Global method for a class of operation optimization problem in steel rolling systems

Many steel products are produced in hot or cold rolling lines with multiple stands. The steel material becomes thinner after being rolled at each stand. Steady-state parameters for controlling the rolling line need to be set so as to satisfy the final product specifications and minimize the total energy consumption. This paper develops a generalized geometric programming model for this setting problem and proposes a global method for solving it. The model can be expressed with a linear objective function and a set of constraints including nonconvex ones. Through constructing lower bounds of some components, the constraints can be converted to convex ones approximately. A sequential approximation method is proposed in a gradually reduced interval to improve accuracy and efficiency. However, the resulting convex programming model in each iteration is still complicated. To reduce the power, it is transformed into a second-order cone programming (SOCP) model and solved using alternating direction method of multipliers (ADMM). The effectiveness of the global method is tested using real data from a hot-rolling line with seven stands. The results demonstrate that the proposed global method solves the problem effectively and reduces the energy consumption

A scheduling model for production in a hot strip mill

M.Ing.This research dissertation highlights the important role of scheduling in a production environment. The functioning of an integrated iron and steel works is discussed. The importance of production scheduling in this environment is shown, followed by a literature survey of strip mill production scheduling models. Thereafter a model is introduced that aids in the production scheduling of plate via coil in a hot strip mill. Finally the benefits of the scheduling model are shown

Material and energy flows of the iron and steel industry: status quo, challenges and perspectives

Integrated analysis and optimization of material and energy flows in the iron and steel industry have drawn considerable interest from steelmakers, energy engineers, policymakers, financial firms, and academic researchers. Numerous publications in this area have identified their great potential to bring significant benefits and innovation. Although much technical work has been done to analyze and optimize material and energy flows, there is a lack of overview of material and energy flows of the iron and steel industry. To fill this gap, this work first provides an overview of different steel production routes. Next, the modelling, scheduling and interrelation regarding material and energy flows in the iron and steel industry are presented by thoroughly reviewing the existing literature. This study selects eighty publications on the material and energy flows of steelworks, from which a map of the potential of integrating material and energy flows for iron and steel sites is constructed. The paper discusses the challenges to be overcome and the future directions of material and energy flow research in the iron and steel industry, including the fundamental understandings of flow mechanisms, the dynamic material and energy flow scheduling and optimization, the synergy between material and energy flows, flexible production processes and flexible energy systems, smart steel manufacturing and smart energy systems, and revolutionary steelmaking routes and technologies

