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

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

An integrated approach to value chain analysis of end of life aircraft treatment

Dans cette thèse, on propose une approche holistique pour l’analyse, la modélisation et l’optimisation des performances de la chaîne de valeur pour le traitement des avions en fin de vie (FdV). Les recherches réalisées ont débouché sur onze importantes contributions. Dans la première contribution, on traite du contexte, de la complexité, de la diversité et des défis du recyclage d’avions en FdV. La seconde contribution traite du problème de la prédiction du nombre de retraits d’avions et propose une approche intégrée pour l’estimation de ce nombre de retraits. Le troisième et le quatrième articles visent à identifier les parties prenantes, les valeurs perçues par chaque partenaire et indiquent comment cette valeur peut affecter les décisions au stade de la conception. Les considérations relatives à la conception et à la fabrication ont donné lieu à quatre contributions importantes. La cinquième contribution traite des défis et opportunités pouvant résulter de l’application des concepts de la chaîne logistique verte, pour les manufacturiers d’avions. Dans la sixième contribution, un outil d’aide à la décision a été développé pour choisir la stratégie verte qui optimise les performances globales de de toute la chaîne de valeur en tenant compte des priorités et contraintes de chaque partenaire. Dans la septième contribution, un modèle mathématique est proposé pour analyser le choix stratégique des manufacturiers en réponse aux directives en matière de FdV de produits comme le résultat des interactions des compétiteurs dans le marché. La huitième contribution porte sur les travaux réalisés dans le cadre d’un stage chez le constructeur d’avions, Bombardier. Cette dernière traite de l’apport de « l’analyse du cycle de vie » au stade de la conception d’avions. La neuvième contribution introduit une méthodologie d’analyse de la chaîne de valeur dans un contexte de développement durable. Finalement, les dixième et onzième contributions proposent une approche holistique pour le traitement des avions en FdV en intégrant les concepts du « lean », du développement durable et des contraintes et opportunités inhérentes à la mondialisation des affaires. Un modèle d’optimisation intégrant les modèles d’affaires, les stratégies de désassemblage et les structures du réseau qui influencent l’efficacité, la stabilité et l’agilité du réseau de récupération est proposé. Les données requises pour exploiter le modèle sont indiquées dans l’article. Mots-clés: Fin de vie des avions, analyse de la chaîne de valeurs, développement durable, intervenants.The number of aircrafts at the end of life (EOL) is continuously increasing. Dealing with retired aircrafts considering the environmental, social and economic impacts is becoming an emerging problem in the aviation industry in near future. This thesis seeks to develop a holistic approach in order to analyze the value chain of EOL aircraft treatment in the context of sustainable development. The performed researches have led to eleven main contributions. In the first contribution, the complexity and diversity of the EOL aircraft recycling including the challenges and problem context are discussed. The second contribution addresses the challenges for estimation of retired aircrafts and proposes an integrated approach for prediction of EOL aircrafts. The third and fourth contributions aim to identify the players involved in EOL recycling context, values perceived by different shareholders and formulate that how such value can affect design decisions. Design stage consideration and manufacture’s issues are discussed and have led to four main contributions. The fifth contribution addresses the opportunities and challenges of applying green supply chain for aircraft manufacturers. In the sixth contribution, a decision tool is developed to aid manufactures in early stage of design for their green strategy choices. In the seventh contribution, a mathematical model is developed in order to analyze the strategic choice of manufacturers in response to EOL directives as the result of the interaction of competitors in the market. An internship project has been also performed in Bombardier and led to the eighth contribution, which addresses life cycle approach and incorporating the sustainability in early stage of design of aircraft. The ninth contribution introduces a methodology for analyzing the value chain in the context of sustainable development. Finally, the tenth and eleventh contributions propose a holistic approach to EOL aircraft treatment considering lean principals, sustainable development, and global business environment. An optimization model is developed to support decision making in both strategic and managerial level. The analytical approaches, decision tools and step by step guidelines proposed in this thesis will aid decision makers to identify appropriate strategies for the EOL aircraft treatment in the sustainable development context. Keywords: End of life aircraft, value chain analysis, sustainable development, stakeholders

Fuzzy Sets Applications in Civil Engineering Basic Areas

Civil engineering is a professional engineering discipline that deals with the design, construction, and maintenance of the physical and naturally built environment, including works like roads, bridges, canals, dams, and buildings. This paper presents some Fuzzy Logic (FL) applications in civil engeering discipline and shows the potential of facilities of FL in this area. The potential role of fuzzy sets in analysing system and human uncertainty is investigated in the paper. The main finding of this inquiry is FL applications used in different areas of civil engeering discipline with success. Once developed, the fuzzy logic models can be used for further monitoring activities, as a management tool

