Overview of Multi-Objective Optimization Approaches in Construction Project Management

The difficulties that are met in construction projects include budget issues, contractual time constraints, complying with sustainability rating systems, meeting local building codes, and achieving the desired quality level, to name but a few. Construction researchers have proposed and construction practitioners have used optimization strategies to meet various objectives over the years. They started out by optimizing one objective at a time (e.g., minimizing construction cost) while disregarding others. Because the objectives of construction projects often conflict with each other, single-objective optimization does not offer practical solutions as optimizing one objective would often adversely affect the other objectives that are not being optimized. They then experimented with multi-objective optimization. The many multi-objective optimization approaches that they used have their own advantages and drawbacks when used in some scenarios with different sets of objectives. In this chapter, a review is presented of 16 multi-objective optimization approaches used in 55 research studies performed in the construction industry and that were published in the period 2012–2016. The discussion highlights the strengths and weaknesses of these approaches when used in different scenarios

Multiobjective genetic algorithm strategies for electricity production from generation IV nuclear technology

Development of a technico-economic optimization strategy of cogeneration systems of electricity/hydrogen, consists in finding an optimal efficiency of the generating cycle and heat delivery system, maximizing the energy production and minimizing the production costs. The first part of the paper is related to the development of a multiobjective optimization library (MULTIGEN) to tackle all types of problems arising from cogeneration. After a literature review for identifying the most efficient methods, the MULTIGEN library is described, and the innovative points are listed. A new stopping criterion, based on the stagnation of the Pareto front, may lead to significant decrease of computational times, particularly in the case of problems involving only integer variables. Two practical examples are presented in the last section. The former is devoted to a bicriteria optimization of both exergy destruction and total cost of the plant, for a generating cycle coupled with a Very High Temperature Reactor (VHTR). The second example consists in designing the heat exchanger of the generating turbomachine. Three criteria are optimized: the exchange surface, the exergy destruction and the number of exchange modules

Development of a multi-objective optimization algorithm based on lichtenberg figures

This doctoral dissertation presents the most important concepts of multi-objective optimization and a systematic review of the most cited articles in the last years of this subject in mechanical engineering. The State of the Art shows a trend towards the use of metaheuristics and the use of a posteriori decision-making techniques to solve engineering problems. This fact increases the demand for algorithms, which compete to deliver the most accurate answers at the lowest possible computational cost. In this context, a new hybrid multi-objective metaheuristic inspired by lightning and Linchtenberg Figures is proposed. The Multi-objective Lichtenberg Algorithm (MOLA) is tested using complex test functions and explicit contrainted engineering problems and compared with other metaheuristics. MOLA outperformed the most used algorithms in the literature: NSGA-II, MOPSO, MOEA/D, MOGWO, and MOGOA. After initial validation, it was applied to two complex and impossible to be analytically evaluated problems. The first was a design case: the multi-objective optimization of CFRP isogrid tubes using the finite element method. The optimizations were made considering two methodologies: i) using a metamodel, and ii) the finite element updating. The last proved to be the best methodology, finding solutions that reduced at least 45.69% of the mass, 18.4% of the instability coefficient, 61.76% of the Tsai-Wu failure index and increased by at least 52.57% the natural frequency. In the second application, MOLA was internally modified and associated with feature selection techniques to become the Multi-objective Sensor Selection and Placement Optimization based on the Lichtenberg Algorithm (MOSSPOLA), an unprecedented Sensor Placement Optimization (SPO) algorithm that maximizes the acquired modal response and minimizes the number of sensors for any structure. Although this is a structural health monitoring principle, it has never been done before. MOSSPOLA was applied to a real helicopter’s main rotor blade using the 7 best-known metrics in SPO. Pareto fronts and sensor configurations were unprecedentedly generated and compared. Better sensor distributions were associated with higher hypervolume and the algorithm found a sensor configuration for each sensor number and metric, including one with 100% accuracy in identifying delamination considering triaxial modal displacements, minimum number of sensors, and noise for all blade sections.Esta tese de doutorado traz os conceitos mais importantes de otimização multi-objetivo e uma revisão sistemática dos artigos mais citados nos últimos anos deste tema em engenharia mecânica. O estado da arte mostra uma tendência no uso de meta-heurísticas e de técnicas de tomada de decisão a posteriori para resolver problemas de engenharia. Este fato aumenta a demanda sobre os algoritmos, que competem para entregar respostas mais precisas com o menor custo computacional possível. Nesse contexto, é proposta uma nova meta-heurística híbrida multi-objetivo inspirada em raios e Figuras de Lichtenberg. O Algoritmo de Lichtenberg Multi-objetivo (MOLA) é testado e comparado com outras metaheurísticas usando funções de teste complexas e problemas restritos e explícitos de engenharia. Ele superou os algoritmos mais utilizados na literatura: NSGA-II, MOPSO, MOEA/D, MOGWO e MOGOA. Após validação, foi aplicado em dois problemas complexos e impossíveis de serem analiticamente otimizados. O primeiro foi um caso de projeto: otimização multi-objetivo de tubos isogrid CFRP usando o método dos elementos finitos. As otimizações foram feitas considerando duas metodologias: i) usando um meta-modelo, e ii) atualização por elementos finitos. A última provou ser a melhor metodologia, encontrando soluções que reduziram pelo menos 45,69% da massa, 18,4% do coeficiente de instabilidade, 61,76% do TW e aumentaram em pelo menos 52,57% a frequência natural. Na segunda aplicação, MOLA foi modificado internamente e associado a técnicas de feature selection para se tornar o Seleção e Alocação ótima de Sensores Multi-objetivo baseado no Algoritmo de Lichtenberg (MOSSPOLA), um algoritmo inédito de Otimização de Posicionamento de Sensores (SPO) que maximiza a resposta modal adquirida e minimiza o número de sensores para qualquer estrutura. Embora isto seja um princípio de Monitoramento da Saúde Estrutural, nunca foi feito antes. O MOSSPOLA foi aplicado na pá do rotor principal de um helicóptero real usando as 7 métricas mais conhecidas em SPO. Frentes de Pareto e configurações de sensores foram ineditamente geradas e comparadas. Melhores distribuições de sensores foram associadas a um alto hipervolume e o algoritmo encontrou uma configuração de sensor para cada número de sensores e métrica, incluindo uma com 100% de precisão na identificação de delaminação considerando deslocamentos modais triaxiais, número mínimo de sensores e ruído para todas as seções da lâmina

