A study of different metaheuristics to solve the urban transit crew scheduling problem

Silva, Gustavo Peixoto; Reis, Allexandre Fortes da Silva

doi:10.1590/2238-1031.jtl.v8n4a9

Cited by 3 publications

(4 citation statements)

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“…However, according to the number and the nature of the resource constraints, generating a pool of column during a preprocessing may sometimes be more efficient [11]. The resulting Branch-and-Price algorithm is designed to be an exact model which, nevertheless, may fail to optimally solve very large problems (with more than thousands tasks) [3,5,20]. To bypass this issue, a common approach consists in truncating the search tree [8], the lower bound quality is ensured then to be the nearest possible from optimal solutions.…”

Section: Related Work and Operational Contextmentioning

confidence: 99%

“…To bypass this issue, a common approach consists in truncating the search tree [8], the lower bound quality is ensured then to be the nearest possible from optimal solutions. Even if many meta-heuristic algorithms have been proposed for the UTCSP [20,7,12,19], the most efficient industrial softwares such as Austrics, 1 Hastus, 2 GoalDriver 3 or LP-EasyDriver 4 are based on a truncated version of the exact Branch-and-Price algorithm.…”

Section: Related Work and Operational Contextmentioning

confidence: 99%

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Using Constraint Programming for the Urban Transit Crew Rescheduling Problem

Lorca

Prud’Homme

Questel

et al. 2016

Lecture Notes in Computer Science

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International audienceScheduling urban and trans-urban transportation is an important issue for industrial societies. The Urban Transit Crew Scheduling Problem is one of the most important optimization problem related to this issue. It mainly relies on scheduling bus drivers' workday respecting both collective agreements and the bus schedule needs. If this problem has been intensively studied from a tactical point of view, its operational aspect has been neglected while the problem becomes more and more complex and more and more prone to disruptions. In this way, this paper presents how the constraint programming technologies are able to recover the tactical plans at the operational level in order to efficiently help in answering regulation needs after disruptions. 1 Context and opportunities Scheduling urban and trans-urban transportation is an important issue for industrial societies. Several aspects are considered by territorial collectivities and transportation operators: human and material resource for, environmental and social constraint enforcement , user need requirements. Basically, there exists two central problems: transit scheduling (vehicles-buses, trains, tramways-planning on routes) and driver duty planning (assigning crew to those routes). These problems become more and more complex (regulation, network expansion) and more and more prone to disruptions (city events, accidents, resource failures) in the operational phase. This is the context in which the Urban Transit Crew Scheduling Problem (UTCSP) has been introduced [5]. In our proposal, we have to schedule bus drivers' workdays according to several constraints mainly related to: (1) collective agreements, e.g. breaking rules (basically, how long a bus driver can work before a break) ; (2) the bus schedule itself, e.g. chaining rules (geographic position, schedule compatibility between two tasks, etc.). From a tactical point of view, the UTCSP has been thoroughly studied both at the academic and industrial levels. Primarily, the technologies derived from mathematical programming (from integer linear programming to column generation and dynamic programming) dominate the literature and offer satisfying results. The problem is mainly solved using a set covering approach that represents bus schedule as a set of tasks, linked together by chaining rules and respecting the breaking rules. From an operational point of view, these technologies become useless due to their time consumption. Such a pitfall leads the operators to manually repair the tactical solutions after

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Section: Related Work and Operational Contextmentioning

confidence: 99%

Section: Related Work and Operational Contextmentioning

confidence: 99%

Using Constraint Programming for the Urban Transit Crew Rescheduling Problem

Lorca

Prud’Homme

Questel

et al. 2016

Lecture Notes in Computer Science

View full text Add to dashboard Cite

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“…Este problema consiste em determinar o número mínimo de tripulações e especificar as suas viagens de tal forma a cobrir todas as viagens da frota em operação com o menor custo possível (Silva e Reis, 2014). Nesta etapa são definidas as jornadas de cada tripulação, portanto, uma tripulação está associada a uma jornada diária de trabalho e vice-versa.…”

