Examination timetabling automation using hybrid meta-heuristics

Trabalho de projeto realizado para obtenção do grau de Mestre em Engenharia Informática e de ComputadoresNos últimos anos, o tema da geração automática de horários tem sido alvo de muito estudo. Em muitas instituições, a elaboração de horários ainda é feita manualmente, constituindo-se uma tarefa demorada e penosa para instâncias de grande dimensão. Outro problema recorrente na abordagem manual é a existência de falhas dada a dificuldade do processo de verificação, e também a qualidade final do horário produzido. Se este fosse criado por computador, o horário seria válido e seriam de esperar horários com qualidade superior dada a capacidade do computador para pesquisar o espaço de soluções. A elaboração de horários não é uma tarefa fácil, mesmo para uma máquina. Por exemplo, horários escolares necessitam de seguir certas regras para que seja possível a criação de um horário válido. Mas como o espaço de estados (soluções) válidas é tão vasto, é impraticável criar um algoritmo que faça a enumeração completa de soluções a fim de escolher a melhor solução possível. Por outro lado, a utilização de algoritmos que realizam a enumeração implícita de soluções (por exemplo, branch and bound), não é viável para problemas de grande dimensão. A utilização de heurísticas que percorrem de uma forma guiada o espaço de estados, conseguindo assim uma solução razoável em tempo útil, constituem uma abordagem adequada para este tipo de problemas. Um dos objetivos do projeto consiste na criação duma abordagem que siga as regras do International Timetabling Competition (ITC) 2007 incidindo na criação de horários de exames em universidades (Examination timetabling track). Este projeto utiliza uma abordagem de heurísticas híbridas. Isto significa que utiliza múltiplas heurísticas para obter a melhor solução possível. Utiliza uma variação da heurística de Graph Coloring para obter uma solução válida e as meta-heurísticas Simulated Annealing e Hill Climbing para melhorar a solução obtida. Os resultados finais são satisfatórios, pois em algumas instâncias os resultados são melhores do que alguns dos cinco finalistas do concurso ITC 2007.Abstract: In the last few years the automatic creation of timetables is being a well-studied subject. In many institutions, the elaboration of timetables is still manual, thus being a time-consuming and difficulty task for large instances. Another current problem in the manual approach is the existence of failures given the difficulty in the process verification, and so the quality of the produced timetable. If this timetable had been created by a computer, the timetable would be valid and timetables with better quality should be obtained, given the computer’s capacity to search the solution space. It is not easy to elaborate timetables, even for a machine. For example, scholar/university timetables need to follow certain type of constraints or rules for them to be considered valid. But since the solution space is so vast, it is highly unlikely to create an algorithm that completely enumerates the solutions in order to choose the best solution possible, considering the problem structure. The use of algorithms that perform implicit enumeration solutions (for example, an branch bound), is not feasible for large problems. Hence the use of heuristics which navigate through the solution space in a guided way, obtaining then a reasonable solution in acceptable time. One main objective of this project consists in creating an approach that follows the International Timetabling Competition (ITC) 2007 rules, focusing on creating examination timetables. This project will use a hybrid approach. This means it will use an approach that includes multiple heuristics in order to find the best possible solution. This approach uses a variant of the Graph Coloring heuristic to find an initial valid solution, and the metaheuristics Simulated Annealing and Hill Climbing to improve that solution. The final results are satisfactory, as in some instances the obtained results beat the results of some of the five finalists from ITC 2007

