Definição de escala de tripulação de transporte coletivo utilizando um algoritmo construtivo-evolutivo

A queda na demanda por transporte coletivo por ônibus observado no Brasil nas últimas décadas, aliada a outros fatores, tem causado o aumento da tarifa desse modal. A sociedade brasileira tem tratado essa pauta como prioridade e tem se posicionado contra esses aumentos firmemente, tendo em vista o seu impacto no cotidiano da população, especialmente sua parcela de renda mais baixa. Para atender às demandas de barateamento é necessário encontrar pontos onde a operação do modal possa ser aperfeiçoada, para que possa haver diminuição nos custos. Uma das lacunas observadas no transporte coletivo por ônibus na cidade de Porto Alegre diz respeito ao Problema de Programação de Tripulações (PPT), que é uma etapa do planejamento do sistema de transporte coletivo. Nesse âmbito o PPT consiste em gerar jornadas de trabalho a serem cumpridas por motoristas e cobradores, utilizando como dado de entrada a Tabela Horária de uma linha de ônibus. Essa jornada, a fim de minimizar custos de mão de obra, deve ter o menor tempo ocioso possível entre as viagens. Além disso, deve atender às particularidades operacionais e trabalhistas do setor, como compatibilizar terminais das viagens e atribuir intervalos para descanso. Na literatura da área, o PPT tem sido resolvido utilizando tanto métodos computacionais exatos quanto aproximativos. Na cidade de Porto Alegre algumas empresas resolvem o PPT sem nenhum tipo de sistematização, o que pode gerar perda de eficiência. Esse trabalho se propôs a encontrar uma solução para o PPT utilizando um Algoritmo Construtivo-Evolutivo, que se baseia no Algoritmo Construtivo AAO* e no Algoritmo Genético. Para isso, foi criada e executada uma rotina de programação na linguagem Python. Usando a Tabela Horária de uma linha de ônibus de Porto Alegre, foi encontrada uma solução que atende a todas as 301 viagens da linha e a todos os requisitos de operação e de legislação trabalhista

A column generation based heuristic for a bus driver rostering problem

The Bus Driver Rostering Problem (BDRP) aims at determining optimal work-schedules for the drivers of a bus company, covering all work duties, respecting the Labor Law and the regulation, while minimizing company costs. A new decomposition model for the BDRP was recently proposed and the problem was addressed by a metaheuristic combining column generation and an evolutionary algorithm. This paper proposes a new heuristic, which is integrated in the column generation, allowing for the generation of complete or partial rosters at each iteration, instead of generating single individual work-schedules. The new heuristic uses the dual solution of the restricted master problem to guide the order by which duties are assigned to drivers. The knowledge about the problem was used to propose a variation procedure which changes the order by which a new driver is selected for the assignment of a new duty. Sequential and random selection methods are proposed. The inclusion of the rotation process results in the generation of rosters with better distribution of work among drivers and also affects the column generation performance. Computational tests assess the proposed heuristic ability to generate good quality rosters and the impact of the distinct variation procedures is discussed.This work is supported by National Funding from FCT - Fundação para a Ciência e a Tecnologia, under the project: UID/MAT/04561/2013.info:eu-repo/semantics/publishedVersio

Bus driver rostering by hybrid methods based on column generation

Tese de doutoramento, Informática (Engenharia Informática), Universidade de Lisboa, Faculdade de Ciências, 2018Rostering problems arise in a diversity of areas where, according to the business and labor rules, distinct variants of the problem are obtained with different constraints and objectives considered. The diversity of existing rostering problems, allied with their complexity, justifies the activity of the research community addressing them. The current research on rostering problems is mainly devoted to achieving near-optimal solutions since, most of the times, the time needed to obtain optimal solutions is very high. In this thesis, a Bus Driver Rostering Problem is addressed, to which an integer programming model is adapted from the literature, and a new decomposition model with three distinct subproblems representations is proposed. The main objective of this research is to develop and evaluate a new approach to obtain solutions to the problem in study. The new approach follows the concept of search based on column generation, which consists in using the column generation method to solve problems represented by decomposition models and, after, applying metaheuristics to search for the best combination of subproblem solutions that, when combined, result in a feasible integer solution to the complete problem. Besides the new decomposition models proposed for the Bus Driver Rostering Problem, this thesis proposes the extension of the concept of search by column generation to allow using population-based metaheuristics and presents the implementation of the first metaheuristic using populations, based on the extension, which is an evolutionary algorithm. There are two additional contributions of this thesis. The first is an heuristic allowing to obtain solutions for the subproblems in an individual or aggregated way and the second is a repair operator which can be used by the metaheuristics to repair infeasible solutions and, eventually, generate missing subproblem solutions needed. The thesis includes the description and results from an extensive set of computational tests. Multiple configurations of the column generation with three decomposition models are tested to assess the best configuration to use in the generation of the search space for the metaheuristic. Additional tests compare distinct single-solution metaheuristics and our basic evolutionary algorithm in the search for integer solutions in the search space obtained by the column generation. A final set of tests compares the results of our final algorithm (with the best column generation configuration and the evolutionary algorithm using the repair operator) and the solutions obtained by solving the problem represented by the integer programming model with a commercial solver.Programa de Apoio à Formação Avançada de Docentes do Ensino Superior Politécnico (PROTEC), SFRH/PROTEC/67405/201