Scaling up a Project Portfolio Selection Technique by using Multiobjective Genetic Optimization

This paper proposes a multiobjective heuristic search approach to support a project portfolio selection technique on scenarios with a large number of candidate projects. The original formulation for the technique requires analyzing all combinations of the candidate projects, which turns to be unfeasible when more than a few alternatives are available. We have used a multiobjective genetic algorithm to partially explore the search space of project combinations and select the most effective ones. We present an experimental study based on four real-world project selection problems that compares the results found by the genetic algorithm to those yielded by a non-systematic search procedure (random search). A second experimental study evaluates the best parameter settings to perform the heuristic search. Experimental results show evidence that the project selection technique can be used in large-scale scenarios and that the genetic algorithm presents better results than simpler search strategies

A review of asset management literature on multi-asset systems

This article gives an overview of the literature on asset management for multi-unit systems with an emphasis on two multi-asset categories: fleet (a system of homogeneous assets) and portfolio (a system of heterogeneous assets). As asset systems become more complicated, researchers have employed different terms to refer to their specific problems. With an objective to facilitate readers in searching conducive studies to their interests, this paper establishes a novel classification scheme for multi-unit systems in accordance with essential features such as diversity of assets and intervention options. Moreover, discerning differences in characteristics between cross-component and cross-asset interactions, we select three types of potential multi-component dependencies (performance, stochastic, and resource) and extend their notions to be applicable to multi-asset systems. The investigation into these dependencies enables the identification of problems that could exist in real industrial settings but are yet to be determined in academia. Ultimately, we delve into modelling approaches adopted by previous researchers. This comprehensive information allows us to offer the insights into the current trends in multi-asset maintenance. We expect that the output of this review paper will not only stress research gaps on multi-asset systems, but more importantly help systematise future studies on this aspect

Evolutionary multi-objective optimization in investment portfolio management

Ph.DDOCTOR OF PHILOSOPH

Four essays on environmental policy under uncertainty with applications to water quality and carbon sequestration

In this thesis, I present four essays that deal with several diverse issues in environmental economics, ranging from soil carbon sequestration, to a design of a pollution permit trading program, to proposing watershed-scale solutions to water quality problems, both on state and regional scale.;The first essay is titled Environmental policy under benefit and cost uncertainty: application to soil carbon offsets . I characterize an optimal spatial allocation of land parcels to specific environmental practices explicitly dealing with uncertainty in both the benefits and program costs. The results provide a magnitude of uncertainty discount for soil carbon offsets and the margin of safety necessary in the budget to ensure at the planning stage that the program\u27s costs will not exceed the planned expenditures.;The second essay is titled Optimal design of permit markets with an ex ante pollution target . In this essay, the design of permit trading programs when the objective is to minimize the cost of achieving an ex ante pollution target; that is, one that is defined in expectation rather than an ex post deterministic value, is examined. I demonstrate that to minimize expected abatement costs regulators must use information on the joint distribution of firms\u27 abatement costs, as well as the pollution delivery coefficients. As a result, the optimal trading ratio is a function of the delivery coefficient, as well as the moments of abatement costs, and the total permit allocation deviates from the pollution goal. These findings differ from a typical permit market design, where no cost information is needed to achieve cost-efficiency, the trading ratio is set to the ratio of pollution delivery coefficients, and the permit allocation exactly equals the pollution goal.;The third and the fourth chapters of the thesis build a simulation-optimization modeling framework for the analysis of efficient nonpoint source pollution reduction strategies. These essays integrate modern multi-objective optimization tools with a realistic water quality model to provide decision-makers with sets of cost-efficient pollution reduction solutions.;In the third essay, titled Efficient reductions in local and state-level nonpoint source nutrient pollution: an application to the state of Iowa, I incorporate a water quality model, SWAT, in conjunction with detailed information on conservation practices, into an evolutionary search algorithm to find allocations of conservation practices that minimize the costs of achieving given water quality targets for all the major watersheds in the state of Iowa.;In the final essay, titled Searching for efficiency: least cost nonpoint source pollution control with multiple pollutants, practices, and targets , I examine the policy implications of efficient control of nonpoint source pollution using a spatially explicit model of a large and critically important agricultural region: the Upper Mississippi River Basin in the central U.S. I derive the conservation production possibility frontier that explicitly incorporates the tradeoffs between pollution control costs and water quality benefits, between different pollutants, or between different control targets. The regional scale of the modeling framework facilitates the investigation of relevant policy analyses related to the growing dead zone in the Gulf of Mexico

