A new history-guided multi-objective evolutionary algorithm based on decomposition for batching scheduling

Jia, Zhaohong; Le-yang, Gao,; Zhang, Xingyi

doi:10.1016/j.eswa.2019.112920

Cited by 14 publications

(4 citation statements)

References 40 publications

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“…Este trabalho investiga AGMOs na soluc ¸ão de um POMR aplicado a SB de produtos farmacêuticos com demandas estocásticas de produtos. Um AGMO baseado no algoritmo NSGA3 a um escalonamento com três objetivos é proposto integrado à uma busca local competitiva que ajusta as posic ¸ões das tarefas para acelerar a convergência [Jia et al 2020]. O método incorpora uma estratégia de reinserc ¸ão baseada no elitismo, retendo metade da populac ¸ão e gerando as soluc ¸ões restantes com base em informac ¸ões históricas e características extraídas das melhores soluc ¸ões.…”

Section: Revisão Bibliográficaunclassified

Aprimoramento de um modelo evolutivo multiobjetivo aplicado ao problema de sequenciamento de bateladas na manufatura de produtos farmacêuticos

Kohara,

Oliveira,

Martins

2023

Anais Do XX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2023)

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A otimização do Sequenciamento de Bateladas (SB) em problemas reais de manufatura farmacêutica é desafiadora devido aos múltiplos objetivos conflitantes, restrições e demanda incerta. Um dos desafios é a baixa convergência de soluções viáveis. Algoritmos Genéticos Multiobjetivos (AGMO) podem ser utilizados para lidar com esse desafio. Propomos aprimoramentos nos operadores genéticos de mutação e cruzamento, bem como uma nova estratégia de inicialização da população, visando aprimorar a qualidade das soluções em relação a métricas como Hipervolume (Hv), Distância Geracional Invertida mais (IGD+), Taxa de Erro (E), Cobertura entre dois conjuntos de soluções não-dominadas (CS) e Número de Soluções válidas (NSV). Os resultados demonstram que as melhorias propostas reduzem a mediana do IGD+ em 76,6% e o E em 12,1%, enquanto aumentam o NSV em 25,0%.

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Section: Revisão Bibliográficaunclassified

Aprimoramento de um modelo evolutivo multiobjetivo aplicado ao problema de sequenciamento de bateladas na manufatura de produtos farmacêuticos

Kohara,

Oliveira,

Martins

2023

Anais Do XX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2023)

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“…The high flexibility and complexity of IPPS need a broad archive size for pheromone trail, which needs lots of computation time to test them. The author had proposed an approach that increases the pheromone by a constant incremental amount without allowing evaporation, as in equation (22). Author suggested that this updating feature saves a lot of computational energy because it only updates a few edges that belong to one solution path rather than the entire pheromone trail.…”

Section: Ant Systemmentioning

confidence: 99%

A Review of Implementing Ant System Algorithms on Scheduling Problems

Kashef

Elshaer

2021

The Egyptian International Journal of Engineering Sciences and

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The ant system (AS) and scheduling problem are well-known concepts in literature. Ant algorithms have been known to be an effective tool for solving combinatorial optimization problems. Elitist AS (EAS), rank-based AS (RAS), ant colony system (ACS), and max-min AS (MMAS) are the variants of the AS algorithm; they are triggered by the different ways of updating the pheromone trail τ, computing the visibility η, and/or other parameters in the basic AS model. The main contribution of this article is twofold. First, the basic AS and its controlled parameters are presented, the key variants of the ant algorithms are explained, and major changes of each variant from the basic model are tracked. Second, sixty papers are collected between 2015 and 2020 based on a search strategy for tracking the implementation of different AS variants in solving scheduling problems. Numerous findings based on a statistical analysis of the collected papers are reported and discussed. This study will allow the researcher to understand the essence of the ant algorithm, recognize the fundamental differences in its five systems, and determine how each of them can be implemented. Tracking a sample of articles that apply an ant algorithm for a specific case study gives researchers new ideas on how to adjust the original model to fit their problem.

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“…The elite replacement strategy (Wang and Liu, 2019;Jia et al, 2020) is implemented to preserve the elite solution with the highest fitness value and replace the worst solution in the next generation. This strategy can prevent the elite solution from being lost and accelerate the convergence of the EABC algorithm.…”

Section: ) Elite Replacement Strategymentioning

confidence: 99%

A New Job Shop Scheduling Method for Remanufacturing Systems Using Extended Artificial Bee Colony Algorithm

et al. 2021

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With the emergence of the remanufacturing industry, scheduling problems related to remanufacturing systems have drawn considerable interest from scholars. There are usually two execution modes of remanufacturing for reprocessing components, i.e., replacement mode and repair mode. However, few studies have focused on the job shop scheduling problem that considers the trade-off between the two execution modes. Thus, a new job shop scheduling method with job families (JSS-JF) is proposed. It decomposes the scheduling problem into three sub-problems: (1) execution mode selection; (2) replacement job assignment and sequencing; and (3) repair machine assignment and operation sequencing. To solve the proposed JSS-JF model, an extended artificial bee colony algorithm with a new three-dimensional encoding scheme is presented to find a near-optimal solution of the proposed JSS-JF model. The crossover and mutation operators, local search, and elite replacement strategy are also integrated in the proposed algorithm. The experiments are conducted for verifying the practicality and effectiveness of the presented algorithm by comparing with six baseline algorithms.INDEX TERMS Job shop scheduling, job families, remanufacturing, execution mode, extended artificial bee colony algorithm.

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A new history-guided multi-objective evolutionary algorithm based on decomposition for batching scheduling

Cited by 14 publications

References 40 publications

Aprimoramento de um modelo evolutivo multiobjetivo aplicado ao problema de sequenciamento de bateladas na manufatura de produtos farmacêuticos

Aprimoramento de um modelo evolutivo multiobjetivo aplicado ao problema de sequenciamento de bateladas na manufatura de produtos farmacêuticos

A Review of Implementing Ant System Algorithms on Scheduling Problems

A New Job Shop Scheduling Method for Remanufacturing Systems Using Extended Artificial Bee Colony Algorithm

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