Section: Introductionunclassified

“…A GLS foi testada com um conjunto de problemas reais de uma empresa de transporte público de médio porte e seus resultados foram comparados com resultados da literatura que utilizaram as metaheurísticas Variable Neighborhood Search e Iterated Local Search (Silva e Reis, 2014).…”

Section: Introductionunclassified

O uso da metaheurística Guided Local Search para resolver o problema de escala de motoristas de ônibus urbano

Silva¹,

Silva

2015

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Resumo: Neste artigo é aplicada a metaheurística Guided Local Search (GLS) para resolver o Problema de Programação de Tripulações de Ônibus Urbano (PPT). O PPT consiste em encontrar um conjunto de jornadas a serem designadas aos motoristas que realizarão a operação diária com o menor custo. A GLS tem como princípio penalizar características indesejáveis presentes na solução corrente, com o objetivo de escapar de soluções ótimas locais. Como heurística de busca local, foi utilizada a heurística Variable Neighborhood Descent, que explora diferentes estruturas de vizinhança para encontrar um mínimo local. De acordo com pesquisa realizada pelos autores, esta abordagem é inédita para resolver o PPT. A implementação proposta foi testada com dados de problemas reais de uma empresa que opera em uma região metropolitana de Belo Horizonte. Os resultados obtidos são similares àqueles presentes na literatura, havendo possibilidades de melhorias, visto que a GLS pode ser explorada em diferentes aspectos.Palavras-chave: Guided Local Search, Programação de Tripulações do Sistema de Transporte. Metaheurística. Abstract:In this paper the metaheuristic Guided Local Search (GLS) is applied to solve the Crew Scheduling Problem (CSP) for public mass transport system. The CSP consists on finding a set of duties to be assigned to drivers in order that the daily service requirement be met at minimum cost. The GLS metaheuristic follows the basic principle of penalizing undesired characteristics that are present in the current solution and to append this penalization in the objective function with the aim of guiding the local search away from local minimum. As local search heuristic, it was employed Variable Neighborhood Descent technique, which explores different neighborhood structures to find a local optimum. According to a research conducted by the authors, this is a novel approach to solve the CSP. The proposed implementation was tested with data from real problems of a bus company operating in a metropolitan region of Belo Horizonte. The results are comparable with those reported in the literature, being subject to improvement, once the GLS can be exploited in different ways.Keywords: Guided Local Search, Crew Scheduling Problem in mass transit, Metaheuristic. INTRODUÇÃOEm muitas cidades o ônibus é o principal, senão o único meio de transporte público de passageiros. Portanto, é necessário que as empresas do ramo tenham suas atividades muito bem planejadas para atender à demanda dos usuários do sistema de maneira econômica. Para tanto, devem ser utilizadas ferramentas computacionais que auxiliam o processo de decisão. Neste contexto é que se inserem os mé-todos de otimização, os quais podem levar à redução dos custos destas empresas. Entre os custos de maiores pesos se destacam os custos operacionais com os veículos e os seus motoristas e cobradores (Bouzada, 2003) Geralmente, o planejamento de um sistema de transporte público é decomposto em etapas a fim de tornar o problema tratável do ponto de vista computacional. A etapa inicia...

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Combining simulated annealing with local search heuristic for MAX-SAT

Bouhmala

2018

J Heuristics

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A study of different metaheuristics to solve the urban transit crew scheduling problem

Cited by 3 publications

References 15 publications

Using Constraint Programming for the Urban Transit Crew Rescheduling Problem

Using Constraint Programming for the Urban Transit Crew Rescheduling Problem

O uso da metaheurística Guided Local Search para resolver o problema de escala de motoristas de ônibus urbano

Combining simulated annealing with local search heuristic for MAX-SAT

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