A Teaching-Learning-Based Optimization Algorithm for the Weighted Set-Covering Problem

The need to make good use of resources has allowed metaheuristics to become a tool to achieve this goal. There are a number of complex problems to solve, among which is the Set-Covering Problem, which is a representation of a type of combinatorial optimization problem, which has been applied to several real industrial problems. We use a binary version of the optimization algorithm based on teaching and learning to solve the problem, incorporating various binarization schemes, in order to solve the binary problem. In this paper, several binarization techniques are implemented in the teaching/learning based optimization algorithm, which presents only the minimum parameters to be configured such as the population and number of iterations to be evaluated. The performance of metaheuristic was evaluated through 65 benchmark instances. The results obtained are promising compared to those found in the literature

Building bridges between industry and academia: what is the profile of an industrial doctorate student?

info:eu-repo/semantics/acceptedVersio

Operations Management

Global competition has caused fundamental changes in the competitive environment of the manufacturing and service industries. Firms should develop strategic objectives that, upon achievement, result in a competitive advantage in the market place. The forces of globalization on one hand and rapidly growing marketing opportunities overseas, especially in emerging economies on the other, have led to the expansion of operations on a global scale. The book aims to cover the main topics characterizing operations management including both strategic issues and practical applications. A global environmental business including both manufacturing and services is analyzed. The book contains original research and application chapters from different perspectives. It is enriched through the analyses of case studies

Multi-objective Optimization of Industrial Ammonia Synthesis

The thesis describes modelling and optimization work of an industrial ammonia synthesis. Author developed first-principle mathematical model of the commercial converter based on gas-solid reaction and heat transfer within the system. The model is validated with industrial data and showed satisfactory accuracy. Further, optimization study is performed in multi-objective manner to intensify ammonia production and decrease heat duty of the process. Result have revealed a potential to improve current operating condition int terms of both objectives

Application of Optimization in Production, Logistics, Inventory, Supply Chain Management and Block Chain

The evolution of industrial development since the 18th century is now experiencing the fourth industrial revolution. The effect of the development has propagated into almost every sector of the industry. From inventory to the circular economy, the effectiveness of technology has been fruitful for industry. The recent trends in research, with new ideas and methodologies, are included in this book. Several new ideas and business strategies are developed in the area of the supply chain management, logistics, optimization, and forecasting for the improvement of the economy of the society and the environment. The proposed technologies and ideas are either novel or help modify several other new ideas. Different real life problems with different dimensions are discussed in the book so that readers may connect with the recent issues in society and industry. The collection of the articles provides a glimpse into the new research trends in technology, business, and the environment

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

Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors

Holistic, data-driven, service and supply chain optimisation: linked optimisation.

The intensity of competition and technological advancements in the business environment has made companies collaborate and cooperate together as a means of survival. This creates a chain of companies and business components with unified business objectives. However, managing the decision-making process (like scheduling, ordering, delivering and allocating) at the various business components and maintaining a holistic objective is a huge business challenge, as these operations are complex and dynamic. This is because the overall chain of business processes is widely distributed across all the supply chain participants; therefore, no individual collaborator has a complete overview of the processes. Increasingly, such decisions are automated and are strongly supported by optimisation algorithms - manufacturing optimisation, B2B ordering, financial trading, transportation scheduling and allocation. However, most of these algorithms do not incorporate the complexity associated with interacting decision-making systems like supply chains. It is well-known that decisions made at one point in supply chains can have significant consequences that ripple through linked production and transportation systems. Recently, global shocks to supply chains (COVID-19, climate change, blockage of the Suez Canal) have demonstrated the importance of these interdependencies, and the need to create supply chains that are more resilient and have significantly reduced impact on the environment. Such interacting decision-making systems need to be considered through an optimisation process. However, the interactions between such decision-making systems are not modelled. We therefore believe that modelling such interactions is an opportunity to provide computational extensions to current optimisation paradigms. This research study aims to develop a general framework for formulating and solving holistic, data-driven optimisation problems in service and supply chains. This research achieved this aim and contributes to scholarship by firstly considering the complexities of supply chain problems from a linked problem perspective. This leads to developing a formalism for characterising linked optimisation problems as a model for supply chains. Secondly, the research adopts a method for creating a linked optimisation problem benchmark by linking existing classical benchmark sets. This involves using a mix of classical optimisation problems, typically relating to supply chain decision problems, to describe different modes of linkages in linked optimisation problems. Thirdly, several techniques for linking supply chain fragmented data have been proposed in the literature to identify data relationships. Therefore, this thesis explores some of these techniques and combines them in specific ways to improve the data discovery process. Lastly, many state-of-the-art algorithms have been explored in the literature and these algorithms have been used to tackle problems relating to supply chain problems. This research therefore investigates the resilient state-of-the-art optimisation algorithms presented in the literature, and then designs suitable algorithmic approaches inspired by the existing algorithms and the nature of problem linkages to address different problem linkages in supply chains. Considering research findings and future perspectives, the study demonstrates the suitability of algorithms to different linked structures involving two sub-problems, which suggests further investigations on issues like the suitability of algorithms on more complex structures, benchmark methodologies, holistic goals and evaluation, processmining, game theory and dependency analysis