Heurísticas bioinspiradas para el problema de Floorplanning 3D térmico de dispositivos MPSoCs

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leída el 20-06-2013Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu

A Hybrid Lehmer Code Genetic Algorithm and Its Application on Traveling Salesman Problems

Traveling Salesman Problems (TSP) is a widely studied combinatorial optimization problem. The goal of the TSP is to find a tour which begins in a specific city, visits each of the remaining cities once and returns to the initial cities such that the objective functions are optimized, typically involving minimizing functions like total distance traveled, total time used or total cost. Genetic algorithms were first proposed by John Holland (1975). It uses an iterative procedure to find the optimal solutions to optimization problems. This research proposed a hybrid Lehmer code Genetic Algorithm. To compensate for the weaknesses of traditional genetic algorithms in exploitation while not hampering its ability in exploration, this new genetic algorithm will combine genetic algorithm with 2-opt and non-sequential 3-opt heuristics. By using Lehmer code representation, the solutions created by crossover parent solutions are always feasible. The new algorithm was used to solve single objective and multi-objectives Traveling Salesman Problems. A non Pareto-based technique will be used to solve multi-objective TSPs. Specifically we will use the Target Vector Approach. In this research, we used the weighted Tchebycheff function with the ideal points as the reference points as the objective function to evaluate solutions, while the local search heuristics, the 2-opt and non-sequential 3-opt heuristics, were guided by a weighted sum function

The multi-objective optimum design of building thermal systems

The thermal design of buildings as a multi-criterion optimisation process since there is always a pay-off (balance) to be made between capital expenditure and the operating cost of the building. This thesis investigates an approach to solving 'whole building' optimisation problems. In particular simultaneous optimisation of the plant size for a fixed arrangement of air conditioning equipment, and the control schedule for its operation to condition the space within a discrete selection of building envelopes. The optimisation is achieved by examining a combination of the cost of operating the plant, the capital cost of the plant and building construction, and maximum percentage people dissatisfied during the occupation of the building. More that one criterion is examined at a time by using multi-criteria optimisation methods. Therefore rather than a single optimum, a payoff between the solutions is sort. The benefit of this is that it provides a more detailed information about the characteristics of the problem and more design solutions available to the end user. The optimisation is achieved using a modified genetic algorithm using Pareto ranking selection to provide the multi-criterion fitness selection. Specific methods for handling the high number of constraints within the problem are examined. A specific operator is designed and demonstrated to deal with the discontinuous effects of the three separate seasons, which are used for the plant selection and for the three separate control schedules. Conclusions are made with respect to the specific application of the multi-criterion optimisation to, building services systems, their control, and the viability of 'whole building design' optimisation

