certain internal and external environmental effects. Therefore, it is necessary to employ a form of SWOT analysis that measures and takes into account the possible dependency among the factors. This paper demonstrates a process for quantitative SWOT analysis that can be performed even when there is dependence among strategic factors. The proposed algorithm uses the analytic network process (ANP), which allows measurement of the dependency among the strategic factors, as well as AHP, which is based on the independence between the factors. Dependency among the SWOT factors is observed to effect the strategic and sub-factor weights, as well as to change the strategy priorities.     

A fuzzy inference system for pump failure diagnosis to improve maintenance process: The case of a petrochemical industry

Pump operating problems may be either hydraulic or mechanical and there is interdependence between the failure diagnoses of these two categories. Consequently, a correct diagnosis of a pump failure needs to consider many symptoms and hydraulic or mechanical causes. But, due to nonlinear, time-varying behavior and imprecise measurement information of the systems it is difficult to deal with pumps failures with precise mathematical equations, while human operators with the aid of their practical experience can handle these complex situations, with only a set of imprecise linguistic if-then rules and imprecise system state, but this procedure is time consuming and needs the knowledge of human experts and experienced maintenance personnel. The purpose of this study is to provide a correct and timely diagnosis mechanism of pump failures by knowledge acquisition through a fuzzy rule-based inference system which could approximate human reasoning. The proposed fuzzy inference system by: (1) reduction of human error, (2) reduction of repair time (3) creation of expert knowledge which could be used for training (4) reduction of unnecessary expenditures for upgrades and finally, (5) reduction of maintenance costs, will improve the maintenance process. The novelty of this work is the knowledge acquisition (the extraction of linguistic rules) through the interactive impact of the critical failure modes on the both hydraulic and mechanical operating parameters including flow rate, discharge pressure, NPSHR (Net Positive Suction Head Required), BHP (Brake Horse Power), efficiency, vibration and temperature. The proposed approach is tested and applied to a petrochemical industry.     

A decision support system based on fuzzy reasoning and AHP–FPP for the ecodesign of products: Application to footwear as case study

The design stage represents one of the most critic steps for product development. Here, a great number of considerations have to be borne in mind, e.g., technical, functional, aesthetic or economic criteria. More recently, the increasing concerns on environmental aspects have added complexity to the process, known as ecodesign. In this respect, a framework to integrate the criteria provided by quantitative environmental indicators has been proposed on the basis of Fuzzy Preference Programming method features and fuzzy logic reasoning. As a result, an integrated Ecodesign Index (EcoInd) is obtained. This idea enables the decision making at process and product level taking into account different indicators at a time. The ecodesign of children's footwear was taken as case study and an ecodesign tool (decision support system) that included the estimation of environmental indicators and their integration was developed. Different models of shoes were analyzed to identify the most environmentally friendly design and to test the tool. In this case, the Ecological Footprint and two Environmental Risk Assessment indicators, namely Hazard Quotient and Cancer Risk, were selected as relevant environmental indicators and they were computed from data provided by a shoes manufacturer. Then, these indicators were integrated in the ecodesign tool and the EcoInd values were appraised for the children's footwear models analyzed. According to these figures, they were ranked as Red Leather > White Leather > White Synthetic > Pink Synthetic, from best to worst.     

The analytic network process for managing inter-enterprise collaboration: A case study in a collaborative enterprise network

In the last years, collaboration among enterprises has gained attention in the business environment as a means to remain competitive. Enterprises that are collaborating look for improving their performance but, in real assessments, they often do not establish efficient frameworks to structure and manage the enterprise association/inter-enterprise performance. In addition, there are many factors that act as barriers to effective collaboration and have to be also properly managed as they impact on the inter-enterprise performance. This paper provides a methodology based on the analytic network process (ANP) to identify and measure, under an integrated approach, both factors and inter-enterprise performance considering their reciprocal impact. With this innovative approach, enterprises will obtain significant information for the decision-making process regarding which are the factors and inter-enterprise performance elements that generate a higher impact and therefore have a high priority within the specific collaborative relationship. Thus, enterprises can focus their efforts on improving those most important factors and performance elements, and consequently, enhancing their competitiveness.     

