Battery Energy Management System Using Edge-Driven Fuzzy Logic

Building energy management systems (BEMSs), dedicated to sustainable buildings, may have additional duties, such as hosting efficient energy management systems (EMSs) algorithms. This duty can become crucial when operating renewable energy sources (RES) and eventual electric energy storage systems (ESSs). Sophisticated EMS approaches that aim to manage RES and ESSs in real time may need high computing capabilities that BEMSs typically cannot provide. This article addresses and validates a fuzzy logic-based EMS for the optimal management of photovoltaic (PV) systems with lead-acid ESSs using an edge computing technology. The proposed method is tested on a real smart grid prototype in comparison with a classical rule-based EMS for different weather conditions. The goal is to investigate the efficacy of islanding the building local network as a control command, along with ESS power control. The results show the implementation feasibility and performance of the fuzzy algorithm in the optimal management of ESSs in both operation modes: grid-connected and islanded modes

A new fuzzy risk management model for production supply chain economic and social sustainability

The issues of operational, organisational and process risk assessment in supply chains (SCs) are the most usually analysed, while other risk groups (like economic and social risks) are not taken into account, even though they have a critical effect on the competitive advantage and SCs sustainability over long time periods. The determination of risk value that may arise due to the materialisation of each defined risk factor (RF) is based on the assessment of the severity of RF consequences and frequency of RF occurrence. These judgments are obtained by decision makers and modelled by using fuzzy set theory. The relative importance of RFs are stated by fuzzy pair-wise comparison matrices in compliance with fuzzy analytical hierarchy process (FAHP). The risk level of SCs could be obtained in an exact way by applying fuzzy logic. The proposed model, to be presented in this paper, provides a possibility to easily and simply determine risk level from the automotive industry SC and to propose appropriate management initiatives that should lead to a reduction or elimination of RF influenc

Agile Supplier Selection In Sanitation Supply Chain Using Fuzzy VIKOR Method

Regarding to the diversified needs of domestic and global customers and various products of domestic and global competitors, importance of agility in supply chain management becomes more important. Suppliers have to provide materials and essential resources of manufacturers in a short time without any lead time.In this research, we identified many criteria for agility in sanitation supply chain. Then with utilization of Fuzzy Delphi, ideas of experts about criteria have been gathered in 8 final criteria. Next step is devoted to prioritization of five suppliers in sanitation industry based on the final criteria with fuzzy VIKORRegarding to the diversified needs of domestic and global customers and various products of domestic and global competitors, importance of agility in supply chain management becomes more important. Suppliers have to provide materials and essential resources of manufacturers in a short time without any lead time.In this research, we identified many criteria for agility in sanitation supply chain. Then with utilization of Fuzzy Delphi, ideas of experts about criteria have been gathered in 8 final criteria. Next step is devoted to prioritization of five suppliers in sanitation industry based on the final criteria with fuzzy VIKORRegarding to the diversified needs of domestic and global customers and various products of domestic and global competitors, importance of agility in supply chain management becomes more important. Suppliers have to provide materials and essential resources of manufacturers in a short time without any lead time.In this research, we identified many criteria for agility in sanitation supply chain. Then with utilization of Fuzzy Delphi, ideas of experts about criteria have been gathered in 8 final criteria. Next step is devoted to prioritization of five suppliers in sanitation industry based on the final criteria with fuzzy VIKO

Modification of fuzzy logic rule base in the optimization of traffic light control system

Road intersections, bad roads, accidents, road construction works, emergencies, etc. are some of the primary causes of high traffic congestions in urban areas. In an attempt to solve some of these problems, traffic wardens and traffic light control systems are employed at road intersections to ensure that deadlocks are avoided. However, the use of traffic warden is associated with weariness which can lead to poor judgement in allocating the right of way to motorist. An alternative approach is to employ the use of Traffic light control system in the management of the increased traffic congestion that is always experience in urban areas. The use of dynamic phase scheduling traffic control system has proven more efficient as compared to the static phase scheduling traffic control system. In this paper, an attempt was made to improve upon an earlier optimized traffic light control system developed using simulation of urban mobility (SUMO) in conjunction with fuzzy inference system which played the role of optimizing the traffic light control system. The modified fuzzy rule based gave a superior average waiting time of 72.07% improvement as compared to an earlier average waiting time improvement of 65.35%. This is an indication that amongst other factors, the size of the fuzzy rule base plays a significant role when fuzzy logic is employed in the optimization of traffic light control systems.Keywords: Fuzzy Logic Controller, Dynamic Automated Traffic Light System, Static Automated Traffic Light Syste

Theoretical optimisation of IT/IS investments: A research note

The justification of Information Technology (IT) is inherently fuzzy, both in theory and practice. The reason for this is due to the largely intangible dimensions of IT projects. In view of this, this research note presents the results of on-going research, in the application of Fuzzy Cognitive Mapping (FCM), as a tool to identify complex functional interrelationships associated with the justification of IT. This paper presents a theoretical functional model which describes these relationships, and by using an FCM, further interrelationships are developed in the context of justifying IT projects. A procedure which would address the optimisation of these intangible relationships in the form of a Genetic Algorithm (GA) is proposed as a process for Investment Justification

A revised perspective on the evaluation of IT/IS investments using an evolutionary approach

On-going research into the evaluation of Information Technology (IT) / Information Systems (IS) projects has shown that aerospace and supply chain industries are needing to address the issue of effective project investment in order to gain technological and competitive advantage. The evaluative nature of the justification process requires a mapping of interrelated quantities to be optimised. Earlier work by the authors (Irani and Sharif 1997) has presented a theoretical functional model that describes these relationships in turn. By applying a fuzzy mapping to these variables, the optimisation of intangible relationships in the form of a Genetic Algorithm (GA) is proposed as a method for investment justification. This paper revises and reviews these key concepts and provides a recapitulation of this optimisation problem in terms of long-term strategy options and cost implications. Glossary of terms : DC = Direct Costs, FA = Financial Appraisal, FR = Financial Risks, FUR = Functional Risks, HC = Human Costs, IC = Indirect Costs, IR = Infrastructural Risks, OB = Operational Benefits, OC = Organisational Costs, PB = Project Benefits, PC = Project Costs, RF = Risk Factor, SB = Strategic Benefits, SM = Strategic medium-term benefit, SR = Systemic Risks, TB = Tangible Benefits, TC = Tangible Costs, TL = project lead time, TR = Technological Risks, V= Project Value