A Hybrid Tabu/Scatter Search Algorithm for Simulation-Based Optimization of Multi-Objective Runway Operations Scheduling

As air traffic continues to increase, air traffic flow management is becoming more challenging to effectively and efficiently utilize airport capacity without compromising safety, environmental and economic requirements. Since runways are often the primary limiting factor in airport capacity, runway operations scheduling emerge as an important problem to be solved to alleviate flight delays and air traffic congestion while reducing unnecessary fuel consumption and negative environmental impacts. However, even a moderately sized real-life runway operations scheduling problem tends to be too complex to be solved by analytical methods, where all mathematical models for this problem belong to the complexity class of NP-Hard in a strong sense due to combinatorial nature of the problem. Therefore, it is only possible to solve practical runway operations scheduling problem by making a large number of simplifications and assumptions in a deterministic context. As a result, most analytical models proposed in the literature suffer from too much abstraction, avoid uncertainties and, in turn, have little applicability in practice. On the other hand, simulation-based methods have the capability to characterize complex and stochastic real-life runway operations in detail, and to cope with several constraints and stakeholders’ preferences, which are commonly considered as important factors in practice. This dissertation proposes a simulation-based optimization (SbO) approach for multi-objective runway operations scheduling problem. The SbO approach utilizes a discrete-event simulation model for accounting for uncertain conditions, and an optimization component for finding the best known Pareto set of solutions. This approach explicitly considers uncertainty to decrease the real operational cost of the runway operations as well as fairness among aircraft as part of the optimization process. Due to the problem’s large, complex and unstructured search space, a hybrid Tabu/Scatter Search algorithm is developed to find solutions by using an elitist strategy to preserve non-dominated solutions, a dynamic update mechanism to produce high-quality solutions and a rebuilding strategy to promote solution diversity. The proposed algorithm is applied to bi-objective (i.e., maximizing runway utilization and fairness) runway operations schedule optimization as the optimization component of the SbO framework, where the developed simulation model acts as an external function evaluator. To the best of our knowledge, this is the first SbO approach that explicitly considers uncertainties in the development of schedules for runway operations as well as considers fairness as a secondary objective. In addition, computational experiments are conducted using real-life datasets for a major US airport to demonstrate that the proposed approach is effective and computationally tractable in a practical sense. In the experimental design, statistical design of experiments method is employed to analyze the impacts of parameters on the simulation as well as on the optimization component’s performance, and to identify the appropriate parameter levels. The results show that the implementation of the proposed SbO approach provides operational benefits when compared to First-Come-First-Served (FCFS) and deterministic approaches without compromising schedule fairness. It is also shown that proposed algorithm is capable of generating a set of solutions that represent the inherent trade-offs between the objectives that are considered. The proposed decision-making algorithm might be used as part of decision support tools to aid air traffic controllers in solving the real-life runway operations scheduling problem

Models, algorithms, and heuristics for multiobjective commercial territory design

In general, distribution firms have complex product distribution networks which are formed by thousands of sales points. In this kind of industry there are many interesting problems from the logistic point of view that can appear in different stages of the decision process. For instance, when a firm is starting, a first problem could be the facility location: where to install the warehouses and/or distribution centers. After that, in order to provide efficient service and to reduce total costs (i.e., production, stock and distribution costs) some questions such as how many products need to be produced, and how to deliver the products to the final customer, need to be answered

Enfoque multiobjetivo de una metaheurística para la generación y optimización de planes de ruteo para el transporte terrestre de pasajeros empleando líneas predefinidas, basada en técnicas de algoritmos evolutivos híbridos

En el mundo real existe una gran cantidad de problemas de optimización para los cuales los métodos de programación matemática no pueden garantizar que la solución obtenida sea óptima y, en muchos de ellos, ni siquiera pueden aplicarse. En estos problemas las metaheurísticas se vuelven una alternativa viable. De entre las diversas metaheurísticas disponibles en la actualidad, los algoritmos Genéticos Híbridos se cuentan entre los más populares debido a su simplicidad conceptual y su eficacia. Hoy en día, los problemas de optimización multiobjetivo se presentan con mucha frecuencia. En ellos hay que optimizar dos o más funciones objetivo que se encuentran normalmente en conflicto entre sí. La solución de problemas multiobjetivo usando metaheurísticas (sobre todo, Algoritmos Genéticos Híbridos) es un tema que ha adquirido gran popularidad en los últimos tiempos. En este documento presentamos un Algoritmo Genético Híbrido Multiobjetivo para la generación y optimización de planes de ruteo, asignación de frecuencias y horarios de despacho empleando líneas predefinidas. Además se documenta un caso de real de uso en una empresa transportadora de pasajeros terrestre en Colombia.MaestríaMagister en Ingeniería Industria

Faster Evolutionary Multi-Objective Optimization via GALE, the Geometric Active Learner