From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing

Evolutionary Search Techniques with Strong Heuristics for Multi-Objective Feature Selection in Software Product Lines

Software design is a process of trading off competing objectives. If the user objective space is rich, then we should use optimizers that can fully exploit that richness. For example, this study configures software product lines (expressed as feature models) using various search-based software engineering methods. Our main result is that as we increase the number of optimization objectives, the methods in widespread use (e.g. NSGA-II, SPEA2) perform much worse than IBEA (Indicator-Based Evolutionary Algorithm). IBEA works best since it makes most use of user preference knowledge. Hence it does better on the standard measures (hypervolume and spread) but it also generates far more products with 0 violations of domain constraints. We also present significant improvements to IBEA\u27s performance by employing three strong heuristic techniques that we call PUSH, PULL, and seeding. The PUSH technique forces the evolutionary search to respect certain rules and dependencies defined by the feature models, while the PULL technique gives higher weight to constraint satisfaction as an optimization objective and thus achieves a higher percentage of fully-compliant configurations within shorter runtimes. The seeding technique helps in guiding very large feature models to correct configurations very early in the optimization process. Our conclusion is that the methods we apply in search-based software engineering need to be carefully chosen, particularly when studying complex decision spaces with many optimization objectives. Also, we conclude that search methods must be customized to fit the problem at hand. Specifically, the evolutionary search must respect domain constraints

Multi-objective evolutionary optimization in time-changing environments

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2007.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 127-135).This research is focused on the creation of evolutionary optimization techniques for the solution of time-changing multi-objective problems. Many optimization problems, ranging from the design of controllers for time-variant systems to the optimal asset allocation in financial portfolios, need to satisfy multiple conflicting objectives that change in time. Since most practical problems involve costly numerical simulations, the goal was to create algorithmic architectures that increase computational efficiency while being robust and widely applicable. A combination of two elements lies at the core of the proposed algorithm. First, there is an anticipatory population that helps the algorithm discover the new optimum when the objective landscape moves in time. Second, a preservation of the balance between convergence and diversity in the population which provides an exploration ability to the algorithm. If there is an amount of predictability in the landscape's temporal change pattern the anticipatory population increases performance by discovering each timestep's optimal solution using fewer function evaluations. It does so by estimating the optimal solution's motion with a forecasting model and then placing anticipatory individuals at the estimated future location.(cont.) In parallel, the preservation of diversity ensures that the optimum will be discovered even if the objectives motion is unpredictable. Together these two elements aim to create a well-performing and robust algorithmic architecture. Experiments show that the overall concept functions well and that the anticipatory population increases algorithm performance by up to 30%. Constraint handling methods for evolutionary algorithms are also implemented, since most of the problems treated in this work are constrained. In its final form the constraint handling method applied is a hybrid variant of the Superiority of Feasible Points, which works in a staged manner. Three different real-world applications are explored. Initially a radar telescope array is optimized for cost and performance as a practical example of a static multi-objective constrained problem. Subsequently, two time-changing problems are studied: the design of an industrial controller and the optimal asset allocation for a financial portfolio. These problems serve as examples of applications for time-changing multi-objective evolutionary algorithms and inspire the improvement of the methods proposed in this work.by Iason Hatzakis.Ph.D