Lost in optimisation of water distribution systems? A literature review of system operation

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Optimisation of the operation of water distribution systems has been an active research field for almost half a century. It has focused mainly on optimal pump operation to minimise pumping costs and optimal water quality management to ensure that standards at customer nodes are met. This paper provides a systematic review by bringing together over two hundred publications from the past three decades, which are relevant to operational optimisation of water distribution systems, particularly optimal pump operation, valve control and system operation for water quality purposes of both urban drinking and regional multiquality water distribution systems. Uniquely, it also contains substantial and thorough information for over one hundred publications in a tabular form, which lists optimisation models inclusive of objectives, constraints, decision variables, solution methodologies used and other details. Research challenges in terms of simulation models, optimisation model formulation, selection of optimisation method and postprocessing needs have also been identified

Controller Design and Optimization for Rotor System Supported by Active Magnetic Bearings

Active Magnetic Bearings (AMBs) have been receiving increased attention in industry because of the advantages (contact-free, oil-free, etc.,) that they display in comparison with conventional bearings. They are used extensively in rotor system applications, especially in conditions where conventional bearing systems fail. Most AMBs are controlled by Proportional-Integral-Derivative (PID)-controllers. Controller design for AMB systems by means of hand tuning is time-consuming and requires expert knowledge. In order to avoid this situation and reduce the effort to tune the controller, multi-objective optimization with genetic algorithm is introduced to design and optimize the AMB controllers. In the optimization, criteria both in time and frequency domain are considered. A hierarchical fitness function evaluation procedure is used to accelerate the optimization process and to increase the probability of convergence. This evaluation procedure guides the optimizer to locate the small feasible region resulting mainly from the requirement for stability of control system. Another strategy to reduce the number of optimization parameters is developed, which is based on a sensitivity analysis of the controller parameters. This strategy reduces directly the complexity of the optimization problem and accelerates the optimization process. Controller designs for two AMB systems are considered in this thesis. Based on the introduced and presented hierarchical evaluation strategy, the controller design for the first AMB system is obtained without specific requirements related to initial solutions. The optimal controller design is applied to a test rig with a flexible rotor supported by AMBs. The results show that the introduced optimization procedure realizes the desired results of the controlled system’s behavior. The maximal speed of 15000 rpm is reached. The second AMB system is designed for a turbo-compressor. The introduced parameter reduction strategy is applied for the controller design of this AMB system. The controller design is optimized in the search space around an initial solution. Optimization results show the efficiency of the introduced strategy.Aufgrund vieler Vorteile (wie z. B. Kontaktfreiheit, Ölfreiheit) gegenüber konventionellen Lagern etablieren sich aktive Magnetlager zunehmend in der Industrie. Aktive Magnetlager werden zum großen Teil in Rotorsystemen verwendet, wo konventionelle Öllager für die Anwendung versagen. PID-Regler werden häufig für Magnetlager verwendet. Die Auslegung des Reglers wird durch manuelle Einstellung (trial and error) bestimmt und ist sehr zeitaufwendig. Zudem bedarf es spezieller Fachkenntnisse zur Einstellung. Um diese Situation zu vermeiden und den Aufwand für die Reglerauslegung zu reduzieren, wird die Mehrzieloptimierung mit Genetischen Algorithmen in der vorliegenden Arbeit zur Optimierung des Reglerentwurfs eingesetzt. In der Optimierung werden die Zielfunktionen sowohl im Zeit- wie auch im Frequenzbereich definiert. Um den Optimierungsprozess zu beschleunigen und die Wahrscheinlichkeit der Konvergenz der Optimierung zu erhöhen, wird eine hierarchische Struktur zur Bewertung der Zielfunktionen eingeführt. Dies hilft dem Optimierer bei der Lokalisierung des kleinen zulässigen Bereichs, der im Wesentlichen aus der Anforderung an die Stabilität des Magnetlagersystems resultiert. Desweitern wird eine Strategie zur Reduzierung der Optimierungsparameter entwickelt, die auf der Sensitivitätsanalyse der Reglerparameter basiert. Diese Strategie reduziert die Komplexität des Optimierungsproblems und führt zu einer Beschleunigung des Optimierungsprozesses. In der vorliegenden Arbeit wird der Reglerentwurf von zwei Magnetlagersystemen berücksichtigt. Mit Hilfe der eingeführten Strategie zur Bewertung der Zielfunktionen, werden die Reglerparameter von dem ersten Magnetlagersystem bestimmt bzw. optimiert, ohne dass irgendeine Information über die Anfangslösung erforderlich ist. Der optimale Reglerentwurf wird dann in einem Versuchstand implementiert, in dem eine elastische Welle durch zwei Magnetlager gelagert ist. Die Versuchsergebnisse zeigen, dass das gewünschte dynamische Verhalten des geregelten Magnetlagersystems durch die Optimierung erzielt wird. Die maximal zulässige Drehzahl (15000 rpm) des Versuchsstandes wird mit dem optimalen Regler ohne Probleme erreicht. Als zweites Beispiel wird der Reglerentwurf eines magnetgelagerten Rotorsystems eines Turboverdichters betrachtet. In der Reglerauslegung wird die vorgeschlagene Optimierungsstrategie mit Hilfe von Parameterreduktion verwendet. Die optimale Lösung wird lokal in der Nähe einer Anfangslösung gesucht. Die Optimierungsergebnisse zeigen die Effizienz der Optimierungsstrategie

