04461 Abstracts Collection -- Practical Approaches to Multi-Objective Optimization

From 07.11.04 to 12.11.04, the Dagstuhl Seminar 04461 ``Practical Approaches to Multi-Objective Optimization\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

A similarity-based cooperative co-evolutionary algorithm for dynamic interval multi-objective optimization problems

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Dynamic interval multi-objective optimization problems (DI-MOPs) are very common in real-world applications. However, there are few evolutionary algorithms that are suitable for tackling DI-MOPs up to date. A framework of dynamic interval multi-objective cooperative co-evolutionary optimization based on the interval similarity is presented in this paper to handle DI-MOPs. In the framework, a strategy for decomposing decision variables is first proposed, through which all the decision variables are divided into two groups according to the interval similarity between each decision variable and interval parameters. Following that, two sub-populations are utilized to cooperatively optimize decision variables in the two groups. Furthermore, two response strategies, rgb0.00,0.00,0.00i.e., a strategy based on the change intensity and a random mutation strategy, are employed to rapidly track the changing Pareto front of the optimization problem. The proposed algorithm is applied to eight benchmark optimization instances rgb0.00,0.00,0.00as well as a multi-period portfolio selection problem and compared with five state-of-the-art evolutionary algorithms. The experimental results reveal that the proposed algorithm is very competitive on most optimization instances

The Energy-Efficient Dynamic Route Planning for Electric Vehicles

Aiming to provide an approach for finding energy-efficient routes in dynamic and stochastic transportation networks for electric vehicles, this paper addresses the route planning problem in dynamic transportation network where the link travel times are assumed to be random variables to minimize total energy consumption and travel time. The changeable signals are introduced to establish state-space-time network to describe the realistic dynamic traffic network and also used to adjust the travel time according to the signal information (signal cycle, green time, and red time). By adjusting the travel time, the electric vehicle can achieve a nonstop driving mode during the traveling. Further, the nonstop driving mode could avoid frequent acceleration and deceleration at the signal intersections so as to reduce the energy consumption. Therefore, the dynamically adjusted travel time can save the energy and eliminate the waiting time. A multiobjective 0-1 integer programming model is formulated to find the optimal routes. Two methods are presented to transform the multiobjective optimization problem into a single objective problem. To verify the validity of the model, a specific simulation is conducted on a test network. The results indicate that the shortest travel time and the energy consumption of the planning route can be significantly reduced, demonstrating the effectiveness of the proposed approaches. Document type: Articl

A MPC Strategy for the Optimal Management of Microgrids Based on Evolutionary Optimization

In this paper, a novel model predictive control strategy, with a 24-h prediction horizon, is proposed to reduce the operational cost of microgrids. To overcome the complexity of the optimization problems arising from the operation of the microgrid at each step, an adaptive evolutionary strategy with a satisfactory trade-off between exploration and exploitation capabilities was added to the model predictive control. The proposed strategy was evaluated using a representative microgrid that includes a wind turbine, a photovoltaic plant, a microturbine, a diesel engine, and an energy storage system. The achieved results demonstrate the validity of the proposed approach, outperforming a global scheduling planner-based on a genetic algorithm by 14.2% in terms of operational cost. In addition, the proposed approach also better manages the use of the energy storage system.Ministerio de Economía y Competitividad DPI2016-75294-C2-2-RUnión Europea (Programa Horizonte 2020) 76409

Scenario driven optimal sequencing under deep uncertainty

Abstract not availableEva H.Y. Beh, Holger R. Maier, Graeme C. Dand

Including adaptation and mitigation responses to climate change in a multiobjective evolutionary algorithm framework for urban water supply systems incorporating GHG emissions

Cities around the world are increasingly involved in climate action and mitigating greenhouse gas (GHG) emissions. However, in the context of responding to climate pressures in the water sector, very few studies have investigated the impacts of changing water use on GHG emissions, even though water resource adaptation often requires greater energy use. Consequently, reducing GHG emissions, and thus focusing on both mitigation and adaptation responses to climate change in planning and managing urban water supply systems, is necessary. Furthermore, the minimization of GHG emissions is likely to conflict with other objectives. Thus, applying a multiobjective evolutionary algorithm (MOEA), which can evolve an approximation of entire trade-off (Pareto) fronts of multiple objectives in a single run, would be beneficial. Consequently, the main aim of this paper is to incorporate GHG emissions into a MOEA framework to take into consideration both adaptation and mitigation responses to climate change for a city’s water supply system. The approach is applied to a case study based on Adelaide’s southern water supply system to demonstrate the framework’s practical management implications. Results indicate that trade-offs exist between GHG emissions and risk-based performance, as well as GHG emissions and economic cost. Solutions containing rainwater tanks are expensive, while GHG emissions greatly increase with increased desalinated water supply. Consequently, while desalination plants may be good adaptation options to climate change due to their climate-independence, rainwater may be a better mitigation response, albeit more expensive.F. L. Paton, H. R. Maier, and G. C. Dand