Identification of a process with control valve stiction using a fuzzy system: A data-driven approach

Many researchers focus on detecting and modelling the valve stiction because it has undesirable effects on the control loop performance, which consequently results in poor product quality and increased energy consumption. It is difficult to model a process with a sticky valve using the mathematical definition because of its nonlinear properties such as stiction, hysteresis, dead band and dead zone. This work aims to develop and determine the appropriate model of a process with stiction, which can be used in controller design to mitigate the undesirable effect of the stiction. To achieve this goal by mapping the process with valve stiction to a fuzzy system, a dynamic fuzzy model of the plant is derived through an iterative well-developed fuzzy clustering algorithm, which generates suitable antecedent parameters from a set of input–output measurements that are obtained from the control output (OP) and the process output (PV). To determine the consequent parameters, the least square (LS) estimation is applied. The results reveal that the obtained data-driven Takagi–Sugeno-type (TS) fuzzy rule-based model can effectively represent an appropriate model of the process with stiction for different amounts of stiction that are obtained from the simulation and different industrial loops.     

A model of resilient supply chain network design: A two-stage programming with fuzzy shortest path

A supply chain network design needs to consider the future probability of reconfiguration due to some problems of disaster or price changes. The objective of this article is to design a reconfigurable supply chain network by optimizing inventory allocation and transportation routing. A two-stage programming is composed according to Benders decomposition by allocating inventory in advance and anticipating the changes of transportation routings; thus the transportation routing is stochastic in nature. In addition, the fuzzy shortest path is developed to solve the problem complexity in terms of the multi-criteria of lead time and capacity with an efficient computational method. The results and analysis indicate that the proposed two-stage programming with fuzzy shortest path surpasses the performance of shortest path problem with time windows and capacity constraint (SPPTWCC) in terms of less computational time and CPU memory consumption. Finally, management decision-making is discussed among other concluding remarks.     

Optimization of process parameters through fuzzy logic and genetic algorithm – A case study in a process industry

The simultaneous generation of steam and power, which is commonly referred to as cogeneration, has been adopted by many sugar mills in India to overcome the power shortage. It becomes an increasingly important source of income for sugar factories. The problems faced by the sugar mill industry arise mainly due to failures of either the complete system or some specific components during the cogeneration process. This paper presents the failure analysis of the boiler during the cogeneration process and provides solution to overcome these failures. The failures frequently occur in the screw conveyor and in the drum feeder of fuel feeding system and the grate of the boiler. In this research work, the statistical tools viz., Failure Mode and Effect Analysis (FMEA) and the Taguchi method have been applied to investigate and alleviate these failures. Since conventional FMEA has some limitations and Taguchi method does not give better solution, fuzzy FMEA has been employed to overcome the limitations and genetic algorithm technique has been applied to obtain failure – free system during the cogeneration process.     

Ubiquitous conceptual design of a ubiquitous application: A textile SME case study for real time manufacturing monitoring

Advances in Information and Communication Technologies (ICT), computing, networking, mechanics and electronics are changing the people’s way of life. Several research efforts are leading the design and development of Artifact and Service Combination (ASC) with the implementation of Ubiquitous Technologies (UTs) in multidisciplinary sectors. However, the design process of such systems often ends in the implementation of conventional approaches and tools. A Ubiquitous Design Support Environment (UDSE) comprising an application intended to guide the different activities, tools and resources applied at the conceptual design stage is presented. After needs analysis, multidisciplinary collaborations are also required in order to generate innovative conceptual solutions, focusing this approach in the conceptual design stage of traditional design methods. Some activities from the conceptual design stage are enhanced through the use of the UDSE as well as through the use of a novel ubiquity assessment tool for concept selection and validation of Ubiquitous Products and Services. Finally, a case study on a Small and Medium Enterprise (SME) from textile sector, in a developing country, is presented to analyze and validate the presented concepts.     

