Multi-objective optimisation of reliable product-plant network configuration.

Ensuring manufacturing reliability is key to satisfying product orders when production plants are subject to disruptions. Reliability of a supply network is closely related to the redundancy of products as production in disrupted plants can be replaced by alternative plants. However the benefits of incorporating redundancy must be balanced against the costs of doing so. Models in literature are highly case specific and do not consider complex network structures and redundant distributions of products over suppliers, that are evident in empirical literature. In this paper we first develop a simple generic measure for evaluating the reliability of a network of plants in a given product-plant configuration. Second, we frame the problem as a multi-objective evolutionary optimisation model to show that such a measure can be used to optimise the cost-reliability trade off. The model has been applied to a producer’s automotive light and lamp production network using three popular genetic algorithms designed for multi-objective problems, namely, NSGA2, SPEA2 and PAES. Using the model in conjunction with genetic algorithms we were able to find trade off solutions successfully. NSGA2 has achieved the best results in terms of Pareto front spread. Algorithms differed considerably in their performance, meaning that the choice of algorithm has significant impact in the resulting search space exploration

Eco-efficient supply chain networks: Development of a design framework and application to a real case study

© 2015 Taylor & Francis. This paper presents a supply chain network design framework that is based on multi-objective mathematical programming and that can identify 'eco-efficient' configuration alternatives that are both efficient and ecologically sound. This work is original in that it encompasses the environmental impact of both transportation and warehousing activities. We apply the proposed framework to a real-life case study (i.e. Lindt & Sprüngli) for the distribution of chocolate products. The results show that cost-driven network optimisation may lead to beneficial effects for the environment and that a minor increase in distribution costs can be offset by a major improvement in environmental performance. This paper contributes to the body of knowledge on eco-efficient supply chain design and closes the missing link between model-based methods and empirical applied research. It also generates insights into the growing debate on the trade-off between the economic and environmental performance of supply chains, supporting organisations in the eco-efficient configuration of their supply chains

An overview of the VRS virtual platform

This paper provides an overview of the development of the virtual platform within the European Commission funded VRShips-ROPAX (VRS) project. This project is a major collaboration of approximately 40 industrial, regulatory, consultancy and academic partners with the objective of producing two novel platforms. A physical platform will be designed and produced representing a scale model of a novel ROPAX vessel with the following criteria: 2000 passengers; 400 cabins; 2000 nautical mile range, and a service speed of 38 knots. The aim of the virtual platform is to demonstrate that vessels may be designed to meet these criteria, which was not previously possible using individual tools and conventional design approaches. To achieve this objective requires the integration of design and simulation tools representing concept, embodiment, detail, production, and operation life-phases into the virtual platform, to enable distributed design activity to be undertaken. The main objectives for the development of the virtual platform are described, followed by the discussion of the techniques chosen to address the objectives, and finally a description of a use-case for the platform. Whilst the focus of the VRS virtual platform was to facilitate the design of ROPAX vessels, the components within the platform are entirely generic and may be applied to the distributed design of any type of vessel, or other complex made-to-order products

Global supply chains of high value low volume products

Imperial Users onl

An integrated model for designing and optimising an international logistics network

Supply-chain configuration has recently gained increasing attention both from the practitioner’s perspective and as a research area. This paper proposes an integrated model for designing and optimising international logistics networks. It consists of a mixed integer linear programming model and a data-mapping section (i.e. methodological guidelines for gathering and processing the data necessary to set up the model). It has been specifically developed for solving the configuration problem for supply chains characterised by a complexity level typical of real-life global logistics networks. Although this topic is well understood and well elaborated at a technical level in the extant literature, it still presents obstacles in practice especially in terms of dealing with real-life complexity, service-level constraints and data mapping. Thus, we developed our integrated approach with the aim to fill these gaps. We designed our model for dealing with multiple-layer, single location-layer, multiple-commodity and time-constrained logistics networks, to be implemented in a single period time horizon and in a deterministic environment. The proposed approach represents an innovative contribution to the existing body of scientific knowledge and facilitates the data gathering and processing activities, which are largely recognised as complex and time-consuming processes for the management of logistics activities

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

A hierarchical Mamdani-type fuzzy modelling approach with new training data selection and multi-objective optimisation mechanisms: A special application for the prediction of mechanical properties of alloy steels

In this paper, a systematic data-driven fuzzy modelling methodology is proposed, which allows to construct Mamdani fuzzy models considering both accuracy (precision) and transparency (interpretability) of fuzzy systems. The new methodology employs a fast hierarchical clustering algorithm to generate an initial fuzzy model efficiently; a training data selection mechanism is developed to identify appropriate and efficient data as learning samples; a high-performance Particle Swarm Optimisation (PSO) based multi-objective optimisation mechanism is developed to further improve the fuzzy model in terms of both the structure and the parameters; and a new tolerance analysis method is proposed to derive the confidence bands relating to the final elicited models. This proposed modelling approach is evaluated using two benchmark problems and is shown to outperform other modelling approaches. Furthermore, the proposed approach is successfully applied to complex high-dimensional modelling problems for manufacturing of alloy steels, using ‘real’ industrial data. These problems concern the prediction of the mechanical properties of alloy steels by correlating them with the heat treatment process conditions as well as the weight percentages of the chemical compositions