Computational Enterprise Modeling and Simulation: Enabling Enterprise Performance Improvement, Modernization and Transformation

Lean Advancement Initiative Annual Conference presentatio

Assessing the effectiveness of managed lane strategies for the rapid deployment of cooperative adaptive cruise control technology

Connected and Automated Vehicle (C/AV) technologies are fast expanding in the transportation and automotive markets. One of the highly researched examples of C/AV technologies is the Cooperative Adaptive Cruise Control (CACC) system, which exploits various vehicular sensors and vehicle-to-vehicle communication to automate vehicular longitudinal control. The operational strategies and network-level impacts of CACC have not been thoroughly discussed, especially in near-term deployment scenarios where Market Penetration Rate (MPR) is relatively low. Therefore, this study aims to assess CACC\u27s impacts with a combination of managed lane strategies to provide insights for CACC deployment. The proposed simulation framework incorporates 1) the Enhanced Intelligent Driver Model; 2) Nakagami-based radio propagation model; and 3) a multi-objective optimization (MOOP)-based CACC control algorithm. The operational impacts of CACC are assessed under four managed lane strategies (i.e., mixed traffic (UML), HOV (High Occupancy Vehicle)-CACC lane (MML), CACC dedicated lane (DL), and CACC dedicated lane with access control (DLA)). Simulation results show that the introduction of CACC, even with 10% MPR, is able to improve the network throughput by 7% in the absence of any managed lane strategies. The segment travel times for both CACC and non-CACC vehicles are reduced. The break-even point for implementing dedicated CACC lane is 30% MPR, below which the priority usage of the current HOV lane for CACC traffic is found to be more appropriate. It is also observed that DLA strategy is able to consistently increase the percentage of platooned CACC vehicles as MPR grows. The percentage of CACC vehicles within a platoon reaches 52% and 46% for DL and DLA, respectively. When it comes to the impact of vehicle-to-vehicle (V2V), it is found that DLA strategy provides more consistent transmission density in terms of median and variance when MPR reaches 20% or above. Moreover, the performance of the MOOP-based cooperative driving is examined. With average 75% likelihood of obtaining a feasible solution, the MOOP outperforms its counterpart which aims to minimize the headway objective solely. In UML, MML, and DL strategy, the proposed control algorithm achieves a balance spread among four objectives for each CACC vehicle. In the DLA strategy, however, the probability of obtaining feasible solution falls to 60% due to increasing size of platoon owing to DLA that constraints the feasible region by introduction more dimensions in the search space. In summary, UML or MML is the preferred managed lane strategy for improving traffic performance when MPR is less than 30%. When MRP reaches 30% or above, DL and DLA could improve the CACC performance by facilitating platoon formation. If available, priority access to an existing HOV lane can be adopted to encourage adaptation of CACC when CACC technology becomes publically available

A User-Friendly Wrapper for DSIDES (Decision Support in the Design of Engineering Systems)

