Modular reactors: What can we learn from modular industrial plants and off site construction research

New modular factory-built methodologies implemented in the construction and industrial plant industries may bring down costs for modular reactors. A factory-built environment brings about benefits such as; improved equipment, tools, quality, shift patterns, training, continuous improvement learning, environmental control, standardisation, parallel working, the use of commercial off shelf equipment and much of the commissioning can be completed before leaving the factory. All these benefits combine to reduce build schedules, increase certainty, reduce risk and make financing easier and cheaper.Currently, the construction and industrial chemical plant industries have implemented successful modular design and construction techniques. Therefore, the objectives of this paper are to understand and analyse the state of the art research in these industries through a systematic literature review. The research can then be assessed and applied to modular reactors.The literature review highlighted analysis methods that may prove to be useful. These include; modularisation decision tools, stakeholder analysis, schedule, supply chain, logistics, module design tools and construction site planning. Applicable research was highlighted for further work exploration for designers to assess, develop and efficiently design their modular reactors

Strategic and Tactical Crude Oil Supply Chain: Mathematical Programming Models

Crude oil industry very fast became a strategic industry. Then, optimization of the Crude Oil Supply Chain (COSC) models has created new challenges. This fact motivated me to study the COSC mathematical programming models. We start with a systematic literature review to identify promising avenues. Afterwards, we elaborate three concert models to fill identified gaps in the COSC context, which are (i) joint venture formation, (ii) integrated upstream, and (iii) environmentally conscious design

An integrated decision support system based on simulation and mathematical programming of Petroleum transportation logistics

Discrete Event simulation (DES), mathematical programming (MP) and analysis of variance (ANOVA) are among the popular tools in operational research (OR) used in dynamic industry like petroleum industry. The integration of these methods even becomes more significant to managerial application in the industry. The objective of this thesis is to present an integrated decision support system by which a decision maker should be able to choose the optimal number of tanks, tank size and truck arrival rate to maximize average total profit and minimize the total transportation cost for an oil refinery terminal operations. The petroleum transportation management system (PTMS) is developed as a DSS using a discrete-event simulation program with ARENA software, mathematical linear programming (LP) with I-Log software and analysis of variance (ANOVA) with SPSS software, and these models are combined in complex program developed using visual basic software (VB). The simulation model represents the logistics operations from oil arriving to the refinery terminal to the supply points. The model process used as a decision support tool to help in evaluating and improving the comprehensive oil terminal operations. And also understanding and assessing of the different steps in a simulation process. An optimization model was formulated with the objective to minimize the total transportation cost. In the model formulation, hard constraints were considered and the linear programming (LP) technique was used. Result obtained suggests the use of certain types of trucks can reduce the operation costs, if compared to that of the current situation. The reduction of costs is due to the reduction of travelling trips as based on the problem constraints. Overall, output of this study has given positive impacts on the transportation operations. The effect of the changes can help the management of the transportation company to make efficient decisions. Multifactor ANOVA is used to determine whether different levels of the three-factors and their interactions significantly impact the oil refinery terminal's profit. ANOVA is also used to determine the flow rate of oil into the tanks station; tank and truck fill rate and a cost and revenue structure. The final step is to expand the model to cover the whole models (DES, LP and ANOVA) and create the integrated user interface. To sum up the combination of these techniques which allows evaluating the actual feasibility of supply planning considering all operations restrictions and variability of the supply logistics and the total transportation cost. In another words, a DSS have been developed to support a decision maker, who is planning to build a new facility or expand an existing oil refinery terminal, should be able to choose the optimal value for all important factors. The PTMS is able to predict with 99% confidence a set of factor levels that yields the highest average total profit

Strategic development in the petrochemical industry

Imperial Users onl

Knowledge management technology for integrated decision support systems in process industries