网络自动答疑系统中模糊数学方法的应用研究

答疑解惑是教学过程中不可缺少的环节,如何在Internet环境下设计具有自动答疑功能的网络自动答疑系统,成为当前智能教学研究的热点。目前国内外在这方面做的工作主要集中在如何提高系统的智能方面,代表性的算法有：关键词匹配算法、加权关键词算法等。     

Environmental comprehensive assessment of agricultural systems at the farm level using fuzzy logic: A case study in cane farms in Iran

Development of a fuzzy inference model is a complex multi-step process in which we encounter a large number of parameters such as type and number of membership functions, fuzzy operators, defuzzification and implication methods and etc. There is currently very little literature on the topic of the best selection of parameters for development of expert based inference models. In this study we developed a fuzzy rule based model, which uses available farm management data as required inputs, for the environmental assessment of farming systems. We also tried to make an analysis on the efficiency of current mathematical parameters in the development of our fuzzy model. Finally, in a practical example we demonstrate the applicability of the developed model for improvement of environmental status of the cane farming in Iran.A Mamdani fuzzy inference model with two inference engines was developed to combine five basic input indexes, which were selected as indicators of farms environmental status based on the experts' interview and scientific knowledge. To validate the developed model, we inserted several cycles of analysis using graphical and global sensitivity methods on the model and compared the model outcomes with experts' viewpoints. Using these analysis methods, we also evaluated the effects of changes in operators, membership function shape and defuzzification methods, on the model outcomes and their sensitivities.In this study, fuzzy inference emerged as a suitable, uncomplicated and effective tool for development of environmental assessment models. Totally, performance of one parameter was highly influenced by other parameters. For the selection of one parameter its interaction with other parameters had to be considered. Type, shape and the number of membership functions were from the most effective parameters for development of the model and significantly influenced the other factors. Case study results showed that environmental indexes of sugarcane production can enhance between 37 and 59% using simple improving strategies.     

Smart control of the assembly process with a fuzzy control system in the context of Industry 4.0

Assembly line balancing is important for the efficiency of the assembly process, however, a wide range of disruptions can break the current workload balance. Some researchers explored the task assignment plan for the assembly line balancing problem with the assumption that the assembly process is smooth with no disruption. Other researchers considered the impacts of disruptions, but they only explored the task re-assignment solutions for the assembly line re-balancing problem with the assumption that the re-balancing decision has been made already. There is limited literature exploring on-line adjustment solutions (layout adjustment and production rate adjustment) for an assembly line in a dynamic environment. This is because real-time monitoring of an assembly process was impossible in the past, and it is difficult to incorporate uncertainty factors into the balancing process because of the randomness and non-linearity of these factors. However, Industry 4.0 breaks the information barriers between different parts of an assembly line, since smart, connected products, which are enabled by advanced information and communication technology, can intelligently interact and communicate with each other and collect, process and produce information. Smart control of an assembly line becomes possible with the large amounts of real-time production data in the era of Industry 4.0, but there is little literature considering this new context. In this study, a fuzzy control system is developed to analyze the real-time information of an assembly line, with two types of fuzzy controllers in the fuzzy system. Type 1 fuzzy controller is used to determine whether the assembly line should be re-balanced to satisfy the demand, and type 2 fuzzy controller is used to adjust the production rate of each workstation in time to eliminate blockage and starvation, and increase the utilization of machines. Compared with three assembly lines without the proposed fuzzy control system, the assembly line with the fuzzy control system performs better, in terms of blockage ratio, starvation ratio and buffer level. Additionally, with the improvement of information transparency, the performance of an assembly line will be better. The research findings shed light on the smart control of the assembly process, and provide insights into the impacts of Industry 4.0 on assembly line balancing.     