Energy efficient control and optimisation techniques for distillation processes

PhD ThesisDistillation unit is one of the most energy intensive processes and is among the major CO2 emitter in the chemical and petrochemical industries. In the quest to reduce the energy consumption and hence the environmental implications of unutilised energy, there is a strong motivation for energy saving procedures for conventional columns. Several attempts have been made to redesign and heat integrate distillation column with the aim of reducing the energy consumption of the column. Most of these attempts often involve additional capital costs in implementing. Also a number of works on applying the second law of thermodynamics to distillation column are focused on quantifying the efficiency of the column. This research aims at developing techniques of increasing the energy efficiency of the distillation column with the application of second law using the tools of advanced control and optimisation. Rigorous model from the fundamental equations and data driven models using Artificial neural network (ANN) and numerical methods (PLS, PCR, MLR) of a number of distillation columns are developed. The data for the data driven models are generated from HYSYS simulation. This research presents techniques for selecting energy efficient control structure for distillation processes. Relative gain array (RGA) and relative exergy array (REA ) were used in the selection of appropriate distillation control structures. The viability of the selected control scheme in the steady state is further validated by the dynamic simulation in responses to various process disturbances and operating condition changes. The technique is demonstrated on two binary distillation systems. In addition, presented in this thesis is optimisation procedures based on second law analysis aimed at minimising the inefficiencies of the columns without compromising the qualities of the products. ANN and Bootstrap aggregated neural network (BANN) models of exergy efficiency were developed. BANN enhances model prediction accuracy and also provides model prediction confidence bounds. The objective of the optimisation is to maximise the exergy efficiency of the column. To improve the reliability of the optimisation strategy, a modified objective function incorporating model prediction confidence bounds was presented. Multiobjective optimisation was also explored. Product quality constraints introduce a measure of penalization on the optimisation result to give as close as possible to what obtains in reality. The optimisation strategies developed were applied to binary systems, multicomponents system, and crude distillation system. The crude distillation system was fully explored with emphasis on the preflash unit, atmospheric distillation system (ADU) and vacuum distillation system (VDU). This study shows that BANN models result in greater model accuracy and more robust models. The proposed ii techniques also significantly improve the second law efficiency of the system with an additional economic advantage. The method can aid in the operation and design of energy efficient column.Commonwealth scholarship commissio

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Optimisation-based methodology for the design and operation of sustainable wastewater treatment facilities

The treatment of municipal and industrial wastewaters in conventional wastewater treatment plants (WWTPs) requires a significant amount of energy in order to meet ever more stringent discharge regulations. However, the wastewater treatment industry is undergoing a paradigm shift from a focus on waste-stream treatment and contaminant removal to a proactive interest in energy and resource recovery facilities, driven by both economic and environmental incentives. The main objective of this thesis is the development of a decision-making tool in order to identify improvement opportunities in existing WWTPs and to develop new concepts of sustainable wastewater treatment/recovery facilities. The first part of the thesis presents the application of a model-based methodology based on systematic optimisation for improved understanding of the tight interplay between effluent quality, energy use, and fugitive emissions in existing WWTPs. Plant-wide models are developed and calibrated in an objective to predict the performance of two conventional activated sludge plants owned and operated by Sydney Water, Australia. In the first plant, a simulation-based approach is applied to quantify the effect of key operating variables on the effluent quality, energy use, and fugitive emissions. The results show potential for reduced consumption of energy (up to 10-20%) through operational changes only, without compromising effluent quality. It is also found that nitrate (and hence total nitrogen) discharge could be signficantly reduced from its current level with a small increase in energy consumption. These results are also compared to an upgraded plant with reverse osmosis in terms of energy consumption and greenhouse gas emissions. In the second plant, a systematic model-based optimisation approach is applied to investigate the effect of key discharge constraints on the net power consumption. The results show a potential for reduction of energy (20-25%), without compromising the current effluent quality. The nitrate discharge could be reduced from its current level to less than 15 mg/L with no increase in net power consumption and could be further reduced to <5 mg/L subject to a 18% increase in net power consumption upon the addition of an external carbon source. This improved understanding of the relationship between nutrient removal and energy use for these two plants will feed into discussions with environmental regulators regarding nutrient discharge licensing.The second part of the thesis deals with the application of a systematic, model-based methodology for the development of wastewater treatment/resource recovery systems that are both economically and environmentally sustainable. With the array of available treatment and recovery options growing steadily, a superstructure modeling approach based on rigorous mathematical optimisation provides a natural approach for tackling these problems. The development of reliable, yet simple, performance and cost models is a key issue with this approach in order to allow for a reliable solution based on global optimisation. it is argued that commercial wastewater simulators can be used to derive such models. The superstructure modeling framework is also able to account for wastewater and sludge treatment in an integrated system and to incorporate LCA with multi-objective optimisation to identify the inherent trade-off between multiple economic and environmental objectives. This approach is illustrated with two case studies of resource recovery from industrial and municipal wastewaters. The results establish that the proposed methodology is computationally tractable, thereby supporting its application as a decision support system for selection of promising wastewater treatment/resource recovery systems whose development is worth pursuing. Our analysis also suggests that accounting for LCA considerations early on in the design process may lead to dramatic changes in the configuration of future wastewater treatment/recovery facilities.Open Acces