A multi-objective genetic algorithm for assembly line resource assignment and balancing problem of type 2 (ALRABP-2)

Triki, Hager; Mellouli, Ahmed; Masmoudi, Faouzi

doi:10.1007/s10845-014-0984-6

Cited by 40 publications

(15 citation statements)

References 33 publications

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“…In this example, a feasible sequence, seq [1,2,6,8,3,4,5,7,9] are considered in distributing task to workstation. In evaluating the optimisation objective , the assembly tasks is assigned to workstations with the ct max constraint where the total processing time for tasks assigned to the workstation must not exceed the cycle time, set as C=10.…”

Section: Numerical Examplementioning

confidence: 99%

“…As an example, a valid feasible assembly sequences (seq), seq 1 [1,2,6,8,3,4,7,5,9] as in Figure 3 and two other sequence seq 2 [1,5,3,4,7,2,6,8,9] and seq 3 [1,3,4,7,2,6,8,5,9] are considered. For given, cycle time maximum for this assembly line is to be 10.…”

Section: Problem Evaluationmentioning

confidence: 99%

“…O. Mutlu in 2013, discussed on worker assignment of assembly line problem type-II and using iterative genetic algorithm (IGA) to assign worker to workstation and at the same time optimize the assembly line [7]. Machine assignment for SALBP-2 have been discussed by H. Triki (2014) where Hybrid Multi Objective Genetic Algorithm (HMOGA) is used to obtained minimum cycle time and cost [8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modelling of Simple Assembly Line Balancing Problem Type 1 (SALBP-1) with Machine and Worker Constraints

Kamarudin

Rashid

2018

J. Phys.: Conf. Ser.

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Abstract-This paper presents a mathematical model for Simple Assembly Line Problem Type 1 (SALBP-1) with resource constraints; machine and worker. The existing model of SALBP-1 assumes that all the workstations have similar capability, while in reality the workstation has different capability because of limitation in term of machines and worker skills. The proposed model is aimed to mathematically represent the SALBP-1 with resource constraints. Besides that, three objective functions which to minimize number of workstation, machine used and number of worker are also presented. The machine considered to have different types of machine needed to produce a product in an assembly line while worker are considered to have different abilities and skills. The model is then illustrated and validated using some examples. The proposed model for SALBP-1 with machine and worker constraints is able to minimize the resources in assembly. Therefore, the assembly cost can be reduced.

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Section: Numerical Examplementioning

confidence: 99%

Section: Problem Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modelling of Simple Assembly Line Balancing Problem Type 1 (SALBP-1) with Machine and Worker Constraints

Kamarudin

Rashid

2018

J. Phys.: Conf. Ser.

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“…In general, the scientific community has proposed numerous studies to adaptation to the incertitude of the market and the fast demand variations aiming to optimize and integrate different manufacturing problems, inventory and SCM so that in an agile and flexible way, it minimizes costs and attend efficiently the client's orders. (consult Bandyopadhyay and Bhattacharya 2014;Baud-Lavigne et al 2014;Diabat et al 2015;Ghiami et al 2013;Hiremath et al 2013;Haq and Boddu 2014;Kumar et al 2014;Mousavi et al 2013Mousavi et al , 2014Park and Kyung 2014;Sadeghi et al 2014;Triki et al 2014;Xiang et al 2014;Yao and Huang 2014;İnkaya and Akansel 15).…”

Section: Contextualization and Rationalementioning

confidence: 99%

A Genetic Algorithm applied to pick sequencing for billing

2015

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This article addresses the use of Holland's Genetic Algorithms (GAs) (Holland in Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor, MI, 1975) in solving an optimization problem not exploited yet by literature, which we have named Optimal Billing Sequencing (OBS). The objective of the GA proposed is to automate pick sequencing, which addresses the process of allocating the stock available for sale to the purchase orders in a portfolio, so that the maximization of the billing is the optimal result for the OBS. A modelling and computational simulation methodology has been employed. Such methodology is designed to enable the GA to meet the boundary conditions established by predefined decision restrictions and parameters. We have reached the conclusion, by means of experimental tests, that the GA developed satisfactorily solves the problem studied. In addition to a low computational overhead, the GA reduces operating costs and speeds picking decision-making processes and billing processes.

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“…The basic profile is divided into two categories, one is the modeling study. For example, considering MMAL balancing in an uncertain environment, Rabbani et al [2] put the labor costs and overtime costs into the model; Triki et al [3] studied the compatibility between task elements; Consider the changes in product demand, assembly technology and technological progress, equipment updates and other dynamic changes, Wu et al [4] established a MMAL evolution balancing model. The second category focuses on the model solution.…”

Section: Introductionmentioning

confidence: 99%

An improved NSGA - II algorithm for mixed model assembly line balancing

Yongming

Luo

et al. 2018

AIP Conference Proceedings

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Abstract.Aiming at the problems of assembly line balancing and path optimization for material vehicles in mixed model manufacturing system, a multi-objective mixed model assembly line (MMAL), which is based on optimization objectives, influencing factors and constraints, is established. According to the specific situation, an improved NSGA-II algorithm based on ecological evolution strategy is designed. An environment self-detecting operator, which is used to detect whether the environment changes, is adopted in the algorithm. Finally, the effectiveness of proposed model and algorithm is verified by examples in a concrete mixing system.

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A multi-objective genetic algorithm for assembly line resource assignment and balancing problem of type 2 (ALRABP-2)

Cited by 40 publications

References 33 publications

Modelling of Simple Assembly Line Balancing Problem Type 1 (SALBP-1) with Machine and Worker Constraints

Modelling of Simple Assembly Line Balancing Problem Type 1 (SALBP-1) with Machine and Worker Constraints

A Genetic Algorithm applied to pick sequencing for billing

An improved NSGA - II algorithm for mixed model assembly line balancing

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