Medición de la eficiencia y la productividad: Aspectos computacionales

Programa de Doctorado en Economía (DECiDE)The purpose of efficiency and productivity problems is based on evaluating whether the use of the resources available (inputs) by a company or public institution (in general, any decision-making unit) corresponds or not with the optimal way of operating in such a way as to generate the largest possible number of outputs. To carry out this type of calculations, several mathematical models have already been proposed in the specialized literature that can be used, all of which are based on Mathematical Programming problems, and, in particular, some of them correspond to Mixed Integer Linear Programming problems (MILP). These types of problems combine several types of variables, continuous and discrete, in the same mathematical model as well as numerous restrictions, depending on the nature of the problem; features that can make the resolution process somewhat difficult. In addition, it is worth noting that these problems tend to be combinatorial in practice (NP-hard). Throughout this work, the analysis and study will focus on a field within the area of Operations Research called Data Envelopment Analysis (DEA), whose main objective is the estimation of production frontiers and the measurement of productive efficiency. Different optimization models belonging to this field will be put to the test in this thesis from a purely computational perspective, being solved through different techniques, both 2 exact and approximate, analyzing the performance and the difficulty of the same. The main objective of this work does not lie in the development and modeling of new problems in the field of DEA, but in how to achieve optimal solutions in a reasonable time for certain problems of a combinatorial nature, given that being NP-hard type problems, as the size of the problem grows, so does the difficulty of obtaining optimal solutions, especially in a short time. At this point, we will focus on the study and design of approximation techniques, known in the literature as Metaheuristics, closely linked to Machine Learning or Artificial Intelligence methodologies. In addition to these methodologies, based on learning and improving the solutions obtained, parallelization techniques have also been incorporated, capable of efficiently reducing the time needed to obtain optimal solutions in complex problems.La finalidad de los problemas de eficiencia y productividad se basan en evaluar si el uso de los recursos (entradas o inputs, en inglés) disponibles por parte de una empresa o institución pública (en general, cualquier unidad tomadora de decisiones) se corresponde o no con la forma óptima de operar de dicha entidad, generando la mayor cantidad de salidas posible (outputs en inglés). Para llevar a cabo este tipo de cálculos, varios modelos matemáticos han sido ya planteados en la literatura especializada que pueden ser utilizados, teniendo en común todos ellos que están basados en problemas de Programación Matemática, y, en particular, algunos de ellos se corresponden con problemas de Programación Matemática Lineal Mixta (Mixed Integer Linear Programming en inglés – MILP). Este tipo de problemas combinan en un mismo modelo matemático varios tipos de variables, continuas y discretas, así como numerosas restricciones, dependiendo de la naturaleza del problema, siendo estas restricciones características que pueden hacer que el proceso de resolución resulte ser algo difícil. Además, cabe destacar la característica de que estos problemas suelen ser en la práctica de tipo combinatorio (NP-duros). A lo largo de este trabajo, el análisis y el estudio se va a centrar en un campo dentro del área de Investigación Operativa denominado Análisis Envolvente de Datos (Data Envelopment Analysis en inglés - DEA), cuyo principal objetivo es el de la estimación de fronteras de producción y la medición de la eficiencia productiva. Diferentes modelos de optimización pertenecientes a este ámbito serán puestos a prueba en esta tesis desde una perspectiva puramente computacional, siendo resueltos a través de diferentes técnicas, tanto exactas como de aproximación, analizando el rendimiento y la dificultad del mismo. El objetivo principal de este trabajo no reside en el desarrollo y modelado de nuevos problemas en el ámbito del DEA, sino en cómo conseguir soluciones óptimas y eficientes en un tiempo razonable para ciertos problemas de naturaleza combinatoria, dado que al ser problemas de tipo NP-duro, a medida que el tamaño del problema crece, también lo hace la dificultad de obtener soluciones óptimas, sobre todo en un tiempo reducido. En este punto, centraremos la atención en el estudio y diseño de técnicas de aproximación, conocidas en la literatura como Metaheurísticas, estando muy ligadas a metodologías de Machine Learning o Artificial Inteligence. Además de estas metodologías, basadas en el aprendizaje y la mejora de las soluciones obtenidas, también se han incorporado técnicas de paralelismo, capaces de reducir de forma eficiente el tiempo necesario para obtener soluciones óptimas en problemas complejos

Metaheuristic Approaches For Estimating In-Kind Food Donations Availability And Scheduling Food Bank Vehicles