When dealing with complex systems, we need to consider that these systems have behaviors that are hard to predict or control, and uncertainties are always present since computational models are abstracts of reality. It is recognized that in many situations, it may not be possible to simultaneously optimize all objectives due to inherent conflicts, resource limitations, or uncertainty. As George E.P. Box said: "All models are wrong, but some are useful." The consequences of these observations are significant. We need to accept that our models might not capture everything and that uncertainties are a part of the picture. Hence, we must accept and deal with uncertainty instead of ignoring it and find solutions that are relatively insensitive to the uncertainties. When choosing a method to work with, we need to consider the quality of our data. To make this all work, we need a method to find solutions that achieve a reasonable compromise or balance among the objectives and identify a set of solutions that are relatively insensitive to uncertainties. Also, be able to facilitate the exploration of solution space to support human decision-making. This ties into the problems we face in supporting decisions for complex systems. These problems involve choosing between options and making compromises. The compromise Decision Support Problem (cDSP) construct and the Adaptive Linear Programming algorithm has been developed as a result, which was first introduced by Mistree and co-authors (1993). It is a domain-independent, multiobjective decision model based on mathematical and goal programming. They effectively deal with multiobjective problems involving bounds, linear and nonlinear constraints, goals, and consisting of Boolean and continuous variables. The requirements for this construct are: 1) Identify a set of solutions that are relatively insensitive to uncertainties 2) Facilitate the exploration of solution space to support human decision-making Mistree and co-authors also designed a computer program to implement cDSP construct. It has been written in FORTRAN to identify robust satisficing solutions to design problems when the models are abstractions of reality. It is called DSIDES (Decision Support in the Design of Engineering Systems). DSIDES is a software tool developed to help engineers and designers make better decisions in the design of complex engineering systems and provides decision support for the design of complex engineering systems. In this thesis, our primary objective is to enhance the accessibility and user-friendliness of DSIDES by designing a user-friendly wrapper. Three key areas of focus are included in this thesis: 1) Exploration of cDSP Construct: In this part, the examination of the cDSP (Compromise Decision Support Problem) construct, including its structural components and the formulation of problem statements within the cDSP framework, has been discussed. 2) Comprehensive Analysis of the DSIDES Wrapper: A detailed exploration of the DSIDES wrapper and a step-by-step walkthrough of the wrapper's functionalities are covered. 3) DSIDES Software Program Manuals: Program manuals for the DSIDES software has been created. These manuals are helpful resources for individuals seeking to enhance, expand, or modify the software. Based on these key areas of focus, there are three different parts to this thesis: 1) Part One: DSIDES Software and cDSP Construct: An Introduction. 2) Part Two: Designing the User-Friendly Wrapper for DSIDES. 3) Part Three: Program Manuals and Improvement of DSIDES. In the following sections, all three parts and their related details are discussed, respectively

Energy-aware scheduling in distributed computing systems

Distributed computing systems, such as data centers, are key for supporting modern computing demands. However, the energy consumption of data centers has become a major concern over the last decade. Worldwide energy consumption in 2012 was estimated to be around 270 TWh, and grim forecasts predict it will quadruple by 2030. Maximizing energy efficiency while also maximizing computing efficiency is a major challenge for modern data centers. This work addresses this challenge by scheduling the operation of modern data centers, considering a multi-objective approach for simultaneously optimizing both efficiency objectives. Multiple data center scenarios are studied, such as scheduling a single data center and scheduling a federation of several geographically-distributed data centers. Mathematical models are formulated for each scenario, considering the modeling of their most relevant components such as computing resources, computing workload, cooling system, networking, and green energy generators, among others. A set of accurate heuristic and metaheuristic algorithms are designed for addressing the scheduling problem. These scheduling algorithms are comprehensively studied, and compared with each other, using statistical tools to evaluate their efficacy when addressing realistic workloads and scenarios. Experimental results show the designed scheduling algorithms are able to significantly increase the energy efficiency of data centers when compared to traditional scheduling methods, while providing a diverse set of trade-off solutions regarding the computing efficiency of the data center. These results confirm the effectiveness of the proposed algorithmic approaches for data center infrastructures.Los sistemas informáticos distribuidos, como los centros de datos, son clave para satisfacer la demanda informática moderna. Sin embargo, su consumo de energético se ha convertido en una gran preocupación. Se estima que mundialmente su consumo energético rondó los 270 TWh en el año 2012, y algunos prevén que este consumo se cuadruplicará para el año 2030. Maximizar simultáneamente la eficiencia energética y computacional de los centros de datos es un desafío crítico. Esta tesis aborda dicho desafío mediante la planificación de la operativa del centro de datos considerando un enfoque multiobjetivo para optimizar simultáneamente ambos objetivos de eficiencia. En esta tesis se estudian múltiples variantes del problema, desde la planificación de un único centro de datos hasta la de una federación de múltiples centros de datos geográficmentea distribuidos. Para esto, se formulan modelos matemáticos para cada variante del problema, modelado sus componentes más relevantes, como: recursos computacionales, carga de trabajo, refrigeración, redes, energía verde, etc. Para resolver el problema de planificación planteado, se diseñan un conjunto de algoritmos heurísticos y metaheurísticos. Estos son estudiados exhaustivamente y su eficiencia es evaluada utilizando una batería de herramientas estadísticas. Los resultados experimentales muestran que los algoritmos de planificación diseñados son capaces de aumentar significativamente la eficiencia energética de un centros de datos en comparación con métodos tradicionales planificación. A su vez, los métodos propuestos proporcionan un conjunto diverso de soluciones con diferente nivel de compromiso respecto a la eficiencia computacional del centro de datos. Estos resultados confirman la eficacia del enfoque algorítmico propuesto