scholarly journals Development of an algorithm for risk-based management of wastewater reuse alternatives

2016 ◽  
Vol 8 (1) ◽  
pp. 38-57 ◽  
Author(s):  
Hossein Shakeri ◽  
Sara Nazif
Keyword(s):  
Risk Assessment ◽  
Risk Management ◽  
Fuzzy Inference ◽  
Wastewater Reuse ◽  
Risk Identification ◽  
Treated Wastewater ◽  
Management Approach ◽  
Inference System ◽  
Management Measures

Abstract Due to water resources limitations, special attention has been paid to wastewater reuse in recent years. The risks associated with wastewater reuse alternatives should be considered in decision-making. Even when selecting the alternative with the least risk, risk management issues are of high importance. This study aims to develop an algorithm for risk-based management of wastewater reuse alternatives. This algorithm uses a three-step risk assessment and management approach. Risks are identified, then risks of alternatives are assessed, and, finally, risk management measures are proposed for risk reduction in the selected alternative. In risk identification, economic, social, health, and environmental aspects are taken into account. In risk assessment, its three components of likelihood, severity, and vulnerability are considered through a fuzzy inference system. Alternatives are prioritized based on calculated risks using a fuzzy VIKOR method. A case study is presented in which the proposed algorithm is used to select the best alternative for reuse of treated wastewater from Ekbatan Town, located in the western part Tehran in Iran. The results showed that the proposed approach provides the users with an easier understanding of risks and increases the relative confidence of decision-makers about the selection of the best alternatives for wastewater reuse and their risk control methods.

scholarly journals RM/RA CRAMM as the new quantitative method for risk management

2018 ◽  
Vol 6 (1) ◽  
pp. 6-12
Author(s):  
Jana Müllerová
Keyword(s):  
Risk Assessment ◽  
Risk Management ◽  
Quantitative Methods ◽  
Case Study Research ◽  
Single Equation ◽  
Risk Identification ◽  
Jel Classification ◽  
Team Work ◽  
Wide Range

Purpose – The aim of this paper is to introduce new risk management method based on quantitative approach. RM/RA CRAMM was designed by Slovak researchers as user-friendly method for Public institutions dealing with risk management, crisis planning, civil protection. It has a multipurpose use. Design/methodology/approach – Three-phase procedure is introduced, including risk identification, analysis and evaluation. The case-study of risk assessment as an example of the application is included. Findings – Quantitative methods in risk management are rare due to the complex of factors influencing the risks being assessed. Research limitations/implications – A complex Area/Location risk assessment needs number of exact values and estimations for proper risk identification. Team-work is very welcomed but not necessary. Practical implications – Practical value of the method is incredible due to its applicability on the wide range of the research fields related to the risk management. Originality/Value – The method introduced is an original product of Slovak team of researchers led by author of this paper. Keywords: RM/RA CRAMM, risk management, risk assessment, quantitative, single equation method, case study. Research type: case study. JEL classification: C20 – General.

scholarly journals A Hybrid Supply Chain Risk Management Approach for Lean Green Performance Based on AHP, RCA and TRIZ: A Case Study

2021 ◽  
Vol 13 (15) ◽  
pp. 8492
Author(s):  
Fatima Ezzahra Essaber ◽  
Rachid Benmoussa ◽  
Roland De Guio ◽  
Sébastien Dubois
Keyword(s):  
Risk Management ◽  
Supply Chain ◽  
Research Work ◽  
Risk Identification ◽  
Ease Of Use ◽  
Management Approach ◽  
Green Performance ◽  
Lean And Green ◽  
Corporate Level

The purpose of this research work is to provide supply chain managers with a formal and generalizable approach that furnishes accurate guidelines to achieve a 2D performance integrating both Lean and Green. Despite the fact that several research works have been conducted in the framework of Lean and Green, at a conceptual level, the relationship between both paradigms is still ambiguous. Furthermore, the literature revealed a lack of relevant and generalizable approaches that explicitly demonstrate how to successfully implement Lean and Green in a relevant and integrated way. Since risks are the main obstacles disrupting performance, this research work addresses the identified gap by proposing a risk management approach (RMA) for Lean Green performance in a supply-chain context. Risk cannot be managed if not well-identified; hence, a rigorous literature investigation was conducted to define this concept in a supply-chain context. Later, risk was introduced into Lean and Green aspects. Subsequently, through a comprehensive review of previous risk identification studies, a novel classification of supply chain risks in a Lean Green context was provided. At a corporate level, risks often include several sources that cannot be treated at once. Therefore, a risk assessment analysis was performed, employing an analytic hierarchy process for its ease of use and broad adaptability. The output of this analysis provides visibility for an organization’s position toward performance goals and underlines crucial risks to be addressed. The risk treatment process was upgraded in this approach to a detailed analysis that aims at investigating the root causes behind the prioritized risks. Deployment of the approach on a corporate level revealed that treating a risk may negatively affect treating another. Indeed, thinking Lean is not necessarily Green, which stands with the fact that Lean Green supply chain challenges may outstrip classic optimization methods and techniques; therefore, its management requires innovative approaches. Thereby, our findings support the applicability and efficiency of the Theory of Inventive Problem Solving (TRIZ) in this setting. Although the case study focused on a specific company, the developed framework can be customized to fit different cases.

scholarly journals Utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Overcrowding Level Risk Assessment in Railway Stations