Bioinspired Material Design: Modeling and Optimization of Nacre-like Materials

Nacre is an inner layer of seashells that is a tough yet stiff natural composite composed of microscopic mineral polygonal tablets bonded by a tough biopolymer. This type of structure is called staggered structure and it has been observed in other biological materials such as bone and teeth. The high stiffness of nacre is known to be due to its high mineral content. In spite of the high mineral content that is mainly ceramic, nacre exhibit high values of toughness. This high value of toughness is explained by its ability to deform past a yield point and develop large inelastic strain over a large volume around defects and cracks. It has been known that the high strain is mainly due to sliding and waviness of the tablets. Researchers have been motivated by the structure of nacre and its mechanical properties in order to mimic this natural material to create new engineering composites with superior mechanical properties. However, mimicking nacre’s remarkable properties, to date, is still a challenge. This is due to fabrication challenges, lack of models that captures the important mechanics and difficulties in selecting the best combination of tablets and matrix materials, and system geometry for optimal mechanical performance (e.g., stiffness, strength and toughness) of the staggered structure. Previous attempts to create analytical models for nacre include tablet sliding but don’t account for the waviness of the tablets. In this work, mathematical models for calculating the elastic modulus and toughness of nacre-like material are proposed to account for the waviness of the tablet. In addition, a multi-objective optimization problem is formulated and solved using an optimization method called ‘genetic algorithm’. Using the models developed in this work, a better prediction of the elastic modulus and toughness are obtained which agrees with experimental results found in the literature. The multi-objective optimization problem solved here results in a set of optimal solutions that the designers can select according to their preferences. This work will aid in the design and optimization of nacre-like materials that can enhance the performance of ceramic materials in different applications in areas such as material science, biomaterials development, civil, petroleum, biomedical and nanotechnology

Advances and applications in high-dimensional heuristic optimization

“Applicable to most real-world decision scenarios, multiobjective optimization is an area of multicriteria decision-making that seeks to simultaneously optimize two or more conflicting objectives. In contrast to single-objective scenarios, nontrivial multiobjective optimization problems are characterized by a set of Pareto optimal solutions wherein no solution unanimously optimizes all objectives. Evolutionary algorithms have emerged as a standard approach to determine a set of these Pareto optimal solutions, from which a decision-maker can select a vetted alternative. While easy to implement and having demonstrated great efficacy, these evolutionary approaches have been criticized for their runtime complexity when dealing with many alternatives or a high number of objectives, effectively limiting the range of scenarios to which they may be applied. This research introduces mechanisms to improve the runtime complexity of many multiobjective evolutionary algorithms, achieving state-of-the-art performance, as compared to many prominent methods from the literature. Further, the investigations here presented demonstrate the capability of multiobjective evolutionary algorithms in a complex, large-scale optimization scenario. Showcasing the approach’s ability to intelligently generate well-performing solutions to a meaningful optimization problem. These investigations advance the concept of multiobjective evolutionary algorithms by addressing a key limitation and demonstrating their efficacy in a challenging real-world scenario. Through enhanced computational efficiency and exhibited specialized application, the utility of this powerful heuristic strategy is made more robust and evident”--Abstract, page iv