Fuzzy-based Simulation Research Articles

Decision-making system for agile manufacturing implementation: a fuzzy-based simulation

Decision-making system for agile manufacturing implementation: a fuzzy-based simulation

Sustainability Balanced Scorecard Approach to Internet of Things Enabled Logistics Systems

Performance evaluation is an essential part of assessing the sustainability of the logistics systems and the implications of Industry 4.0 technologies. This research proposes an integrated sustainability evaluation framework for logistics providers using a fuzzy-based simulation model. A sustainability balanced scorecard approach is used in a hierarchical model based on multiple Mamdani Fuzzy Inference Systems (FIS). The sustainability of Internet of Things (IoT) adoption for a logistics provider was demonstrated using a hypothetical case study. A Design of Experiment approach is applied to evaluate the validity of the model. Moreover, different defuzzification methods are used to test the robustness of the fuzzy inference systems. The proposed method can aid operational managers in assessing the contribution of IoT to the sustainability of logistics systems considering inventory, warehouse, and order fulfillment aspects.

Employing Fuzzy Logic to Analyze the Structure of Complex Biological and Epidemic Spreading Models

Complex networks constitute a new field of scientific research that is derived from the observation and analysis of real-world networks, for example, biological, computer and social ones. An important subset of complex networks is the biological, which deals with the numerical examination of connections/associations among different nodes, namely interfaces. These interfaces are evolutionary and physiological, where network epidemic models or even neural networks can be considered as representative examples. The investigation of the corresponding biological networks along with the study of human diseases has resulted in an examination of networks regarding medical supplies. This examination aims at a more profound understanding of concrete networks. Fuzzy logic is considered one of the most powerful mathematical tools for dealing with imprecision, uncertainties and partial truth. It was developed to consider partial truth values, between completely true and completely false, and aims to provide robust and low-cost solutions to real-world problems. In this manuscript, we introduce a fuzzy implementation of epidemic models regarding the Human Immunodeficiency Virus (HIV) spreading in a sample of needle drug individuals. Various fuzzy scenarios for a different number of users and different number of HIV test samples per year are analyzed in order for the samples used in the experiments to vary from case to case. To the best of our knowledge, analyzing HIV spreading with fuzzy-based simulation scenarios is a research topic that has not been particularly investigated in the literature. The simulation results of the considered scenarios demonstrate that the existence of fuzziness plays an important role in the model setup process as well as in analyzing the effects of the disease spread.

Strategic innovation and entrepreneurial ownership: an analysis using GEM data and fuzzy simulation

PurposeThis paper aims to study entrepreneurial ownership and technological input on product innovation globally and in the Indian context. The data used in this study is collected from the Global Entrepreneurship Monitor (GEM).Design/methodology/approachThe study evaluates strategic innovation and entrepreneurial ownership in global and country-level data. The study was extracted from global and country-level data by the GEM. One of the methods used is ordinal regression analysis to examine the importance of entrepreneurial ownership and technology usage on product innovation. Another technique used is the fuzzy-based simulation to simulate the scenario of entrepreneurial ownership and usage of technology in business processes impacting product innovation.FindingsResults from statistical analysis indicate that entrepreneurial ownership is high in India compared to the globe. The usage of technology and its excellence is still lower than the global level and in other countries. In India, product innovation is achieved because of strong entrepreneurial ownership but, globally product innovation is achieved because of the higher level of technological excellence and technology usage in innovation activities. The fuzzy-based simulation shows that product innovation is at peak only when both technological input and entrepreneurial ownership are high.Originality/valueThis paper's original contribution is that the fuzzy-based simulations are applied to GEM data to study the relationships of entrepreneurial ownership and technological input on product innovation through simulation. The study has shed light on how entrepreneurial ownership and usage of technology are influential in product innovation.

Decision making system for agile manufacturing implementation: A fuzzy-based simulation

Decision making system for agile manufacturing implementation: A fuzzy-based simulation

Justification of synergistic implementation of TQM-SCM using fuzzy-based simulation model

PurposeThe purpose of this paper is to evaluate the contribution of significant factors in the success of supply chain management (SCM) implementation and in synergistic approach (SCM-TQM) terms of business performance of Indian medium- and large-scale manufacturing industry.Design/methodology/approachThis research paper deploys the fuzzy inference system (fuzzy logic tool box) to evaluate the success of SCM and synergistic approach’s implementation. For this purpose, significant factors for each SCM approach as well as synergistic approach are identified from relevant literature and validated by industry experts.FindingsThe results of fuzzy rule viewer and surface view tool of fuzzy tool box in MATLAB have highlighted that synergistic approach is better as compared to SCM approach. Furthermore, top management support and leadership role, SCM issues and total quality management (TQM) issues have emerged as significant predictor variables for successful synergistic implementation of TQM-SCM in Indian industries.Originality/valueTQM and SCM are considered as performance improvement techniques by the manufacturing organizations. The present research work establishes that combined (TQM-SCM) initiatives have contributed more as compared to only SCM approach in the business performance of Indian manufacturing industry. So, the study stresses upon the need for improving coordination between various manufacturing parameters as well as competitive dimensions of TQM and SCM paradigms to enjoy higher potential of business performance.