To construct a monitoring mechanism of production loss by using Fuzzy Delphi method and fuzzy regression technique – A case study of IC package testing company

The development of information technology and internet capabilities over the years has been at an accelerated pace of global intense challenges and competitions of the technologies markets. Therefore, the issue of effectively utilizing valuable information located in databases worldwide, by monitoring the performance of their operations became an important issue in the electronic business environment. Methods to address this issue were employed to quickly identify the Key Performance Indicators (KPI’s) and operational problems and clarify any relationships between them, to allow the overall business objectives to be achieved. In this paper, the Fuzzy Delphi and ranking methods were used to extract the most concerning issues through expert questionnaires. A fuzzy regression model was then constructed and applied to clarify the relationships between the KPI’s and the key management objective, the area of production loss. Therefore, the key factors for future improvements were obtained by tracking the fuzzy regression model. Finally a case study of a semiconductor assembly and testing, through a company in Taiwan, was used to illustrate the proposed framework. The results indicated that this model could be easily implemented to analyze the influence of concerned KPIs on key management objectives.     

A fuzzy reasoning and fuzzy-analytical hierarchy process based approach to the process of railway risk information: A railway risk management system

Risk management is becoming increasingly important for railway companies in order to safeguard their passengers and employees while improving safety and reducing maintenance costs. However, in many circumstances, the application of probabilistic risk analysis tools may not give satisfactory results because the risk data are incomplete or there is a high level of uncertainty involved in the risk data. This article presents the development of a risk management system for railway risk analysis using fuzzy reasoning approach and fuzzy analytical hierarchy decision making process. In the system, fuzzy reasoning approach (FRA) is employed to estimate the risk level of each hazardous event in terms of failure frequency, consequence severity and consequence probability. This allows imprecision or approximate information in the risk analysis process. Fuzzy analytical hierarchy process (fuzzy-AHP) technique is then incorporated into the risk model to use its advantage in determining the relative importance of the risk contributions so that the risk assessment can be progressed from hazardous event level to hazard group level and finally to railway system level. This risk assessment system can evaluate both qualitative and quantitative risk data and information associated with a railway system effectively and efficiently, which will provide railway risk analysts, managers and engineers with a method and tool to improve their safety management of railway systems and set safety standards. A case study on risk assessment of shunting at Hammersmith depot is used to illustrate the application of the proposed risk assessment system.     

Optimal design for robust control of uncertain flexible joint manipulators: a fuzzy dynamical system approach

A novel fuzzy dynamical system approach to the control design of flexible joint manipulators with mismatched uncertainty is proposed. Uncertainties of the system are assumed to lie within prescribed fuzzy sets. The desired system performance includes a deterministic phase and a fuzzy phase. First, by creatively implanting a fictitious control, a robust control scheme is constructed to render the system uniformly bounded and uniformly ultimately bounded. Both the manipulator modelling and control scheme are deterministic and not IF-THEN heuristic rules-based. Next, a fuzzy-based performance index is proposed. An optimal design problem for a control design parameter is formulated as a constrained optimisation problem. The global solution to this problem can be obtained from solving two quartic equations. The fuzzy dynamical system approach is systematic and is able to assure the deterministic performance as well as to minimise the fuzzy performance index.     

Application of the fuzzy analytic hierarchy process in multi-criteria decision in noise action plans: Prioritizing road stretches

Traffic noise is one of the major environmental impacts of road infrastructures. Critical study of published Noise Action Plans (NAP) signals a widespread lack of objective criteria and methodologies for prioritizing actions against noise as well as the suitability of solutions. The present paper develops a methodology to sort, by priority, road stretches included in a NAP. In obtaining and allocating weights to variables involved in the decision-making problem (“Road Stretch Priority Variables”) to define a normalized numerical index (“Road Stretch Priority Index”), Fuzzy Analytic Hierarchy Process (FAHP) with two different defuzzification methods is applied to the results of an expert panel. Comparison of the outcomes of both FAHP versions, plus analysis of the results of a case study, enables to determine the relative influence of these variables in the problem. An objective and reasoned methodology for the prioritized classification of road stretches according to noise problems is thereby validated.