Planning, Scheduling, and Timetabling in a University Setting

Methods and procedures for modeling university student populations, predicting course enrollment, allocating course seats, and timetabling final examinations are studied and proposed. The university enrollment model presented uses a multi-dimensional state space based on student demographics and the Markov property, rather than longitudinal data to model student movement. The procedure for creating adaptive course prediction models uses student characteristics to identify groups of undergraduates whose specific course enrollment rates are significantly different than the rest of the university population. Historical enrollment rates and current semester information complete the model for predicting enrollment for the coming semester. The course prediction model aids in the system for reserving course seats for new students during summer registration sessions. The seat release model addresses how to estimate seat need each session, how to release seats among multiple course sections, and how to predict seat shortages and surpluses. Finally, procedures for creating reusable university final examination timetables are developed and compared. Course times, rather than individual courses, are used as the assignment elements because the demand for course times remains relatively constant despite changes in course schedules. Our heuristic procedures split the problem into two phases: a clustering phase--to minimize conflicts--and a sequencing phase--to distribute exams throughout finals week while minimizing the occurrence of consecutive exams. Results for all methods are compared using enrollment data from Clemson University

Ant-Balanced multiple traveling salesmen: ACO-BmTSP

A new algorithm based on the ant colony optimization (ACO) method for the multiple traveling salesman problem (mTSP) is presented and defined as ACO-BmTSP. This paper addresses the problem of solving the mTSP while considering several salesmen and keeping both the total travel cost at the minimum and the tours balanced. Eleven different problems with several variants were analyzed to validate the method. The 20 variants considered three to twenty salesmen regarding 11 to 783 cities. The results were compared with best-known solutions (BKSs) in the literature. Computational experiments showed that a total of eight final results were better than those of the BKSs, and the others were quite promising, showing that with few adaptations, it will be possible to obtain better results than those of the BKSs. Although the ACO metaheuristic does not guarantee that the best solution will be found, it is essential in problems with non-deterministic polynomial time complexity resolution or when used as an initial bound solution in an integer programming formulation. Computational experiments on a wide range of benchmark problems within an acceptable time limit showed that compared with four existing algorithms, the proposed algorithm presented better results for several problems than the other algorithms did.info:eu-repo/semantics/publishedVersio

Construção de Calendários de Exames

Tese de mestrado, Estatística e Investigação Operacional (Investigação Operacional) Universidade de Lisboa, Faculdade de Ciências, 2022Todos os anos, os estabelecimentos de ensino tem de criar calendários de exames. Uma grande maioria destes estabelecimentos ainda opta pela sua criação manual, resultando numa tarefa demorada, e muitas vezes, pouco eficiente. A Faculdade de Ciências da Universidade de Lisboa não é uma exceção, utilizando um calendário de exames já criado em anos anteriores, e adaptando manualmente ao ano letivo corrente. O problema da calendarização de exames pertence a classe de problemas NP-Difícil e, como tal, para obter soluções de boa qualidade em tempos aceitáveis são necessários algoritmos heurísticos. Um dos tipos de algoritmos utilizados para este género de problemas são os algoritmos de coloração de grafos. Nesta dissertação são propostos três algoritmos, testados em dez instâncias diferentes, para a criação de um calendário de exames direcionado para a época especial, uma época de exames na qual apenas dias antes da sua realização se sabe ao certo o número de alunos para cada exame. Os primeiros algoritmos utilizados neste trabalho são de coloração de grafos e coloração equitativa, para a criação do calendário de exames, e tem como objetivo uma distribuição mais uniforme, e justa para os alunos, dos exames pelos dias da época especial. O terceiro algoritmo desenvolvido é baseado em algoritmos de caminho ótimo, para a reorganização dos dias do calendário criado para a melhoria de uma restrição soft, a garantia de que todos os alunos tenham pelo menos um dia de intervalo entre os dois exames. Os resultados obtidos foram favoráveis para todas instâncias, sendo possível concluir que a utilização de algoritmos de coloração de grafos permite atingir resultados promissores, principalmente se complementados com outro tipo de algoritmos direcionados para a melhoria das restrições soft.Every year, educational institutions have to create exam schedules. A large majority of this insti tutions still create them manually, resulting in a time-consuming and often not very efficient task. The Faculdade de Ciencias da Universidade de Lisboa is no exception, using an already existing exam schedule already created in previous years, and adapting it manually to the current academic year. The exam scheduling problem belongs to the class of NP-hard problems and, as such, to obtain good quality soluti ons in acceptable times heuristic algorithms are required. Graph coloring algorithms are one of the type of algoritmhs used for this kind of problems. In this dissertation three algorithms are proposed, tested in ten different instances, for the creation of an exam schedule aimed to the special examination period, an exam period in which only a few days before its realization it is known for sure the number of stu dents enrolled in each exam. The two first algorithms used in this work are graph coloring and equitable coloring, for the creation of the exam schedule, with the goal of a more uniform, and fairer distribution for students, of exams over the days of the special period. The third algorithm developed is based on optimal path algorithms, for the reorganization of the days of the created schedule for the improvement of the soft constraint, the guarantee that all students have at least one day between the two exams. The obtained results were favorable for all instances, and it is possible to bring to a conclusion that the use of graph coloring algorithms allows the achievement of promising results, especially if complemented with other types of algorithms, aimed at improving the soft constraints

Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization

Nature-inspired algorithms have a great popularity in the current scientific community, being the focused scope of many research contributions in the literature year by year. The rationale behind the acquired momentum by this broad family of methods lies on their outstanding performance evinced in hundreds of research fields and problem instances. This book gravitates on the development of nature-inspired methods and their application to stochastic, dynamic and robust optimization. Topics covered by this book include the design and development of evolutionary algorithms, bio-inspired metaheuristics, or memetic methods, with empirical, innovative findings when used in different subfields of mathematical optimization, such as stochastic, dynamic, multimodal and robust optimization, as well as noisy optimization and dynamic and constraint satisfaction problems

Bio-inspired optimization in integrated river basin management

Water resources worldwide are facing severe challenges in terms of quality and quantity. It is essential to conserve, manage, and optimize water resources and their quality through integrated water resources management (IWRM). IWRM is an interdisciplinary field that works on multiple levels to maximize the socio-economic and ecological benefits of water resources. Since this is directly influenced by the river’s ecological health, the point of interest should start at the basin-level. The main objective of this study is to evaluate the application of bio-inspired optimization techniques in integrated river basin management (IRBM). This study demonstrates the application of versatile, flexible and yet simple metaheuristic bio-inspired algorithms in IRBM. In a novel approach, bio-inspired optimization algorithms Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to spatially distribute mitigation measures within a basin to reduce long-term annual mean total nitrogen (TN) concentration at the outlet of the basin. The Upper Fuhse river basin developed in the hydrological model, Hydrological Predictions for the Environment (HYPE), is used as a case study. ACO and PSO are coupled with the HYPE model to distribute a set of measures and compute the resulting TN reduction. The algorithms spatially distribute nine crop and subbasin-level mitigation measures under four categories. Both algorithms can successfully yield a discrete combination of measures to reduce long-term annual mean TN concentration. They achieved an 18.65% reduction, and their performance was on par with each other. This study has established the applicability of these bio-inspired optimization algorithms in successfully distributing the TN mitigation measures within the river basin. Stakeholder involvement is a crucial aspect of IRBM. It ensures that researchers and policymakers are aware of the ground reality through large amounts of information collected from the stakeholder. Including stakeholders in policy planning and decision-making legitimizes the decisions and eases their implementation. Therefore, a socio-hydrological framework is developed and tested in the Larqui river basin, Chile, based on a field survey to explore the conditions under which the farmers would implement or extend the width of vegetative filter strips (VFS) to prevent soil erosion. The framework consists of a behavioral, social model (extended Theory of Planned Behavior, TPB) and an agent-based model (developed in NetLogo) coupled with the results from the vegetative filter model (Vegetative Filter Strip Modeling System, VFSMOD-W). The results showed that the ABM corroborates with the survey results and the farmers are willing to extend the width of VFS as long as their utility stays positive. This framework can be used to develop tailor-made policies for river basins based on the conditions of the river basins and the stakeholders' requirements to motivate them to adopt sustainable practices. It is vital to assess whether the proposed management plans achieve the expected results for the river basin and if the stakeholders will accept and implement them. The assessment via simulation tools ensures effective implementation and realization of the target stipulated by the decision-makers. In this regard, this dissertation introduces the application of bio-inspired optimization techniques in the field of IRBM. The successful discrete combinatorial optimization in terms of the spatial distribution of mitigation measures by ACO and PSO and the novel socio-hydrological framework using ABM prove the forte and diverse applicability of bio-inspired optimization algorithms

A Polyhedral Study of Mixed 0-1 Set

We consider a variant of the well-known single node fixed charge network flow set with constant capacities. This set arises from the relaxation of more general mixed integer sets such as lot-sizing problems with multiple suppliers. We provide a complete polyhedral characterization of the convex hull of the given set

Mathematical Methods and Operation Research in Logistics, Project Planning, and Scheduling

In the last decade, the Industrial Revolution 4.0 brought flexible supply chains and flexible design projects to the forefront. Nevertheless, the recent pandemic, the accompanying economic problems, and the resulting supply problems have further increased the role of logistics and supply chains. Therefore, planning and scheduling procedures that can respond flexibly to changed circumstances have become more valuable both in logistics and projects. There are already several competing criteria of project and logistic process planning and scheduling that need to be reconciled. At the same time, the COVID-19 pandemic has shown that even more emphasis needs to be placed on taking potential risks into account. Flexibility and resilience are emphasized in all decision-making processes, including the scheduling of logistic processes, activities, and projects