Operator Training Simulator Using Plantwide Control for Biodiesel Production from Waste Cooking Oil

Kajian ini bertujuan untuk membangunkan simulator latihan operator (OTS) untuk mangkin homogen bagi proses dua langkah biodiesel yang kompleks. Latihan sambil bekerja selalunya memerlukan kos yang tinggi, berisiko dan tidak lengkap kerana beberapa situasi kecemasan mungkin tidak berlaku semasa sesi latihan. Biodiesel dilihat sebagai sumber bahan api alternative, Disebabkan ketersediaan yang terhad sumber tenaga yang tidak boleh diperbaharui dan juga kebimbangan terhadap alam sekitar. Walau bagaimanapun, kos pengeluaran yang tinggi bagi biodiesel menghadkan pengeluaran dan penggunaannya. Salah satu pilihan yang terbaik adalah dengan menggunakan sisa minyak masak (WCO) sebagai sumber bahan mentah bagi pengeluaran biodiesel yang kos efektif dan juga penggunaan WCO yang berkesan. Dalam kajian ini, sisa minyak sawit masak dianggap dengan 6% asid lemak bebas (FFA) sebagai bekalan simpanan. Dua proses pengeluaran biodiesel (kedua-duanya melibatkan pengesteran asid dan transesterifikasi alkali) telah dibandingkan untuk analisis ekonomi dan alam sekitar. Pertama, proses ini dalam simulator Aspen Plus. Selepas itu, kedua-dua proses dioptimumkan dengan mengambil kira keuntungan, tenaga haba dan bahan buangan organik sebagai objektif, dan menggunakan program berasaskan Excel pengoptimuman multi-objektif (EMOO) untuk pengisihan algoritma genetic elitis tidak dikuasai (NSGA-II). Proses 1 mempunyai tiga reaktor transesterifikasi yang menghasilkan sisa organik jauh lebih rendah (32%), memerlukan duti haba yang lebih rendah (39%) dan sedikit keuntungan (1.6%) berbanding Proses 2 yang hanya mempunyai satu reaktor transesterifikasi dan juga urutan pemisahan yang berbeza. Sistem kawalan loji lebar (PWC) yang berkesan adalah penting untuk operasi loji biodiesel yang selamat, lancar dan ekonomi. Oleh itu, sistem PWC yang sesuai telah dibangunkan untuk proses biodiesel yang menggunakan simulasi rangka kerja bersepadu dan heuristik (IFSH). Merit utama metodologi IFSH adalah keberkesanan penggunaan proses simulator yang baik dan heuristik dalam membangunkan sistem PWC dan kesederhanaan applikasinya. Akhir sekali, pelaksanaan sistem kawalan yang dibangunkan dinilai dari segi masa penetapan, sisihan daripada sasaran pengeluaran (DPT), dan jumlah variasi keseluruhan (TV) dalam pembolehubah yang dimanipulasi. Penilaian-penilaian prestasi dan keputusan simulasi dinamik menunjukkan bahawa sistem PWC yang dihasilkan adalah stabil, berkesan, dan teguh terhadap beberapa gangguan. Akhir sekali, OTS telah dibangunkan untuk penghasilan biodiesel daripada WCO. Oleh itu, latihan menggunakan OTS adalah penting. OTS telah dibangunkan untuk pengeluaran biodiesel dan telah diapplikasikan dengan beberapa keadaan proses yang tidak normal. Keadaan proses ini boleh dimuatkan dan digunakan pada bila-bila masa untuk melatih operator baru dan sedia ada. Kajian ini adalah yang pertama dibangunkan menggunakan struktur lengkap PWC dan OTS untuk mangkin yang homogeneous bagi dua langkah pengeluaran biodiesel daripada WCO. ________________________________________________________________________________________________________________________ This study aims at developing an operator training simulator (OTS) for the complex homogeneously catalyzed two-step biodiesel process. On-job training is often costly, risky and incomplete as some emergency situations may not arise during the training session. Therefore, training using an OTS is crucial. Pertaining to the limited availability of non-renewable energy sources and the environmental concerns, biodiesel is considered as a potential alternative fuel. However, the high production cost of biodiesel limits its manufacture and utilization. One attractive option is to use waste cooking oil (WCO) as the feedstock that enables cost effective biodiesel production and also facilitates effective WCO utilization. This study considers waste cooking palm oil with 6% free fatty acids (FFA) as feedstock. Two biodiesel production processes (both involving acid esterification and alkali transesterification) are compared for economic and environmental objectives. Firstly, these processes are simulated realistically in Aspen Plus simulator. Subsequently, both the processes are optimized considering profit, heat duty and organic waste as objectives, and using an Excel-based multi-objective optimization (EMOO) program for the elitist non-dominated sorting genetic algorithm (NSGA-II). Process 1 having three transesterification reactors produces significantly lower organic waste (by 32%), requires lower heat duty (by 39%) and slightly more profitable (by 1.6%) compared to Process 2 having a single transesterification reactor and also a different separation sequence. An effective plantwide control (PWC) system is crucial for the safe, smooth, and economical operation of a biodiesel plant. Hence, a suitable PWC system is developed for the biodiesel process using the integrated framework of simulation and heuristics (IFSH). The main merits of the IFSH methodology are effective use of rigorous process simulators and heuristics in developing a PWC system and simplicity of application. Later, the performance of the developed control system is assessed in terms of settling time, deviation from the production target (DPT), and overall total variation (TV) in manipulated variables. These performance assessments and the results of dynamic simulations showed that the developed PWC system is stable, effective, and robust in the presence of several disturbances. Finally, an OTS has been developed for the biodiesel production from WCO. The developed OTS for biodiesel production process has been investigated for several abnormal process conditions. These process scenarios can be loaded and utilized at any point in time to train the new and existing operators. This is the first study to develop a complete PWC structure and OTS for a homogeneously catalyzed two-step biodiesel production from WCO

