A mathematical programming model for manufacturing cell formation to develop multiple configurations

Shiyas, C.R.; Pillai, V. Madhusudanan

doi:10.1016/j.jmsy.2013.10.002

Cited by 32 publications

(23 citation statements)

References 58 publications

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“…According to the mathematical model established, the layout of the MCs can still ensure that the logistics path is in the optimized state when the number of products of the MCs changes within a certain range, as it considers the uncertainty of demand in the model. When new products are introduced into the MCs, it can effectively avoid the frequent crosscell routines of new products by distributing high flexible machines in the layout, which is guaranteed by (2). Therefore, when new products are introduced frequently, the logistics path of the entire cell is still in a more optimized state.…”

Section: Fixed Demandmentioning

confidence: 99%

“…With the intensification of market competition and the developing trend of the manufacturing system towards digitization, networking, and intelligence, the highly flexible manufacturing cells, especially numerically controlled (NC) manufacturing cells (MC), are becoming one of the most important manufacturing modes dealing with dynamic production environment [1,2].…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Zhou

Zhao

2018

Mathematical Problems in Engineering

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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.

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Section: Fixed Demandmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhang

Zhou

Zhao

2018

Mathematical Problems in Engineering

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“…These include mathematical programming techniques that optimize a specific algebraic objective function of specific decision variables (Chryssolouris, 2006). These exact methods converge to a single solution which is sometimes not feasible due to some unpractical condition not being considered in the model (Shiyas and Pillai, 2014). For this reason, the generation of alternative configurations that can be further scrutinized by the designer can potentially lead to a more effective decision-support tool.…”

Section: A) State Of the Artmentioning

confidence: 99%

Layout validation and re-configuration in Plug&Produce systems

Neves

Ribeiro

Dias-Ferreira

et al. 2016

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Purpose This paper aims to provide a method and decision support tool to enhance swift reconfiguration of Plug&Produce (P&P) systems in the presence of continuously changing production orders. Design/methodology/approach The paper reviews different production scenarios and system design and configuration methods and more particularly specifies the need of decision support tools for P&P systems that integrate configuration and planning activities. This problem is then addressed by proposing a method that helps reduce the solution space of the reconfiguration problem and allows the timely selection of the most promising reconfiguration alternative. Findings The proposed method was found to be helpful in reducing the reconfiguration alternatives that need to be considered and in selecting the most promising one for different orders. The advantages and limitations of this method are identified, and an illustrative test case of the approach is presented, corroborating the method applicability in the absence of large queues in the system. Originality/value This paper addresses a less explored domain within the P&P systems research field, which is the system reconfiguration. It proposed a method to support system validation and reconfiguration jointly with an illustrative test case. This represents an original contribution to the P&P research field, and it can have impact in improving agility and decreasing the complexity of reconfiguration activities to cope with constantly changing production orders.

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“…They solved this multi-objective model with NSGAII in order to getting Pareto optimal solutions. Shiyas et al [38] presented and analyzed a mathematical model for the design of manufacturing cells, which considered two conflicting objectives such as the heterogeneity of cells and the intercellular moves. A genetic algorithm (GA) based solution methodology developed for solving the model using an optimization package.…”

Section: Multi-objective Approaches In Cmsmentioning

confidence: 99%

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

Aghajani

Didehbani

Zadahmad

et al. 2014

Int J Adv Manuf Technol

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This paper presents a dynamic multi-objective mixed integer mathematical model for cell formation problem with probabilistic demand and machine reliability analysis, where the total system costs, machine underutilization cost, and maximum system failure rate over the planning time periods are to be minimized simultaneously. The total system cost objective function calculates machine operating, internal part production, intercellular material handling, and subcontracting costs. Since in this type of problems, objectives are in conflict with each other, so finding an ideal solution (a solution that satisfies all objectives simultaneously) is not possible. Therefore, this study uses the augmented ε-constraint method (to solve small size problems) and a nondominated sorting genetic algorithm (NSGAII) (to solve large size problems) to find the Pareto optimal frontier that decision makers can select her/his preferred solution. Numerical examples will be solved to demonstrate the efficiency of the proposed algorithm.

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A mathematical programming model for manufacturing cell formation to develop multiple configurations

Cited by 32 publications

References 58 publications

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

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

Layout validation and re-configuration in Plug&Produce systems

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

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