Evaluation of sales and operations planning in a process industry

Cette thèse porte sur la planification des ventes et des opérations (S±&OP) dans une chaîne d'approvisionnements axée sur la demande. L'objectif de la S±&OP, dans un tel contexte, est de tirer profit de l'alignement de la demande des clients avec la capacité de la chaîne d'approvisionnement par la coordination de la planification des ventes, de la production, de la distribution et de l'approvisionnement. Un tel processus de planification exige une collaboration multifonctionnelle profonde ainsi que l'intégration de la planification. Le but étant d'anticiper l'impact des décisions de vente sur les performances de la chaîne logistique , alors que l'influence de la dynamique des marchés est prise en compte pour les décisions concernant la production, la distribution et l'approvisionnement. La recherche a été menée dans un environnement logistique manufacturier multi-site et multi-produit, avec un approvisionnement et des ventes régis par des contrats ou le marché. Cette thèse examine deux approches de S±&OP et fournit un support à la décision pour l'implantation de ces méthodes dans une chaîne logistique multi-site de fabrication sur commande. Dans cette thèse, une planification traditionnelle des ventes et de la production basée sur la S±feOP et une planification S±fcOP plus avancée de la chaîne logistique sont tout d'abord caractérisées. Dans le système de chaîne logistique manufacturière multi-site, nous définissons la S±&OP traditionnelle comme un système dans lequel la planification des ventes et de la production est effectuée conjointement et centralement, tandis que la planification de la distribution et de l'approvisionnement est effectuée séparément et localement à chaque emplacement. D'autre part, la S±fcOP avancée de la chaîne logistique consiste en la planification des ventes, de la production, de la distribution et de l'approvisionnement d'une chaîne d'approvisionnement effectuée conjointement et centralement. Basés sur cette classification, des modèles de programmation en nombres entiers et des modèles de simulation sur un horizon roulant sont développés, représentant, respectivement, les approches de S±&OP traditionnelle et avancée, et également, une planification découplée traditionnelle, dans laquelle la planification des ventes est effectuée centralement et la planification de la production, la distribution et l'approvisionnement est effectuée séparément et localement par les unités d'affaires. La validation des modèles et l'évaluation pré-implantation sont effectuées à l'aide d'un cas industriel réel utilisant les données d'une compagnie de panneaux de lamelles orientées. Les résultats obtenus démontrent que les deux méthodes de S±feOP (traditionnelle et avancée) offrent une performance significativement supérieure à celle de la planification découplée, avec des bénéfices prévus supérieurs de 3,5% et 4,5%, respectivement. Les résultats sont très sensibles aux conditions de marché. Lorsque les prix du marché descendent ou que la demande augmente, de plus grands bénéfices peuvent être réalisés. Dans le cadre de cette recherche, les décisions de vente impliquent des ventes régies par des contrats et le marché. Les décisions de contrat non optimales affectent non seulement les revenus, mais également la performance manufacturière et logistique et les décisions de contrats d'approvisionnement en matière première. Le grand défi est de concevoir et d'offrir les bonnes politiques de contrat aux bons clients de sorte que la satisfaction des clients soit garantie et que l'attribution de la capacité de la compagnie soit optimisée. Également, il faut choisir les bons contrats des bons fournisseurs, de sorte que les approvisionnements en matière première soient garantis et que les objectifs financiers de la compagnie soient atteints. Dans cette thèse, un modèle coordonné d'aide à la décision pour les contrats e développé afin de fournir une aide à l'intégration de la conception de contrats, de l'attribution de capacité et des décisions de contrats d'approvisionnement pour une chaîne logistique multi-site à trois niveaux. En utilisant la programmation stochastique à deux étapes avec recours, les incertitudes liées à l'environnement et au système sont anticipées et des décisions robustes peuvent être obtenues. Les résultats informatiques montrent que l'approche de modélisation proposée fournit des solutions de contrats plus réalistes et plus robustes, avec une performance prévue supérieure d'environ 12% aux solutions fournies par un modèle déterministe

Book of abstracts of the 14th International Symposium of Croatian Metallurgical Society - SHMD \u272020, Materials and metallurgy

Book of abstracts of the 14th International Symposium of Croatian Metallurgical Society - SHMD \u272020, Materials and metallurgy held in Šibenik, Croatia, June 21-26, 2020. Abstracts are organized in four sections: Materials - section A; Process metallurgy - Section B; Plastic processing - Section C and Metallurgy and related topics - Section D

Recent Advances in Technologies, Methods, and Economic Analysis for Sustainable Development of Energy, Water, and Environment Systems

Sustainability has become a broad societal goal, aiming to ensure that human beings coexist safely and harmoniously with nature over a longer time. The influence of the COVID-19 pandemic on the global economy is coming to an end. The development and merits of sustainable energy supply, advanced technology, and economic features have received significant attention over the last few decades. However, significant gaps still exist with respect to how to design, construct, and implement hybrid and optimal energy systems with the lowest investment and cost. Since 2002, the Sustainable Development of Energy, Water, and Environment Systems (SDEWES) conferences have become a significant meeting venue for researchers to introduce, discuss, share, and disseminate novel concepts and ideas. This paper presents an overview of published articles in the Special Issues (SIs) dedicated by the series SDEWES conferences, especially those published in Energies recommended by the 16th SDEWES Conference, which was held on 10–15 October 2021 in Dubrovnik, Croatia. This SI in Energies focused on four main topics, including the application of renewable bioenergy, component enhancement in renewable systems, sustainable development for buildings and economic analysis and evaluation for sustainability. The collected papers provide insight into the topics related to recent advances in improving sustainable efficiency, including studies on waste-to-wealth techniques, utilization of hybrid bioenergy systems, heat exchangers and other components for performance enhancement, energy supply and demand analysis, low-temperature DHC systems, techno-economic assessment, and environmental evaluation

A survey of AI in operations management from 2005 to 2009

Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the ten-year period 1995-2004. Like the previous survey, it uses Elsevier’s Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case-based reasoning (CBR), fuzzy logic (FL), knowledge-Based systems (KBS), data mining, and hybrid AI in the four application areas are identified. Findings: the survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004 (Kobbacy et al. 2007). Like the previous survey, it uses the Elsevier’s ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings: The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research