A contribution to support decision making in energy/water sypply chain optimisation

The seeking of process sustainability forces enterprises to change their operations. Additionally, the industrial globalization implies a very dynamic market that, among other issues, promotes the enterprises competition. Therefore, the efficient control and use of their Key Performance Indicators, including profitability, cost reduction, demand satisfaction and environmental impact associated to the development of new products, is a significant challenge. All the above indicators can be efficiently controlled through the Supply Chain Management. Thus, companies work towards the optimization of their individual operations under competitive environments taking advantage of the flexibility provided by the virtually inexistent world market restrictions. This is achieved by the coordination of the resource flows, across all the entities and echelons belonging to the system network. Nevertheless, such coordination is significantly complicated if considering the presence of uncertainty and even more if seeking for a win-win outcome. The purpose of this thesis is extending the current decision making strategies to expedite these tasks in industrial processes. Such a contribution is based on the development of efficient mathematical models that allows coordinating large amount of information synchronizing the production and distribution tasks in terms of economic, environmental and social criteria. This thesis starts presents an overview of the requirements of sustainable production processes, describing and analyzing the current methods and tools used and identifying the most relevant open issues. All the above is always within the framework of Process System Engineering literature. The second part of this thesis is focused in stressing the current Multi-Objective solution strategies. During this part, first explores how the profitability of the Supply Chain can be enhanced by considering simultaneously multiple objectives under demand uncertainties. Particularly, solution frameworks have been proposed in which different multi-criteria decision making strategies have been combined with stochastic approaches. Furthermore, additional performance indicators (including financial and operational ones) have been included in the same solution framework to evaluate its capabilities. This framework was also applied to decentralized supply chains problems in order to explore its capabilities to produce solution that improves the performances of each one of the SC entities simultaneously. Consequently, a new generalized mathematical formulation which integrates many performance indicators in the production process within a supply chain is efficiently solved. Afterwards, the third part of the thesis extends the proposed solution framework to address the uncertainty management. Particularly, the consideration of different types and sources of uncertainty (e.g. external and internal ones) where considered, through the implementation of preventive approaches. This part also explores the use of solution strategies that efficiently selects the number of scenarios that represent the uncertainty conditions. Finally, the importance and effect of each uncertainty source over the process performance is detailed analyzed through the use of surrogate models that promote the sensitivity analysis of those uncertainties. The third part of this thesis is focused on the integration of the above multi-objective and uncertainty approaches for the optimization of a sustainable Supply Chain. Besides the integration of different solution approaches, this part also considers the integration of hierarchical decision levels, by the exploitation of mathematical models that assess the consequences of considering simultaneously design and planning decisions under centralized and decentralized Supply Chains. Finally, the last part of this thesis provides the final conclusions and further work to be developed.La