Solving bi-objective flow shop problem with hybrid path relinking algorithm

Zeng, Rong-Qiang; Basseur, Matthieu; Hao, Jin‐Kao

doi:10.1016/j.asoc.2013.05.018

Cited by 15 publications

(4 citation statements)

References 17 publications

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“…Thus, in the basic version of path relinking, two solutions are chosen at each time and the solution elements of one of these solutions are changed through a path that finalizes when the starting solution changes to the target solution. The usage of path relinking has been mostly associated with GRASP (Festa and Resende, , , ), but there are also other hybridizations in the literature focusing on more specific problems (Zeng et al., ).…”

Section: The Hybrid Path‐relinking Methodsmentioning

confidence: 99%

A hybrid path‐relinking method for solving two‐stage stochastic integer problems

Amorim

Costa

Almada-Lobo

2014

Int Trans Operational Res

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Path relinking has been used for solving deterministic problems by exploring the neighborhood of elite solutions in an intelligent way. We present an algorithm that combines a mixed-integer linear solver with a truncated path-relinking method in order to solve two-stage stochastic integer problems with complete recourse and first-stage integer variables. This method takes advantage of a possible scenario-based decomposition in an innovative way. Therefore, path relinking is used to combine optimized solutions from different scenarios in order to pursue good stochastic solutions. To assess the computational performance of this method, we use the stochastic lot sizing and scheduling problem dealing with perishable products. In this problem, first-stage decision variables are linked to production sequences and production quantities. After the uncertain demand is unveiled, the second-stage variables decide on the inventory usage. Computational results show a clear advantage of the proposed method when compared to a state-of-the-art mixed-integer linear solver.

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Section: The Hybrid Path‐relinking Methodsmentioning

confidence: 99%

A hybrid path‐relinking method for solving two‐stage stochastic integer problems

Amorim

Costa

Almada-Lobo

2014

Int Trans Operational Res

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“…According to the value, the population is subjected to adaptive perturbation at the probability = − * , where K is a control parameter. If ≤ , the local search strategy of path relinking [27] will be introduced to disperse the current sequence and avoid the local optimum trap. The optimal sequence of the current population is selected as the target sequence, and the sequence to be promoted is taken as the original sequence.…”

Section: End If End If End If End Formentioning

confidence: 99%

An Intelligent Optimization Algorithm for Blocking Flow-Shop Scheduling Based on Differential Evolution

Xu¹,

Xie²,

Yuan³

et al. 2019

Int. j. simul. model.

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Owing to its large scale, the blocking flow-shop scheduling problem (BFSP) cannot be solved effectively by traditional optimization methods. To solve the problem, this paper develops a novel intelligent optimization algorithm based on differential evolution (DE) for the BFSP with a single objective: minimizing the total flow time (TFT). On the one hand, a new heuristic method was introduced to balance the quality and diversity of the initial population. On the other hand, a new operator was adopted to update the acceleration, velocity and position of each particle. In this way, the population will not converge prematurely to local optimums, and the local and global search abilities are perfectly balanced. Simulation on standard test set proves that our algorithm outperformed most commonly used methods in solving the BFSP.

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“…Há diversas abordagens na literatura para solucionar o PFSP multiobjetivo. Algumas são baseadas em Pareto com algoritmo genético [Basseur et al 2002], outras usam busca local [Minella et al 2011] e algumas utilizam Path Relinking [Zeng et al 2013]. Como o foco deste trabalhoé a heurística baseada em NEH, alguns trabalhos envolvendo uso de NEH para o PFSP merecem destaque.…”

Section: Introductionunclassified

MOEA/D com Busca Local para o Flow Shop Multiobjetivo

Antunes

Almeida

Lüders

et al. 2019

Anais Do XVI Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2019)

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Este artigo aborda o Flow Shop de Permutação, um problema de sequenciamento presente em muitos mecanismos de gerenciamento de processos de produção industrial. A abordagem multiobjetivo considerada neste trabalho envolve a minimização do tempo máximo para completar um trabalho (makespan) e do tempo total de atraso (total tardiness). Para isso é utilizada uma plataforma multiobjetivo denominada MOEA/D-DRA (do inglês Multi-objective Evolutionary Algorithm based on Decomposition with Dynamic Resource Allocation). O foco do trabalho reside na utilização de um mecanismo muito conhecido por seus bons resultados nas versões mono-objetivo do problema. Este mecanismo, denominado NEH, é adaptado para ser utilizado na busca local incluída no MOEA/D-DRA, aplicado na solução de 11 instâncias do Flow Shop de permutação com tamanhos variando de 20 a 200 tarefas e 5 a 20 máquinas. A abordagem proposta é comparada com o MOEA/D-DRA utilizando NEH apenas na inicialização da população. Os resultados mostram que, em apenas 2 instâncias, a versão do MOEA/D-DRA sem busca local supera a abordagem proposta.

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Solving bi-objective flow shop problem with hybrid path relinking algorithm

Cited by 15 publications

References 17 publications

A hybrid path‐relinking method for solving two‐stage stochastic integer problems

A hybrid path‐relinking method for solving two‐stage stochastic integer problems

An Intelligent Optimization Algorithm for Blocking Flow-Shop Scheduling Based on Differential Evolution

MOEA/D com Busca Local para o Flow Shop Multiobjetivo

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