IoT node selection in Opportunistic Networks: Implementation of fuzzy-based simulation systems and testbed

Opportunistic Network is a type of Ad-hoc networks, where nodes may be connected temporarily. Contacts between nodes are intermittent and an end-to-end link may never be established. These networks enable communication when conventional network infrastructures cannot be implemented. Each day, numerous nodes become part of the Internet, extending the connectivity to everyday objects. This network, made up of diverse nodes which connect to each other, is called Internet of Things (IoT). An IoT network is heterogeneous, which means that when a task needs a certain action, there are some IoT nodes who may complete it better than the others. Choosing which nodes are better fitted, is the main challenge we have considered in this paper. To make a proper decision on which IoT nodes are better than others, we have proposed two systems based on Fuzzy Logic: IoT Node Selection System 1 (INSS1) and IoT Node Selection System 2 (INSS2). For INSS1 we use three input parameters: Node’s Distance to Task (NDT), Node’s Remaining Energy (NRE), Node’s Buffer Occupancy. For INSS2, we add a new parameter Node Inter Contact Time (NICT) and together with 3 parameters of INSS1, there are four input parameters. The simulation results show that INSS2 makes a better IoT node selection, but is more complex than INSS1, because of higher number of rules. We implemented a testbed and compared simulation results with experimental results. The testbed results give a better insight than the simulation results due to the fact of being implemented on a real environment.

An approach for justification of success 5S program in manufacturing organisations using fuzzy-based simulation model

Customised demands having low cost, good quality of products and services in time are the major issues of the global competitive markets. Every organisation resolves these issues by the implementation of different quality improvement tools like lean and 5S implementation in order to stay competitive in the market. Though 5S is a simple technique to learn, but the sustainability and success of implementation in organisation needs a number of factors to be considered which later on become a multi criteria decision making (MCDM) problem. This research paper deploys the fuzzy inference system (fuzzy logic toolbox) to evaluate the success of 5S implementation. The results of fuzzy rule viewer and surface view tool of fuzzy tool box in MATLAB have highlighted that top to bottom management involvement; basic 5S issues and fifth S (SHITSUKE) elements have emerged as significant predictor variables for successful 5S implementation program in Indian industries.

An approach for justification of success 5S program in manufacturing organisations using fuzzy-based simulation model

An approach for justification of success 5S program in manufacturing organisations using fuzzy-based simulation model

Selection of performance measure in JIT through fuzzy logic-based simulation

The present study is a new approach for finding the most significant performance measure in JIT with the help of fuzzy logic rule base system. Fuzzy logic is a decision making tool which depends upon various factors like percentage JIT implementation in the organisation and gain in performance measure due to implementation of JIT. The success of any industry depends on quality product with optimum utilisation of men material and machinery and to achieve the above said factors the just-in-time (JIT) practice should be efficient and effective. Fuzzy base manufacturing system (FBMS) has been used for the present study because of the fact that fuzzy logic tool is best suited for dynamic environment where decisions are required to be taken on the basis of multi variable parameters. The main focus of this study is the modelling of a 'fuzzy-based simulation' for finding the significance of performance measure of JIT considering percentage of JIT implementation and percentage gain in performance measure due to implementation of JIT as parameters.

Synergistic suitability of transfusion of TQM-TPM for Indian manufacturing industries using fuzzy-based model simulation

Total quality management (TQM) and total productive maintenance (TPM), are popular initiatives employed by organisations as performance improvement techniques for attaining customer satisfaction, reliability, productivity, market share, profitability and even survival. Now a day’s all manufacturers are trying to implement new methods, i.e., multi criteria decision making (MCDM) like analytical hierarchy process (AHP), piecewise-affine (PWA), a fuzzy-based (FB) model, etc., along with TQM-TPM drives for effective production process. In this paper, an attempt has been made to show the synergistic suitability of transfusion of TQM-TPM for Indian manufacturing industries. For the study, the most relevant factors affecting these drives like quality, cost and maintenance has been considered and further these factors has been simulated by the data given by experts in this fields using fuzzy logic toolbox of MATLAB which provides the steps for designing fuzzy inference systems using graphical tools, and a Simulink block for analysing, designing, and simulating systems based on fuzzy logic.

Suitability Assessment of Lean Kitting Assembly through Fuzzy Based Simulation Model

The success of an industry depends on its product‟s quality, cost and delivery time. So, now a days all manufacturers are trying to implement new manufacturing methods for their production process. In this paper, an attempt has been made to find the suitability of new assembly method known as Lean Kitting assembly for a leading two wheeler manufacturer in India. Lean Kitting means supplying assembly station with kit of components. Even though lot of Multi Criteria Decision Making (MCDM) models like AHP, ANP and PVA are available, a Fuzzy Based Simulation (FBS) model is necessary to assure the suitability by considering important factors and simulate the factors with data given by the experts in those fields. This paper mainly focused on the modeling of a „Fuzzy Based Simulation‟ for finding the suitability of the Lean Kitting system by considering the following important factors: Work In Process inventory, Floor space required, operator walking distance.