globalización industrial genera un ambiente dinámico en los mercados que, entre otras cosas, promueve la competencia entre corporaciones. Por lo tanto, el uso eficiente de las los indicadores de rendimiento, incluyendo rentabilidad, satisfacción de la demanda y en general el impacto ambiental, representa un area de oportunidad importante. El control de estos indicadores tiene un efecto positivo si se combinan con la gestión de cadena de suministro. Por lo tanto, las compañías buscan definir sus operaciones para permanecer activas dentro de un ambiente competitivo, tomando en cuenta las restricciones en el mercado mundial. Lo anterior puede ser logrado mediante la coordinación de los flujos de recursos a través de todas las entidades y escalones pertenecientes a la red del sistema. Sin embargo, dicha coordinación se complica significativamente si se quiere considerar la presencia de incertidumbre, y aún más, si se busca exclusivamente un ganar-ganar. El propósito de esta tesis es extender el alcance de las estrategias de toma de decisiones con el fin de facilitar estas tareas dentro de procesos industriales. Estas contribuciones se basan en el desarrollo de modelos matemáticos eficientes que permitan coordinar grandes cantidades de información sincronizando las tareas de producción y distribución en términos económicos, ambientales y sociales. Esta tesis inicia presentando una visión global de los requerimientos de un proceso de producción sostenible, describiendo y analizando los métodos y herramientas actuales así como identificando las áreas de oportunidad más relevantes dentro del marco de ingeniería de procesos La segunda parte se enfoca en enfatizar las capacidades de las estrategias de solución multi-objetivo, durante la cual, se explora el mejoramiento de la rentabilidad de la cadena de suministro considerando múltiples objetivos bajo incertidumbres en la demanda. Particularmente, diferentes marcos de solución han sido propuestos en los que varias estrategias de toma de decisión multi-criterio han sido combinadas con aproximaciones estocásticas. Por otra parte, indicadores de rendimiento (incluyendo financiero y operacional) han sido incluidos en el mismo marco de solución para evaluar sus capacidades. Este marco fue aplicado también a problemas de cadenas de suministro descentralizados con el fin de explorar sus capacidades de producir soluciones que mejoran simultáneamente el rendimiento para cada uno de las entidades dentro de la cadena de suministro. Consecuentemente, una nueva formulación que integra varios indicadores de rendimiento en los procesos de producción fue propuesta y validada. La tercera parte de la tesis extiende el marco de solución propuesto para abordar el manejo de incertidumbres. Particularmente, la consideración de diferentes tipos y fuentes de incertidumbre (p.ej. externos e internos) fueron considerados, mediante la implementación de aproximaciones preventivas. Esta parte también explora el uso de estrategias de solución que elige eficientemente el número de escenarios necesario que representan las condiciones inciertas. Finalmente, la importancia y efecto de cada una de las fuentes de incertidumbre sobre el rendimiento del proceso es analizado en detalle mediante el uso de meta modelos que promueven el análisis de sensibilidad de dichas incertidumbres. La tercera parte de esta tesis se enfoca en la integración de las metodologías de multi-objetivo e incertidumbre anteriormente expuestas para la optimización de cadenas de suministro sostenibles. Además de la integración de diferentes métodos. Esta parte también considera la integración de diferentes niveles jerárquicos de decisión, mediante el aprovechamiento de modelos matemáticos que evalúan lasconsecuencias de considerar simultáneamente las decisiones de diseño y planeación de una cadena de suministro centralizada y descentralizada. La parte final de la tesis detalla las conclusiones y el trabajo a futuro necesario sobre esta línea de investigaciónPostprint (published version