Two metaheuristic methods for the common cycle economic lot sizing and scheduling in flexible flow shops with limited intermediate buffers: The finite horizon case

Akrami, B.; Karimi, Behrooz; Hosseini, Seyed Mahdi

doi:10.1016/j.amc.2006.05.106

Cited by 31 publications

(13 citation statements)

References 16 publications

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“…They proposed three bi-objective metaheuristic algorithms based on the simulated annealing algorithm. Akrami, Karimi and Moattar Hosseini [12] proposed GA and Tabu search (TS) for joint economic lot sizing and scheduling problems in the FFS environment with respect to limited intermediate buffers.…”

Section: Literature Reviews 21 Metaheuristic Algorithms For Flexiblementioning

confidence: 99%

Flexible flow shop scheduling problem to minimize makespan with renewable resources

2019

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This paper deals with a flexible flow shop (FFS) scheduling problem with unrelated parallel machines and renewable resource shared among the stages. The FFS scheduling problem is one of the most common manufacturing environment in which there is more than a machine in at least one production stage. In such a system, to decrease the processing times, additional renewable resources are assigned to the jobs or machines, and it can lead to decrease the total completion time. For this purpose, a mixed integer linear programming (MILP) model is proposed to minimize the maximum completion time (makespan) in an FFS environment. The proposed model is computationally intractable. Therefore, a particle swarm optimization (PSO) algorithm as well as a hybrid PSO and simulated annealing (SA) algorithm named SA-PSO, are developed to solve the model. Through numerical experiments on randomly generated test problems, the authors demonstrate that the hybrid SA-PSO algorithm outperforms the PSO, especially for large size test problems.

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Section: Literature Reviews 21 Metaheuristic Algorithms For Flexiblementioning

confidence: 99%

Flexible flow shop scheduling problem to minimize makespan with renewable resources

2019

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“…Para la generación de la población inicial [8,15,[24][25]27] utilizan una generación aleatoria, sin incluir algún conocimiento previo del conjunto de cromosomas [28]. Mientras en [14,16,[29][30] utilizan heurísticas para la generación de la población inicial, introduciendo un conocimiento previo de un conjunto probable de buenas soluciones (cromosomas), el cual proporciona una forma de aceleración en la obtención de una buena solución. En este trabajo se utiliza la generación de la población inicial como vecindades de las soluciones generadas por las heurísticas EDD y Slack debido al buen desempeño observado respecto del algoritmo genético básico estudiado en [15].…”

Section: Algoritmo Genéticounclassified

Algoritmo genético mejorado para la minimización de la tardanza total en un flowshop flexible con tiempos de preparación dependientes de la secuencia

Hornig

Guarachi

2015

Ingeniare. Rev. chil. ing.

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Este trabajo considera un entorno de producción de flowshop flexible con tiempos de preparación anticipatorios dependientes de la secuencia. Se presenta un algoritmo genético mejorado para minimizar la tardanza total. La generación de la población inicial se realiza utilizando vecindades de las heurísticas EDD (en inglés Earliest Due Date) y Slack, considerando además una búsqueda en vecindad IP para mejorar el rendimiento del algoritmo genético propuesto. Los resultados muestran que los algoritmos genéticos con población inicial generada como vecindad de EDD (AG_EDD) y Slack (AG_Slack) mejoran el rendimiento del algoritmo genético básico. El algoritmo AG_EDD muestra un mejor desempeño, característica que se mantiene al incorporar una búsqueda en vecindad IP.

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“…In order to distinguish it from the pure FFS problem that can be solved by MILP, recent researches have designed different manufacturing environments with specific characters that may need unique solutions, for example, FFS problem with sequencedependent setup times [6,7], with common cycle multiproduct lot sizing [8], with a bottleneck stage [9], or with processor blocking [10]. But no matter how characteristic these research environments may be, there is one thing in common: that is, they all make the scheduling plan in a static way.…”

Section: Static Scheduling and Dynamic Schedulingmentioning

confidence: 99%

Distributed‐Intelligence Approaches for Weighted Just‐in‐Time Production

Weng

Fujimura

2010

IEEJ Transactions Elec Engng

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This paper considers the dynamic flexible-flow shop scheduling problem in which the manufacturing environment consists of multiple stages with multiple machines at each stage. All jobs must go through all stages in sequence in order to become a product, and the processing time for each product on each machine is different. There exists a delivery time between machines of neighboring stages, and the release time of each job is unknown, which means that a new job may be released into the system at any time. The objective of this work is to minimize the total earliness and tardiness penalties of all jobs, or to achieve just-in-time production. Previous researches did much on the static scheduling of such problem with different objectives, whereas little effort has been made on dynamic scheduling for such problem, which is more difficult than the static problem but becomes more and more important under the increasingly competitive manufacturing industry. Therefore, to address such need, efforts are made on dynamic scheduling and several distributed-intelligence approaches are proposed in this paper. Featured by concurrent computing, the distributed-intelligence approaches have been tested on two different manufacturing environments, whose result tells that the proposed approaches deliver competitive performance for the targeted problem. 

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Two metaheuristic methods for the common cycle economic lot sizing and scheduling in flexible flow shops with limited intermediate buffers: The finite horizon case

Cited by 31 publications

References 16 publications

Flexible flow shop scheduling problem to minimize makespan with renewable resources

Flexible flow shop scheduling problem to minimize makespan with renewable resources

Algoritmo genético mejorado para la minimización de la tardanza total en un flowshop flexible con tiempos de preparación dependientes de la secuencia

Distributed‐Intelligence Approaches for Weighted Just‐in‐Time Production

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