Goal optimization has long been a topic of great interest in computer science. The literature contains many thousands of papers that discuss methods for the search of optimal solutions to complex problems. In the case of multi-objective optimization, such a search yields iteratively improved approximations to the Pareto frontier, i.e. the set of best solutions contained along a trade-off curve of competing objectives.;To approximate the Pareto frontier, one method that is ubiquitous throughout the field of optimization is stochastic search. Stochastic search engines explore solution spaces by randomly mutating candidate guesses to generate new solutions. This mutation policy is employed by the most commonly used tools (e.g. NSGA-II, SPEA2, etc.), with the goal of a) avoiding local optima, and b) expand upon diversity in the set of generated approximations. Such blind mutation policies explore many sub-optimal solutions that are discarded when better solutions are found. Hence, this approach has two problems. Firstly, stochastic search can be unnecessarily computationally expensive due to evaluating an overwhelming number of candidates. Secondly, the generated approximations to the Pareto frontier are usually very large, and can be difficult to understand.;To solve these two problems, a more-directed, less-stochastic approach than standard search tools is necessary. This thesis presents GALE (Geometric Active Learning). GALE is an active learner that finds approximations to the Pareto frontier by spectrally clustering candidates using a near-linear time recursive descent algorithm that iteratively divides candidates into halves (called leaves at the bottom level). Active learning in GALE selects a minimally most-informative subset of candidates by only evaluating the two-most different candidates during each descending split; hence, GALE only requires at most, 2Log2(N) evaluations per generation. The candidates of each leaf are thereafter non-stochastically mutated in the most promising directions along each piece. Those leafs are piece-wise approximations to the Pareto frontier.;The experiments of this thesis lead to the following conclusion: a near-linear time recursive binary division of the decision space of candidates in a multi-objective optimization algorithm can find useful directions to mutate instances and find quality solutions much faster than traditional randomization approaches. Specifically, in comparative studies with standard methods (NSGA-II and SPEA2) applied to a variety of models, GALE required orders of magnitude fewer evaluations to find solutions. As a result, GALE can perform dramatically faster than the other methods, especially for realistic models

Meta-heuristics in cellular manufacturing: A state-of-the-art review

Meta-heuristic approaches are general algorithmic framework, often nature-inspired and designed to solve NP-complete optimization problems in cellular manufacturing systems and has been a growing research area for the past two decades. This paper discusses various meta-heuristic techniques such as evolutionary approach, Ant colony optimization, simulated annealing, Tabu search and other recent approaches, and their applications to the vicinity of group technology/cell formation (GT/CF) problem in cellular manufacturing. The nobility of this paper is to incorporate various prevailing issues, open problems of meta-heuristic approaches, its usage, comparison, hybridization and its scope of future research in the aforesaid area

Optimización de problemas multi-objetivo de empaquetado de palets mediante algoritmos evolutivos

En este trabajo se ha implementado un algoritmo evolutivo multi-objetivo paralelo, para resolver el problema de empaquetamiento en dos dimensiones con restricciones, para una aplicación de transporte de palets en camiones. El transporte de palets en camiones tiene una gran importancia en Andalucía y especialmente en el campo almeriense donde a diario salen hacia Europa cientos de camiones cargados de productos del campo. El problema de empaquetamiento en dos dimensiones (2DPP) consiste en insertar un conjunto de objetos caracterizados por tener un alto y ancho específico, en el menor número de camiones posibles donde el alto y ancho es igual para todos. A partir de esta definición existen multitud de variantes al problema. En la variante multi-objetivo del problema, además de minimizar el número de camiones, se intenta minimizar el balanceo de carga de los mismos intentando colocar la carga de las piezas de la mejor forma posible para que el centro de gravedad del camión quede lo más cercano posible al centro deseado. El algoritmo se ha aplicado a una variante del problema donde se trata de insertar un conjunto de palets con su alto, ancho y peso específico, en el menor número de camiones posible y con el mejor balanceo de carga, evitando una serie de restricciones añadidas al problema. Cada palet corresponde a un cliente, con lo cual todos los palets de un mismo cliente deben de ir en el mismo camión, cada camión no puede ir cargado con más de 25000 kilos y el centro de gravedad debe de ir lo más próximo al eje del camión. Para la optimización de este problema hemos implementado un algoritmo evolutivo TPMOEA, este tipo de algoritmos están inspirados en la teoría de la evolución de Darwin y en el desarrollo de la informática evolutiva. Los algoritmos evolutivos son técnicas de optimización y búsqueda de soluciones basadas en la selección natural y genética que permiten resolver problemas no lineales en los que interviene un alto número de variables en problemas complejos. El algoritmo ha sido implementado con un conjunto de operadores evolutivos diseñados para obtener soluciones de gran calidad para un conjunto de instancias establecidas

