A multi-objective mathematical model for cellular manufacturing systems design with probabilistic demand and machine reliability analysis

Aghajani, Aydin; Didehbani, Saeid Ahmadi; Zadahmad, Manouchehr; Seyedrezaei, M.; Mohsenian, Omid

doi:10.1007/s00170-014-6084-0

Cited by 11 publications

(3 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The idle time percentage was used as a criterion because it is a measure of waste time. The reliability criterion was selected because having more cells mitigates the risk created by damages in a cell or defects in the materials and increases the process flexibility [46]. The reliability was measured as the number of cells, where 1 is the lower value, and the total number of cells is the maximum value.…”

Section: Proposed Methodology Approachmentioning

confidence: 99%

Cellular manufacturing system selection with multi-lean measures using optimization and simulation

Bocanegra-Herrera

Orejuela-Cabrera²

2016

I&U:EfD

View full text Add to dashboard Cite

<p>The cellular manufacturing technique is widely used in many industries. This research proposes an alternative to design and balance a cellular manufacturing system of an assembly company, with the purpose to obtain an optimal configuration under different criteria such as Process, Total Cost, Idle Time and Reliability. The methodology consists in: I) To obtain candidate solutions using optimization models where the objectives are to minimize Cycle Time and Total Cost II) To find the performance measures of each candidate solution using discrete-event simulation III) To choose the optimal configuration using the multi-criteria decision analysis ANP. The best configuration was found to have the bottleneck in the starting, a smoothness index in 0, the highest Reliability and Idle Time of 0%, although it was not the solution with the lower cost.</p>

show abstract

Section: Proposed Methodology Approachmentioning

confidence: 99%

Cellular manufacturing system selection with multi-lean measures using optimization and simulation

Bocanegra-Herrera

Orejuela-Cabrera²

2016

I&U:EfD

View full text Add to dashboard Cite

show abstract

“…Equations (8) and (9) mean that the equipment cannot be overlapped, and the minimum distance of equipment should be kept. Equations (10) and (11) mean that the equipment should be located in the MC. Equations (12) and (13) mean that the MC should be located in the layout area.…”

Section: Fixed Demandmentioning

confidence: 99%

Layout Optimization of Flexible Manufacturing Cells Based on Fuzzy Demand and Machine Flexibility

Zhang

Zhou

Zhao

2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Layout flexibility is critical for the performance of flexible manufacturing cells, especially in dynamic production environment. To improve layout flexibility, layout optimization should consider more flexible factors based on existed models. On the one hand, not only should the current production demands be covered, but also the future uncertain demands should be considered so that the cell can adapt to the dynamic changes in a long term. On the other hand, the flexibility of machines should be balanced in the layout in order to guarantee that the cell can deal with dynamic new product introduction. Starting from these two points, we formulate a layout optimization model based on fuzzy demand and machine flexibility and then develop a genetic algorithm with bilayer chromosome to solve the model. We apply this new model to a flexible cell of shell products and test its performance by comparing it with the classical two-stage model. The total logistics path of the new model is shown to be significantly shorter than the classical model. Then we carry out adaptability experiments to test the flexibility of the new model. For the dynamic situation of both the fluctuation of production demands and the introduction of new products, the new model shows obvious advantages to the classical model. The results indicate that this advantage becomes greater as the dynamics becomes greater, which implies that considering fuzzy demand and machine flexibility is necessary and reasonable in layout optimization, especially when the dynamics of the production environment is dramatic.

show abstract

“…On one hand, it seeks to minimize the costs associated with production, and on the other it reduces costs and waste of time caused by machine failure. Other examples of failure consideration in CMS are given in Aghajani et al (2014).…”

Section: Introductionmentioning

confidence: 99%

Dynamic cellular manufacturing system considering machine failure and workload balance

2018

View full text Add to dashboard Cite

Machines are a key element in the production system and their failure causes irreparable effects in terms of cost and time. In this paper, a new multi-objective mathematical model for dynamic cellular manufacturing system (DCMS) is provided with consideration of machine reliability and alternative process routes. In this dynamic model, we attempt to resolve the problem of integrated family (part/machine cell) formation as well as the operators' assignment to the cells. The first objective minimizes the costs associated with the DCMS. The second objective optimizes the labor utilization and, finally, a minimum value of the variance of workload between different cells is obtained by the third objective function. Due to the NP-hard nature of the cellular manufacturing problem, the problem is initially validated by the GAMS software in smallsized problems, and then the model is solved by two well-known meta-heuristic methods including non-dominated sorting genetic algorithm and multi-objective particle swarm optimization in large-scaled problems. Finally, the results of the two algorithms are compared with respect to five different comparison metrics.

show abstract

A multi-objective mathematical model for cellular manufacturing systems design with probabilistic demand and machine reliability analysis

Cited by 11 publications

References 37 publications

Cellular manufacturing system selection with multi-lean measures using optimization and simulation

Cellular manufacturing system selection with multi-lean measures using optimization and simulation

Layout Optimization of Flexible Manufacturing Cells Based on Fuzzy Demand and Machine Flexibility

Dynamic cellular manufacturing system considering machine failure and workload balance

Contact Info

Product

Resources

About