Traffic waiting time management using fuzzy logic approach

The significant increase in the number of vehicles has become a major problem, that causes enormous problems such as congestion, pollution, and the wasting of money and time. Therefore, the simulating and optimizing traffic control algorithms are needed to accommodate the demand in order to solve traffic congestion problems. Predicting effective and optimal green time taken for the intersections will help minimizing the traffic congestion henceforth reducing the waiting time. In this study, fuzzy logic is used to find optimal traffic waiting time. The method determines the effective and optimal traffic signal timing that suits different traffic densities. In this case, the study has considered a four-way intersection. The results indicate that different road intersections require different effective and optimal of green time to reduce traffic congestion. The higher the number of cars at the intersection, the effective green time will be longer rather than the lesser number of cars. The flexibility feature of the fuzzy logic will provide suitable optimal green time for the intersection, which is cordially benefit the users. It is worth mentioning that fuzzy logic traffic lights controller performed better than the fixed-time controller due to its flexibility and the capability in reducing the waiting time

Integrating fuzzy AHP and Z-TOPSIS for supplier selection in an automotive manufacturing company

Selecting the right supplier is one important process in a supply chain of a company. It will reduce procurement cost but increase stakeholders’ satisfaction. Living in the environment filled with uncertainties, while the suppliers are prescribed under multiple criteria and also the expert may not be able to evaluate the suppliers precisely, fuzzy multi-criteria decision making (MCDM) is used to handle this uncertain situation. However, the classical fuzzy MCDM assumes decision information is completely reliable. Thus, the main aim of this study is to incorporate the degree of reliability of the expert’s judgment in fuzzy environment by integrating fuzzy MCDM with Z-number. Fuzzy AHP is used to determine the weight of criteria/sub-criteria and rating the suppliers in an automotive manufacturing company, while Z-TOPSIS is used to evaluate the overall performance of suppliers. Results of the evaluation would help the purchasing manager to determine the right supplier that fulfills the company’s goal. Besides, the methodology of this study is a good guide to be implemented in other multi-criteria decision making problems

A Preliminary Study on Understanding the Consumptions of Therapeutic Essential Oils During Covid-19 Pandemic Among Adults Using ANN

The COVID-19 pandemic has emphasized the significance of utilizing essential oils (EO) as one of the holistic ways of supporting and enhancing health. As a consequence of growing knowledge of connected health concerns, people all over the world are looking for natural ways to avoid different ailments. It has been proven that excellent health and psychological awareness increase the human body's immune response, therefore boosting disease resistance. Essential oils are derived in a number of ways from valued plants containing active chemicals with medicinal qualities. In Malaysia, many have used EO in their daily lives. This paper identifies the hierarchy of importance among factors which contribute towards the usage frequency of essential oils in Malaysia using an artificial neural network. Two-layer neural network (NN) models have been applied, which are multilayer perceptron (MLP) and radial basis function (RBF). Based on the analysis done, RBF-NN performed the best with SSE=4.436 and RE=0.548. It can be concluded that, based on sensitivity analysis, the top five factors toward usage frequency are consumption, age, external use, clinic visit, and occasion, with normalized importance of 100%, 90.8%, 89.3%, 68.2%, and 42.2% respectively

Large-scale group decision-making method using hesitant fuzzy rule-based network for asset allocation

Large-scale group decision-making (LSGDM) has become common in the new era of technology development involving a large number of experts. Recently, in the use of social network analysis (SNA), the community detection method has been highlighted by researchers as a useful method in handling the complexity of LSGDM. However, it is still challenging to deal with the reliability and hesitancy of information as well as the interpretability of the method. For this reason, we introduce a new approach of a Z-hesitant fuzzy network with the community detection method being put into practice for stock selection. The proposed approach was subsequently compared to an established approach in order to evaluate its applicability and efficacy

Impact of strategic leadership on organizational performance, strategic orientation and operational strategy