2020 ◽  
Vol 10 (15) ◽  
pp. 5156
Author(s):  
Hamad Alawad ◽  
Min An ◽  
Sakdirat Kaewunruen
Keyword(s):  
Risk Assessment ◽  
Risk Management ◽  
Real Time ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Risk Level ◽  
Railway Stations ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Managing Risk

The railway network plays a significant role (both economically and socially) in assisting the reduction of urban traffic congestion. It also accelerates the decarbonization in cities, societies and built environments. To ensure the safe and secure operation of stations and capture the real-time risk status, it is imperative to consider a dynamic and smart method for managing risk factors in stations. In this research, a framework to develop an intelligent system for managing risk is suggested. The adaptive neuro-fuzzy inference system (ANFIS) is proposed as a powerful, intelligently selected model to improve risk management and manage uncertainties in risk variables. The objective of this study is twofold. First, we review current methods applied to predict the risk level in the flow. Second, we develop smart risk assessment and management measures (or indicators) to improve our understanding of the safety of railway stations in real-time. Two parameters are selected as input for the risk level relating to overcrowding: the transfer efficiency and retention rate of the platform. This study is the world’s first to establish the hybrid artificial intelligence (AI) model, which has the potency to manage risk uncertainties and learns through artificial neural networks (ANNs) by integrated training processes. The prediction result shows very high accuracy in predicting the risk level performance, and proves the AI model capabilities to learn, to make predictions, and to capture risk level values in real time. Such risk information is extremely critical for decision making processes in managing safety and risks, especially when uncertain disruptions incur (e.g., COVID-19, disasters, etc.). The novel insights stemmed from this study will lead to more effective and efficient risk management for single and clustered railway station facilities towards safer, smarter, and more resilient transportation systems.

Investigating the potential of redundant manipulators in narrow channels

2020 ◽  
pp. 095440622096451
Author(s):  
Ashish Singla ◽  
Jyotindra Narayan ◽  
Himanshu Arora
Keyword(s):  
Case Studies ◽  
Fuzzy Inference ◽  
Redundant Manipulators ◽  
Redundancy Resolution ◽  
Inference System ◽  
Narrow Channels ◽  
First Case ◽  
Task Priority ◽  
Patient Side

In this paper, an attempt has been made to investigate the potential of redundant manipulators, while tracking trajectories in narrow channels. The behavior of redundant manipulators is important in many challenging applications like under-water welding in narrow tanks, checking the blockage in sewerage pipes, performing a laparoscopy operation etc. To demonstrate this snake-like behavior, redundancy resolution scheme is utilized using two different approaches. The first approach is based on the concept of task priority, where a given task is split and prioritize into several subtasks like singularity avoidance, obstacle avoidance, torque minimization, and position preference over orientation etc. The second approach is based on Adaptive Neuro Fuzzy Inference System (ANFIS), where the training is provided through given datasets and the results are back-propagated using augmentation of neural networks with fuzzy logics. Three case studies are considered in this work to demonstrate the redundancy resolution of serial manipulators. The first case study of 3-link manipulator is attempted with both the approaches, where the objective is to track the desired trajectory while avoiding multiple obstacles. The second case study of 7-link manipulator, tracking trajectory in a narrow channel, is investigated using the concept of task priority. The realistic application of minimum-invasive surgery (MIS) based trajectory tracking is considered as the third case study, which is attempted using ANFIS approach. The 5-link spatial redundant manipulator, also known as a patient-side manipulator being developed at CSIR-CSIO, Chandigarh is used to track the desired surgical cuts. Through the three case studies, it is well demonstrated that both the approaches are giving satisfactory results.

Sustainable management of water demand using fuzzy inference system: a case study of Kenyir Lake, Malaysia

2021 ◽  
Author(s):  
Nor Najwa Irina Mohd Azlan ◽  
Marlinda Abdul Malek ◽  
Maslina Zolkepli ◽  
Jamilah Mohd Salim ◽  
Ali Najah Ahmed
Keyword(s):  
Fuzzy Inference System ◽  
Water Demand ◽  
Sustainable Management ◽  
Fuzzy Inference ◽  
Inference System ◽  
System A

scholarly journals PROPOSING A NEW METHODOLOGY BASED ON FUZZY LOGIC FOR TUNNELLING RISK ASSESSMENT

2014 ◽  
Vol 20 (1) ◽  
pp. 82-94 ◽  
Author(s):  
Abdolreza Yazdani-Chamzini
Keyword(s):  
Risk Assessment ◽  
Fuzzy Inference ◽  
Assessment Model ◽  
Tunnel Construction ◽  
Construction Operations ◽  
Protective Measures ◽  
Inference System ◽  
Proposed Model ◽  
Mine Development ◽  
Civil Defence

Tunnels are artificial underground spaces that provide a capacity for particular goals such as storage, under-ground transportation, mine development, power and water treatment plants, civil defence. This shows that the tunnel construction is a key activity in developing infrastructure projects. In many situations, tunnelling projects find themselves involved in the situations where unexpected conditions threaten the continuity of the project. Such situations can arise from the prior knowledge limited by the underground unknown conditions. Therefore, a risk analysis that can take into account the uncertainties associated with the underground projects is needed to assess the existing risks and prioritize them for further protective measures and decisions in order to reduce, mitigate and/or even eliminate the risks involved in the project. For this reason, this paper proposes a risk assessment model based on the concepts of fuzzy set theory to evaluate risk events during the tunnel construction operations. To show the effectiveness of the proposed model, the results of the model are compared with those of the conventional risk assessment. The results demonstrate that the fuzzy inference system has a great potential to accurately model such problems.

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