Driving Sustainability through Engineering Management and Systems Engineering

Despite the ongoing impact of the COVID-19 pandemic, the challenge of realizing sustainability across the triple bottom line of social, environmental, and economic development remains an urgent priority. If anything, it is now imperative that we work towards achieving the United Nations Sustainable Development Goals (SDGs). However, the global challenges are significant. Many of the societal challenges represent complex problems that require multifaceted solutions drawing on multidisciplinary approaches. Engineering management involves the management of people and projects related to technological or engineering systems—this includes project management, engineering economy, and technology management, as well as the management and leadership of teams. Systems engineering involves the design, integration, and management of complex systems over the full life cycle—this includes requirements capture, integrated system design, as well as modelling and simulation. In addition to the theoretical underpinnings of both disciplines, they also provide a range of tools and techniques that can be used to address technological and organisational complexity. The disciplines of engineering management and systems engineering are therefore ideally suited to help tackle both the challenges and opportunities associated with realising a sustainable future for all. This book provides new insights on how engineering management and systems engineering can be utilised as part of the journey towards sustainability. The book includes discussion of a broad range of different approaches to investigate sustainability through utilising quantitative, qualitative and conceptual methodologies. The book will be of interest to researchers and students focused on the field of sustainability as well as practitioners concerned with devising strategies for sustainable development

Driving Sustainability through Engineering Management and Systems Engineering

Despite the ongoing impact of the COVID-19 pandemic, the challenge of realizing sustainability across the triple bottom line of social, environmental, and economic development remains an urgent priority. If anything, it is now imperative that we work towards achieving the United Nations Sustainable Development Goals (SDGs). However, the global challenges are significant. Many of the societal challenges represent complex problems that require multifaceted solutions drawing on multidisciplinary approaches.Engineering management involves the management of people and projects related to technological or engineering systems—this includes project management, engineering economy and technology management, as well as the management and leadership of teams. Systems engineering involves the design, integration and management of complex systems over the full life cycle—this includes requirements capture and integrated system design, as well as modelling and simulation. In addition to the theoretical underpinnings of both disciplines, they also provide a range of tools and techniques that can be used to address technological and organisational complexity. The disciplines of engineering management and systems engineering are therefore ideally suited to help tackle both the challenges and the opportunities associated with realising a sustainable future for all.This book provides new insights on how engineering management and systems engineering can be utilised as part of the journey towards sustainability. The book includes a discussion of a broad range of different approaches to investigate sustainability through utilising quantitative, qualitative and conceptual methodologies. The book will be of interest to researchers and students focused on the field of sustainability as well as practitioners concerned with devising strategies for sustainable development

Intelligent Computing: The Latest Advances, Challenges and Future

Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human-computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing. Intelligent computing is still in its infancy and an abundance of innovations in the theories, systems, and applications of intelligent computing are expected to occur soon. We present the first comprehensive survey of literature on intelligent computing, covering its theory fundamentals, the technological fusion of intelligence and computing, important applications, challenges, and future perspectives. We believe that this survey is highly timely and will provide a comprehensive reference and cast valuable insights into intelligent computing for academic and industrial researchers and practitioners

Unsupervised Intrusion Detection with Cross-Domain Artificial Intelligence Methods

Cybercrime is a major concern for corporations, business owners, governments and citizens, and it continues to grow in spite of increasing investments in security and fraud prevention. The main challenges in this research field are: being able to detect unknown attacks, and reducing the false positive ratio. The aim of this research work was to target both problems by leveraging four artificial intelligence techniques. The first technique is a novel unsupervised learning method based on skip-gram modeling. It was designed, developed and tested against a public dataset with popular intrusion patterns. A high accuracy and a low false positive rate were achieved without prior knowledge of attack patterns. The second technique is a novel unsupervised learning method based on topic modeling. It was applied to three related domains (network attacks, payments fraud, IoT malware traffic). A high accuracy was achieved in the three scenarios, even though the malicious activity significantly differs from one domain to the other. The third technique is a novel unsupervised learning method based on deep autoencoders, with feature selection performed by a supervised method, random forest. Obtained results showed that this technique can outperform other similar techniques. The fourth technique is based on an MLP neural network, and is applied to alert reduction in fraud prevention. This method automates manual reviews previously done by human experts, without significantly impacting accuracy