This paper focuses on the impact of strategic leadership on operational strategy and organizational performance of the automobile industry in Malaysia with a particular focus on Proton (Perusahaan Otomobil Malaysia). Since the mid-1980s a growing body of research on leadership has focused on strategic leadership, in contrast to managerial and visionary leadership. It has focused on how lead-ers make decisions in the short term that guarantees long-term viability of the organization. Senior leaders also have the ability to align human resources in an effective way directly to the business strategy. This article focuses on how national car manufacturer, Proton, exercises strategic leadership to influence its operational strategy and performance. It examines both dependent and inde-pendent variables that influence on strategic leadership with implications for future research

Review of Fuzzy Numbers and its Application in Capital Budgeting

A fuzzy number is an extension of a regular number in the sense that it does not refer to one single value but rather to a connected set of possible values. Whereas, capital budgeting is the planning process used to determine whether an organisation’s long term investments such as new machinery, replacement machinery, new plants, new products, and research development projects are worth pursuing. The purpose of this study is to review the concept of one, two and ndimensional fuzzy numbers and investigate the application of fuzzy numbers in financial field specifically in capital budgeting problem. There are five capital budgeting techniques usually used in practices, such as revenue per one dollar, payback period, net present value(NPV), net future value(NFV), and the modified of internal rate of return(MIRR) methods. In this research both, classical and fuzzy approach are used in order to evaluate the project as mentioned above. The research will also focus on the application of special case of trapezoidal fuzzy numbers, triangular fuzzy number. At the end of this research the comparison of classical and fuzzy methods are discussed according to five capital budgeting methods. The result of this study provides the alternative way in financial field to evaluate the profitable project to invest

Interactive TOPSIS Based Group Decision Making Methodology Using Z-Numbers

The ability in providing result that is consistent with actual ranking remains the major concern in group decision making environment. The main aim of this paper is to introduce a novel modification of TOPSIS method to facilitate multi criteria decision making problems based on the concept of Z-numbers called Z-TOPSIS. The proposed method is adequate and intuitive in giving meaningful structure for formalizing information of a decision making problem, as it takes into account the decision makers’ reliability. This study also provides bridge with some established knowledge in fuzzy sets to certain extend as to strengthen the concept of ranking alternatives using Z – numbers. To ensure practicality and effectiveness of proposed method, stock selection problem is studied. The ranking based on proposed method is validated comparatively using spearman rho rank correlation. Based on the analysis, the proposed method outperforms the established TOPSIS methods in term of ranking performance

FN-TOPSIS: fuzzy networks for ranking traded equities

Fuzzy systems consisting of networked rule bases, called fuzzy networks, capture various types of imprecision inherent in financial data and in the decision-making processes on them. This paper introduces a novel extension of the Technique for Ordering of Preference by Similarity to Ideal Solution (TOPSIS) method and uses fuzzy networks to solve multi criteria decision-making problems where both benefit and cost criteria are presented as subsystems. Thus the decision maker evaluates the performance of each alternative for portfolio optimisation and further observes the performance for both benefit and cost criteria. This approach improves significantly the transparency of the TOPSIS methods, while ensuring high effectiveness in comparison to established approaches. The proposed method is further tested here on portfolio selection problems covering developed and emergent financial markets. The ranking produced by the method is validated using Spearman rho rank correlation. Based on the case study, the proposed method outperforms the existing TOPSIS approaches in term of ranking performance

Selection of alternatives using fuzzy networks with rule base aggregation

This paper introduces a novel extension of the Technique for Ordering of Preference by Similarity to Ideal Solution (TOPSIS) method. The method is based on aggregation of rules with different linguistic of the output of fuzzy networks to solve multi-criteria decision-making problems whereby both benefit and cost criteria are presented as subsystems. Thus the decision maker evaluates the performance of each alternative for decision process and further observes the performance for both benefit and cost criteria. The aggregation sub-stage in a fuzzy system maps the fuzzy membership functions for all rules to an aggregated fuzzy membership function representing the overall output for the rules. This approach improves significantly the transparency of the TOPSIS methods, while ensuring high effectiveness in comparison to established approaches. To ensure practicality and effectiveness, the proposed method is further tested on portfolio selection problems. The ranking produced by the method is comparatively validated using Spearman rho rank correlation. The results show that the proposed method outperforms the existing TOPSIS approaches in term of ranking performance