Note: A local-search heuristic for large set-covering problems

Jacobs, Larry W.; Brusco, Michael J.

doi:10.1002/1520-6750(199510)42:7<1129::aid-nav3220420711>3.0.co;2-m

Cited by 133 publications

(57 citation statements)

References 33 publications

(2 reference statements)

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“…The ILS algorithm has also been extended to an iterated greedy (IG) algorithm [77]. The essential idea in IG, and also a few other algorithms [45,80], is to perturb the current solution by a destruction/construction mechanism. In the solution destruction phase, a complete solution is reduced to a partial solution s p by removing solu-tion components; in the following construction phase, a complete solution is reconstructed starting from s p by a greedy construction heuristic.…”

Section: Ils For Other Problemsmentioning

confidence: 99%

Iterated Local Search: Framework and Applications

Lourenço

Martin

Stützle

2010

International Series in Operations Research &Amp; Management Science

372

262

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Section: Ils For Other Problemsmentioning

confidence: 99%

Iterated Local Search: Framework and Applications

Lourenço

Martin

Stützle

2010

International Series in Operations Research &Amp; Management Science

372

262

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“…The standard iterated greedy (IG) algorithm [30] was intended for single-objective combinatorial optimization problems. The IG algorithm consists of two iterative phases, i.e., destruction and construction.…”

Section: The Basic Ig Algorithmmentioning

confidence: 99%

Sustainable Scheduling of Cloth Production Processes by Multi-Objective Genetic Algorithm with Tabu-Enhanced Local Search

Zhang

2017

Sustainability

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Abstract:The dyeing of textile materials is the most critical process in cloth production because of the strict technological requirements. In addition to the technical aspect, there have been increasing concerns over how to minimize the negative environmental impact of the dyeing industry. The emissions of pollutants are mainly caused by frequent cleaning operations which are necessary for initializing the dyeing equipment, as well as idled production capacity which leads to discharge of unconsumed chemicals. Motivated by these facts, we propose a methodology to reduce the pollutant emissions by means of systematic production scheduling. Firstly, we build a three-objective scheduling model that incorporates both the traditional tardiness objective and the environmentally-related objectives. A mixed-integer programming formulation is also provided to accurately define the problem. Then, we present a novel solution method for the sustainable scheduling problem, namely, a multi-objective genetic algorithm with tabu-enhanced iterated greedy local search strategy (MOGA-TIG). Finally, we conduct extensive computational experiments to investigate the actual performance of the MOGA-TIG. Based on a fair comparison with two state-of-the-art multi-objective optimizers, it is concluded that the MOGA-TIG is able to achieve satisfactory solution quality within tight computational time budget for the studied scheduling problem.

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“…Assim, o PCC construído é submetido a um algoritmo heurístico construtivo com o objetivo de obter uma estimativa do custo da solução rapidamente. Nesse caso foi utilizado o algoritmo de [23]. Dessa forma, f(S) é a soma dos custos das colunas que participam da solução construída para o PCC.…”

Section: 12unclassified

Pré-processamento do problema de cobertura de conjunto aplicado ao escalonamento de condutores

Constantino

Castro

Araújo

et al. 2016

RBCA

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Resumo: o problema de escalonamento de condutores (PEC) consiste em distribuir de maneira eficiente o quadro de viagens de uma empresa de transporte coletivo entre os condutores disponíveis. Esse problema é comumente modelado como um problema de cobertura de conjunto -PCC (set covering problem). Nesse caso, um bom resultado para o PEC depende de uma boa construção e resolução do PCC. Porém, a maior parte da bibliografia relacionada trata apenas da resolução das instâncias do PCC, sem avaliar a influência dos procedimentos com sua construção, aqui denominado de pré-processamento. Este trabalho propõe-se e investigar metodologias heurísticas baseadas em Simulated Annealing para o pré-processamento de instâncias do PCC, cujas características possibilitem os algoritmos de resolução obterem melhores resultados para o PEC. Nos testes efetuados, conseguiu-se uma redução de até 8% no custo das soluções apresentadas, em comparação com a resolução de instâncias geradas por um método clássico de geração do PCC.Palavras-chave: Escalonamento de condutores. Heurística. Simulated Annealing. Abstract: the crew scheduling problem consists on efficiently distribute trips of an urban transport company to available drivers. Generally it is modeled as a set covering problem (SCP). In this case, good results for the crew scheduling problem depend on good generation and resolution to the SCP. Most of works in this area proposes only SCP resolution methods, but does not include the SCP generation in their matters. This paper investigates and proposes heuristic methodologies based on Simulated IntroduçãoA resolução do problema de planejamento de escala de veículos e condutores consiste em determinar um plano de distribuição de viagens sobre um conjunto de veículos e motoristas envolvidos na operação, ao longo de um período de planejamento. Como resultado desse processo, para cada veículo é alocado um conjunto de viagens a ser realizado e cada viagem é alocada a um condutor.

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Note: A local-search heuristic for large set-covering problems

Cited by 133 publications

References 33 publications

Iterated Local Search: Framework and Applications

Iterated Local Search: Framework and Applications

Sustainable Scheduling of Cloth Production Processes by Multi-Objective Genetic Algorithm with Tabu-Enhanced Local Search

Pré-processamento do problema de cobertura de conjunto aplicado ao escalonamento de condutores

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