Evolutionary approaches for portfolio optimization

Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk objectives. Markowitz’s mean variance (MV) model is widely regarded as the foundation of modern portfolio theory and provides a quantitative framework for portfolio optimization problems. In real market, investors commonly face real-world trading restrictions and it requires that the constructed portfolios have to meet trading constraints. When additional constraints are added to the basic MV model, the problem thus becomes more complex and the exact optimization approaches run into difficulties to deliver solutions within reasonable time for large problem size. By introducing the cardinality constraint alone already transformed the classic quadratic optimization model into a mixed-integer quadratic programming problem which is an NP-hard problem. Evolutionary algorithms, a class of metaheuristics, are one of the known alternatives for optimization problems that are too complex to be solved using deterministic techniques. This thesis focuses on single-period portfolio optimization problems with practical trading constraints and two different risk measures. Four hybrid evolutionary algorithms are presented to efficiently solve these problems with gradually more complex real world constraints. In the first part of the thesis, the mean variance portfolio model is investigated by taking into account real-world constraints. A hybrid evolutionary algorithm (PBILDE) for portfolio optimization with cardinality and quantity constraints is presented. The proposed PBILDE is able to achieve a strong synergetic effect through hybridization of PBIL and DE. A partially guided mutation and an elitist update strategy are proposed in order to promote the efficient convergence of PBILDE. Its effectiveness is evaluated and compared with other existing algorithms over a number of datasets. A multi-objective scatter search with archive (MOSSwA) algorithm for portfolio optimization with cardinality, quantity and pre-assignment constraints is then presented. New subset generations and solution combination methods are proposed to generate efficient and diverse portfolios. A learning-guided multi-objective evolutionary (MODEwAwL) algorithm for the portfolio optimization problems with cardinality, quantity, pre-assignment and round lot constraints is presented. A learning mechanism is introduced in order to extract important features from the set of elite solutions. Problem-specific selection heuristics are introduced in order to identify high-quality solutions with a reduced computational cost. An efficient and effective candidate generation scheme utilizing a learning mechanism, problem specific heuristics and effective direction-based search methods is proposed to guide the search towards the promising regions of the search space. In the second part of the thesis, an alternative risk measure, VaR, is considered. A non-parametric mean-VaR model with six practical trading constraints is investigated. A multi-objective evolutionary algorithm with guided learning (MODE-GL) is presented for the mean-VaR model. Two different variants of DE mutation schemes in the solution generation scheme are proposed in order to promote the exploration of the search towards the least crowded region of the solution space. Experimental results using historical daily financial market data from S &P 100 and S & P 500 indices are presented. When the cardinality constraints are considered, incorporating a learning mechanism significantly promotes the efficient convergence of the search

Evolutionary approaches for portfolio optimization

Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk objectives. Markowitz’s mean variance (MV) model is widely regarded as the foundation of modern portfolio theory and provides a quantitative framework for portfolio optimization problems. In real market, investors commonly face real-world trading restrictions and it requires that the constructed portfolios have to meet trading constraints. When additional constraints are added to the basic MV model, the problem thus becomes more complex and the exact optimization approaches run into difficulties to deliver solutions within reasonable time for large problem size. By introducing the cardinality constraint alone already transformed the classic quadratic optimization model into a mixed-integer quadratic programming problem which is an NP-hard problem. Evolutionary algorithms, a class of metaheuristics, are one of the known alternatives for optimization problems that are too complex to be solved using deterministic techniques. This thesis focuses on single-period portfolio optimization problems with practical trading constraints and two different risk measures. Four hybrid evolutionary algorithms are presented to efficiently solve these problems with gradually more complex real world constraints. In the first part of the thesis, the mean variance portfolio model is investigated by taking into account real-world constraints. A hybrid evolutionary algorithm (PBILDE) for portfolio optimization with cardinality and quantity constraints is presented. The proposed PBILDE is able to achieve a strong synergetic effect through hybridization of PBIL and DE. A partially guided mutation and an elitist update strategy are proposed in order to promote the efficient convergence of PBILDE. Its effectiveness is evaluated and compared with other existing algorithms over a number of datasets. A multi-objective scatter search with archive (MOSSwA) algorithm for portfolio optimization with cardinality, quantity and pre-assignment constraints is then presented. New subset generations and solution combination methods are proposed to generate efficient and diverse portfolios. A learning-guided multi-objective evolutionary (MODEwAwL) algorithm for the portfolio optimization problems with cardinality, quantity, pre-assignment and round lot constraints is presented. A learning mechanism is introduced in order to extract important features from the set of elite solutions. Problem-specific selection heuristics are introduced in order to identify high-quality solutions with a reduced computational cost. An efficient and effective candidate generation scheme utilizing a learning mechanism, problem specific heuristics and effective direction-based search methods is proposed to guide the search towards the promising regions of the search space. In the second part of the thesis, an alternative risk measure, VaR, is considered. A non-parametric mean-VaR model with six practical trading constraints is investigated. A multi-objective evolutionary algorithm with guided learning (MODE-GL) is presented for the mean-VaR model. Two different variants of DE mutation schemes in the solution generation scheme are proposed in order to promote the exploration of the search towards the least crowded region of the solution space. Experimental results using historical daily financial market data from S &P 100 and S & P 500 indices are presented. When the cardinality constraints are considered, incorporating a learning mechanism significantly promotes the efficient convergence of the search