Premi extraordinari doctorat curs 2011-2012, àmbit d’Enginyeria IndustrialNowadays, factors such as globalization of trade, market uncertainty and fierce competition involve dwindling error margins in enterprises. Two key aspects for achieve it are the viability and the competitiveness of enterprises, which highly depend on the effectiveness for taking their decisions related to their manufacturing characteristics, such as economic efficiency, product quality, flexibility or reliability. For this reason, companies have taken the task, for many years, of develop better management information systems in order to help the decision makers to exploit data and models, with the final objective of discussing and improving decision-making. In this sense, decision support systems must be improved in order to deal with the large amount of available data and the heterogeneity of existing modeling approaches along the hierarchical levels in the enterprise structure. Hence, this thesis proposes the application of ontologies as a decision support tool, since they are increasingly seen as a key semantic technology for addressing heterogeneities and mitigating the problems they create and for enabling data mining by semantics-driven the knowledge processing. The aim of this thesis is to contribute to the development of decision support tools for the enterprise process industry. As a decision support tool, must be capable of become a robust model which interacts among the different decision hierarchical levels, providing a unified framework of data and information levels integration. On the other hand, this thesis also aims the improvement in the development of the ontologies. Firstly, a detailed state of the art about the different production process systems, knowledge management base on ontologies, as well as decision support systems is carried out. Based on this review, the specific thesis objectives are posed. Next, a methodology is proposed for the development and use of ontologies, based on the analysis and adaptation of previously existing methodologies. Such methodology is based on the improvement cycle (PSDA), allowing a better way to design, construct and apply domain ontologies. The second part of this thesis is devoted to the application of the different parts of the previously proposed methodology for the development of an ontological framework in the process industry domain concerning the strategic, tactical and operational decision levels. Next, the description of the decision areas in which the ontological framework is applied is presented. Namely, in the process control decision level, the coordination control is considered. Regarding scheduling decisions level, mathematical optimization approaches are applied. Finally, the distributed hierarchical decision level considers the mathematical optimization for decentralized supply chain networks is adopted. These decision areas and the performance of the proposed framework interaction are studied along the different case studies presented in the thesis. On the whole, this thesis represents a step forward toward the integration among the enterprise hierarchical levels, the process and enterprise standardization and improved procedures for decision-making. The aforementioned achievements are boosted by the application of semantic models, which are currently increasingly used.En la actualidad, factores como la globalización del comercio, la incertidumbre del mercado y la feroz competencia implican la disminución de los márgenes de error en las empresas. Dos aspectos claves para lograrlo son la viabilidad y la competitividad de las enterprisesm, que dependen en gran medida la eficacia para la toma de sus decisiones relacionadas con sus características de fabricación, tales como eficiencia económica, la calidad del producto, la flexibilidad y fiabilidad. Por esta razón, las empresas han dado a la tarea, desde hace muchos años, de desarrollar mejores sistemas de gestión de la información con el fin de ayudar a los tomadores de decisiones de explotación de datos y modelos, con el objetivo final de la discusión y mejorar la toma de decisiones. En este sentido, los sistemas de apoyo a las decisiones deben ser mejorados con el fin de hacer frente a la gran cantidad de datos disponibles y la heterogeneidad de los métodos de modelización existentes a lo largo de los niveles jerárquicos en la estructura de la empresa. Por lo tanto, esta tesis se propone la aplicación de ontologías como herramienta de apoyo a la decisión, ya que son cada vez más como una tecnología clave semántica para hacer frente a las heterogeneidades y la mitigación de los problemas que crean y para permitir la extracción de datos por la semántica impulsado la elaboración del conocimiento. El objetivo de esta tesis es contribuir al desarrollo de herramientas de apoyo para la industria de procesos empresariales. Como una herramienta de apoyo a la decisión, debe ser capaz de convertirse en un modelo sólido que interactúa entre los diferentes niveles de decisión jerárquica, proporcionando un marco unificado de datos e integración de los niveles de información. Por otra parte, esta tesis también tiene como objetivo la mejora en el desarrollo del área de ingeniería ontológica. En primer lugar, un estado detallado de la técnica sobre los diferentes sistemas de procesos de producción, la base de la gestión del conocimiento en ontologías, así como los sistemas de soporte de decisiones se ha llevado a cabo. Basado en esa revision, los objetivos específicos de la tesis se plantean. A continuación, se propone una metodología para el desarrollo y uso de ontologías, con base en el análisis y adaptación de las metodologías ya existentes. Dicha metodología se basa en el ciclo de mejora (PSDA), lo que permite una mejor manera de diseñar, construir y aplicar las ontologías de dominio. La segunda parte de esta tesis se dedica a la aplicación de las diferentes partes de la metodología propuesta anteriormente para el desarrollo de un marco ontológico en el ámbito de la industria de procesos relativos a los niveles de decisiones estratégicas, tácticas y operativas. A continuación, la descripción de las áreas de decisión en la que se aplica el marco ontológico se presenta. Es decir, en el nivel de decision de proceso de control, el control de la coordinación se considera. En cuanto al nivel de decisiones de programación de la producción, los métodos matemáticos de optimización se aplican. Finalmente, el nivel jerárquico distribuido decisión considera la optimización matemática de las redes descentralizadas de la cadena de suministro que se adopte. Estas áreas de decisión y el desempeño de la interacción marco propuesto se estudian a lo largo de los diferentes casos de estudio presentados en la tesis. En general, esta tesis supone un paso hacia adelante en la integración entre los niveles jerárquicos de la empresa, el proceso y la estandarización de la empresa y mejorar los procedimientos de toma de decisiones. Los logros mencionados se potencian mediante la aplicación de modelos semánticos, que actualmente se utilizan cada vez más.Award-winningPostprint (published version

A unit cost adjusting heuristic algorithm for the integrated planning and scheduling of a two-stage supply chain

