Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

An advanced framework for leakage risk assessment of hydrogen refueling stations using interval-valued spherical fuzzy sets (IV-SFS)

The extensive population growth calls for substantial studies on sustainable development in urban areas. Thus, it is vital for cities to be resilient to new situations and adequately manage the changes. Investing in renewable and green energy, including high-tech hydrogen infrastructure, is crucial for sustainable economic progress and for preserving environmental quality. However, implementing new technology needs an effective and efficient risk assessment investigation to minimize the risk to an acceptable level or ALARP (As low as reasonably practicable). The present study proposes an advanced decision-making framework to manage the risk of hydrogen refueling station leakage by adopting the Bow-tie analysis and Interval-Value Spherical Fuzzy Sets to properly deal with the subjectivity of the risk assessment process. The outcomes of the case study illustrate the causality of hydrogen refueling stations' undesired events and enhance the decision-maker's thoughts about risk management under uncertainty. According to the findings, jet fire is a more likely accident in the case of liquid hydrogen leakage. Furthermore, equipment failure has been recognized as the most likely cause of hydrogen leakage. Thus, in order to maintain the reliability of liquid hydrogen refueling stations, it is crucial that decision-makers develop a trustworthy safety management system that integrates a variety of risk mitigation measures including asset management strategies

Assessment of Energy Systems Using Extended Fuzzy AHP, Fuzzy VIKOR, and TOPSIS Approaches to Manage Non-Cooperative Opinions

Energy systems planning commonly involves the study of supply and demand of power, forecasting the trends of parameters established on economics and technical criteria of models. Numerous measures are needed for the fulfillment of energy system assessment and the investment plans. The higher energy prices which call for diversification of energy systems and managing the resolution of conflicts are the results of high energy demand for growing economies. Due to some challenging problems of fossil fuels, energy production and distribution from alternative sources are getting more attention. This study aimed to reveal the most proper energy systems in Saudi Arabia for investment. Hence, integrated fuzzy AHP (Analytic Hierarchy Process), fuzzy VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje) and TOPSIS (Technique for Order Preferences by Similarity to Idle Solution) methodologies were employed to determine the most eligible energy systems for investment. Eight alternative energy systems were assessed against nine criteria—power generation capacity, efficiency, storability, safety, air pollution, being depletable, net present value, enhanced local economic development, and government support. Data were collected using the Delphi method, a team of three decision-makers (DMs) was established in a heterogeneous manner with the addition of nine domain experts to carry out the analysis. The fuzzy AHP approach was used for clarifying the weight of criteria and fuzzy VIKOR and TOPSIS were utilized for ordering the alternative energy systems according to their investment priority. On the other hand, sensitivity analysis was carried out to determine the priority of investment for energy systems and comparison of them using the weight of group utility and fuzzy DEA (Data Envelopment Analysis) approaches. The results and findings suggested that solar photovoltaic (PV) is the paramount renewable energy system for investment, according to both fuzzy VIKOR and fuzzy TOPSIS approaches. In this context our findings were compared with other works comprehensively.This research was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, under grant no. (RG-7-135-38). The authors, therefore, acknowledge with thanks DSR technical and financial support

Quantifying reputation loss of pipeline operator from various stakeholders perspectives, part 1: prioritization

Quantifying reputation loss (RL) due to pipeline damage is commonly generalized based on the owner's definition. This one-way perspective of portraying RL is unfair and unrealistic and consequently miscalculates the impact assessment of pipeline damage; hence, inaccurate risk prediction. It is crucial to develop a model to quantify qualitative RL to avoid unpredicted risk. Thus, this article provides a framework for a procedure to calculate RL by utilizing the factors identified in a previous study. In this paper (Part 1), the prioritization of factors based on the stakeholders' perspectives is presented. The factors were grouped into stakeholder-influenced categories and prioritized by a fuzzy analytic hierarchy process based on the feedback gained from the stakeholders, i.e., investors, customers, employees and the public. The result shows that factor D3, “Accident severity”, was ranked highest by all stakeholders. The priority vector for each factor obtained was assigned as a weight of the factor. The pipeline owner's reputation loss model (RLM) is developed by applying the obtained priority vectors in the subsequent paper (Part 2). The developed model was verified by experts as a comprehensive, clear, objective, practical and moderately reliable model. The model was applied to a case study and eventually produced a lower risk value when compared with the currently used model. It is proven that RL factors can be quantitatively measured and can simultaneously improve pipeline damage impact assessment. Thus, a risk-based inspection schedule can be managed comprehensively

Aerospace Medicine and Biology: A continuing bibliography, supplement 191

A bibliographical list of 182 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1979 is presented

A rule based fuzzy synthetic evaluation method for risk assessment in pipeline transport

Integrity Management Program (IMP) for oil and gas pipeline system demands a robust risk assessment method, which needs fully utilize the expert knowledge on the empirical data and the ambiguous cause(s) – effect(s) failure mechanism and then keep a good balance between precision and practicality. A new method, Rule Based Fuzzy Synthetic Evaluation (RB-FSE) which combines Fuzzy Synthetic Evaluation (FSE) with Fuzzy Logic (FLo) is proposed in this paper. It is applied to the pipeline risk assessment for Third-Party Damage (TPD). The proposed method is compared with scoring-type method in a case study. Results indicate that the relative assessment values from RB-FSE model could better support risk-ranking and decision-making in the IMP

SciTech News Volume 71, No. 2 (2017)

Columns and Reports From the Editor 3 Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division 9 Aerospace Section of the Engineering Division 12 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 14 Reviews Sci-Tech Book News Reviews 16 Advertisements IEEE