Food banks provide services that allow households facing food insecurity to receive nutritious food items. Food banks, however, experience operational challenges as a result of constrained and uncertain supply and complex routing challenges. The goal of this research is to explore opportunities to enhance food bank operations through metaheuristic forecasting and scheduling practices. Knowledge discovery methods and supervised machine learning are used to forecast food availability at supermarkets. In particular, a quasi-greedy algorithm which selects multi-layer perceptron models to represent food availability is introduced. In addition, a new classification of the vehicle routing problem is proposed to manage the distribution and collection of food items. In particular, variants of the periodic vehicle routing problem backhauls are introduced. In addition to discussing model formulations for the routing problems, a hybrid genetic algorithm is introduced which finds good solutions for larger problem instances in a reasonable computation time

A computational study of the Firefighter Problem on graphs

Orientadores: Cid Carvalho de Souza, Pedro Jussieu de RezendeDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O Problema do Brigadista em Grafos (FFP do inglês The Firefighter Problem é um modelo determinístico e em tempo discreto para simular a propagação e contenção de incêndios em grafos. Ele pode ser descrito da seguinte forma. Na entrada, é dado um inteiro D representando a quantidade de brigadistas disponíveis, um grafo não direcionado e não ponderado G = (V,E) e um subconjunto de vértices B de V, os focos de incêndio. Então, inicia-se nos elementos de B um processo iterativo de propagação e contenção de fogo através dos vértices de G, em rodadas discretas, o qual termina quando não existem mais vértices que possam ser queimados, ou seja, quando o fogo está contido. O objetivo ao resolver o FFP é maximizar o número de vértices não queimados quando o fogo é contido, com a restrição de que no máximo D vértices podem ser protegidos contra o fogo por rodada. Aplicações práticas do FFP, além da obtenção de estratégias para minimização de danos causados por incêndios, podem ser encontradas em áreas como controle de doenças e segurança em redes. O FFP é NP-difícil e métodos heurísticos para lidar com o problema foram relatados previamente na literatura. Nessa dissertação, primeiramente, apresentamos melhorias feitas na primeira formulação PLI proposta para o FFP através de técnicas de preprocessamento e agregação de restrições. Em seguida, descrevemos novas heurísticas gulosas e introduzimos uma nova matheurística para o FFP, uma abordagem que se baseia na interoperação entre meta-heurísticas e programação matemática. Experimentos foram conduzidos em um benchmark público tanto para configuração de parâmetros quanto para análise de desempenho, através de comparação dos resultados obtidos com aqueles publicados anteriormente. Com respeito às modificações no modelo PLI, um speedup de aproximadamente 2 em média foi alcançado. Observamos que as modificações feitas podem levar a geração de soluções infactíveis, mas conseguimos demonstrar que é possível tornar tais soluções factíveis em tempo polinomial. Em referência às heurísticas, essas foram executadas seguindo uma metodologia para construir uma solução na qual escolhas gulosas aleatorizadas são realizadas para selecionar quais vértices serão defendidos, de acordo com conceitos introduzidos na meta-heurística GRASP. Comparando essas heurísticas com as que foram propostas por trabalhos anteriores, observamos que duas das nossas estão entre as cinco melhores na maioria dos casos. Com relação à matheurística, através de uma análise estatística rigorosa, verificamos que existe diferença estatisticamente significativa entre nossa estratégia e as demais, ao mesmo tempo que nossa matheurística conseguiu resultados melhores na maioria das instânciasAbstract: The firefighter problem (FFP) is a deterministic discrete-time model for the spread and containment of fire on a graph. Such problem is described as follows. As its inputs, there is an integer D representing the number of available firefighters, an undirected and unweighted graph G=(V, E) and a subset of vertices B of V, the fire outbreaks. Then, an iterative process of fire propagation and containment through the vertices of G is started at the ones from B. This process ends when there are no more vertices to be burnt, that is, the fire is contained. The goal when solving the FFP is to maximize the number of vertices that are not burned when the fire is contained, with the constraint that at most D vertices can be protected against the fire per iteration. Practical applications of the FFP, besides obtaining strategies to minimize the damage caused by fire, can be found in areas such as disease control and network security. The FFP is NP-hard and heuristic methods to tackle the problem were proposed earlier in the literature. In this dissertation, firstly we present modifications made on the first ILP model proposed to the FFP through techniques of preprocessing and constraint aggregation. Moreover, we describe new greedy heuristics and also we introduce a novel matheuristic to the FFP, an approach based on the interoperation between metaheuristics and mathematical programming. A series of computational experiments were conducted on a public benchmark both for parameter tuning and to compare our results with those obtained previously. In respect to the modifications on the ILP model, a speedup of 2 in average was obtained. While constraint aggregation can lead to infeasible solutions, we prove that the latter can be converted to feasible ones in linear time. Regarding the heuristics, they were executed following a methodology to construct a solution in which greedy randomized choices are made to select which vertices should be defended, according to concepts introduced by the GRASP metaheuristic. Comparing these heuristics with the ones proposed by previous works, we observe that two of ours are between the five best ones in general. In relation to the matheuristic, through rigorous statistical analysis, we were able to verify that there is a statistically significant difference between our strategy and the remaining ones, while our matheuristic had better results on the majority of the instancesMestradoCiência da ComputaçãoMestre em Ciência da Computação133728/2016-1CNP