Purpose: The stable relationship of one-supplier-one-customer is replaced by a dynamic relationship of multi-supplier-multi-customer in current market gradually, and efficient scheduling techniques are important tools of the dynamic supply chain relationship establishing process. This paper studies the optimization of the integrated planning and scheduling problem of a two-stage supply chain with multiple manufacturers and multiple retailers to obtain a minimum supply chain operating cost, whose manufacturers have different production capacities, holding and producing cost rates, transportation costs to retailers. Design/methodology/approach: As a complex task allocation and scheduling problem, this paper sets up an INLP model for it and designs a Unit Cost Adjusting (UCA) heuristic algorithm that adjust the suppliers’ supplying quantity according to their unit costs step by step to solve the model. Findings: Relying on the contrasting analysis between the UCA and the Lingo solvers for optimizing many numerical experiments, results show that the INLP model and the UCA algorithm can obtain its near optimal solution of the two-stage supply chain’s planning and scheduling problem within very short CPU time. Research limitations/implications: The proposed UCA heuristic can easily help managers to optimizing the two-stage supply chain scheduling problems which doesn’t include the delivery time and batch of orders. For two-stage supply chains are the most common form of actual commercial relationships, so to make some modification and study on the UCA heuristic should be able to optimize the integrated planning and scheduling problems of a supply chain with more reality constraints. Originality/value: This research proposes an innovative UCA heuristic for optimizing the integrated planning and scheduling problem of two-stage supply chains with the constraints of suppliers’ production capacity and the orders’ delivering time, and has a great practical significance to the dynamic relationship establishment of multi-supplier-multi-customer in current market.Peer Reviewe

A unit cost adjusting heuristic algorithm for the integrated planning and scheduling of a two-stage supply chain

Purpose: The stable relationship of one-supplier-one-customer is replaced by a dynamic relationship of multi-supplier-multi-customer in current market gradually, and efficient scheduling techniques are important tools of the dynamic supply chain relationship establishing process. This paper studies the optimization of the integrated planning and scheduling problem of a two-stage supply chain with multiple manufacturers and multiple retailers to obtain a minimum supply chain operating cost, whose manufacturers have different production capacities, holding and producing cost rates, transportation costs to retailers. Design/methodology/approach: As a complex task allocation and scheduling problem, this paper sets up an INLP model for it and designs a Unit Cost Adjusting (UCA) heuristic algorithm that adjust the suppliers’ supplying quantity according to their unit costs step by step to solve the model. Findings: Relying on the contrasting analysis between the UCA and the Lingo solvers for optimizing many numerical experiments, results show that the INLP model and the UCA algorithm can obtain its near optimal solution of the two-stage supply chain’s planning and scheduling problem within very short CPU time. Research limitations/implications: The proposed UCA heuristic can easily help managers to optimizing the two-stage supply chain scheduling problems which doesn’t include the delivery time and batch of orders. For two-stage supply chains are the most common form of actual commercial relationships, so to make some modification and study on the UCA heuristic should be able to optimize the integrated planning and scheduling problems of a supply chain with more reality constraints. Originality/value: This research proposes an innovative UCA heuristic for optimizing the integrated planning and scheduling problem of two-stage supply chains with the constraints of suppliers’ production capacity and the orders’ delivering time, and has a great practical significance to the dynamic relationship establishment of multi-supplier-multi-customer in current market.Peer Reviewe

Social, environmental and economic impacts of alternative energy and fuel supply chains

Energy supply nowadays, being a vital element of a country’s development, has to independently meet diverse, sustainability criteria, be it economic, environmental and social. The main goal of the present research work is to present a methodological framework for the evaluation of alternative energy and fuel Supply Chains (SCs), consisting of a broad topology (representation) suggested, encompassing all the well-known energy and fuel SCs, under a unified scheme, a set of performance measures and indices as well as mathematical model development, formulated as Multi-objective Linear Programming with the extension of incorporating binary decisions as well (Multi-objective Mixed Integer-Linear programming). Basic characteristics of the current modelling approach include the adaptability of the model to be applied at different levels of energy SCs decisions, under different time frames and for multiple stakeholders. Model evaluation is carried for a set of Greek islands, located in the Aegean Archipelagos, examining both the existing energy supply options as well future, more sustainable Energy Supply Chains (ESCs) configurations. Results of the specific research work reveal the social and environmental costs which are underestimated under the traditional energy supply options' evaluation, as well as the benefits that may be produced from renewable energy based applications in terms of social security and employment

Time to be responsive in the process industry: a literature-based analysis of trends of change, solutions and challenges

The current uncertain and volatile business context is challenging firms worldwide, leading to the need to be responsive at a competitive cost. This trend is so substantial that it even affects industries traditionally competing in rather stable contexts, such as the process industry. Although the process industry includes multiple sectors with different technologies and processes, these share several aspects that make the industry as a whole distinctive to the discrete manufacturing industry. Based on a literature review, this study identifies and describes trends leading the process industry to the need for responsiveness, corresponding solutions to accommodate the need, and related challenges hindering the industrialization and diffusion of solutions in this industry. This study shows that trends, such as the uncertainty and volatility of market requirements, are challenging the process industry to develop reconfigurability solutions across multiple production levels. The development of reconfigurability solutions is hindered by modularity, integrability, co-ordination and collaboration challenges