Consolidation of Urban Freight Transport – Models and Algorithms

Urban freight transport is an indispensable component of economic and social life in cities. Compared to other types of transport, however, it contributes disproportionately to the negative impacts of traffic. As a result, urban freight transport is closely linked to social, environmental, and economic challenges. Managing urban freight transport and addressing these issues poses challenges not only for local city administrations but also for companies, such as logistics service providers (LSPs). Numerous policy measures and company-driven initiatives exist in the area of urban freight transport to overcome these challenges. One central approach is the consolidation of urban freight transport. This dissertation focuses on urban consolidation centers (UCCs) which are a widely studied and applied measure in urban freight transport. The fundamental idea of UCCs is to consolidate freight transport across companies in logistics facilities close to an urban area in order to increase the efficiency of vehicles delivering goods within the urban area. Although the concept has been researched and tested for several decades and it was shown that it can reduce the negative externalities of freight transport in cities, in practice many UCCs struggle with a lack of business participation and financial difficulties. This dissertation is primarily focused on the costs and savings associated with the use of UCCs from the perspective of LSPs. The cost-effectiveness of UCC use, which is also referred to as cost attractiveness, can be seen as a crucial condition for LSPs to be interested in using UCC systems. The overall objective of this dissertation is two-fold. First, it aims to develop models to provide decision support for evaluating the cost-effectiveness of using UCCs. Second, it aims to analyze the impacts of urban freight transport regulations and operational characteristics on the cost attractiveness of using UCCs from the perspective of LSPs. In this context, a distinction is made between UCCs that are jointly operated by a group of LSPs and UCCs that are operated by third parties who offer their urban transport service for a fee. The main body of this dissertation is based on three research papers. The first paper focuses on jointly-operated UCCs that are operated by a group of cooperating LSPs. It presents a simulation model to analyze the financial impacts on LSPs participating in such a scheme. In doing so, a particular focus is placed on urban freight transport regulations. A case study is used to analyze the operation of a jointly-operated UCC for scenarios involving three freight transport regulations. The second and third papers take on a different perspective on UCCs by focusing on third-party operated UCCs. In contrast to the first paper, the second and third papers present an evaluation approach in which the decision to use UCCs is integrated with the vehicle route planning of LSPs. In addition to addressing the basic version of this integrated routing problem, known as the vehicle routing problem with transshipment facilities (VRPTF), the second paper presents problem extensions that incorporate time windows, fleet size and mix decisions, and refined objective functions. To heuristically solve the basic problem and the new problem variants, an adaptive large neighborhood search (ALNS) heuristic with embedded local search heuristic and set partitioning problem (SPP) is presented. Furthermore, various factors influencing the cost attractiveness of UCCs, including time windows and usage fees, are analyzed using a real-world case study. The third paper extends the work of the second paper and incorporates daily and entrance-based city toll schemes and enables multi-trip routing. A mixed-integer linear programming (MILP) formulation of the resulting problem is proposed, as well as an ALNS solution heuristic. Moreover, a real-world case study with three European cities is used to analyze the impact of the two city toll systems in different operational contexts