Abstract
Cell formation (CF) problem is one of the most important decision problems in designing a cellular manufacturing system includes grouping machines into machine cells and parts into part families. Several factors should be considered in a cell formation problem. In this work, robust optimization of a mathematical model of a dynamic cell formation problem integrating CF, production planning and worker assignment is implemented with uncertain scenario-based data. The robust approach is used to reduce the effects of fluctuations of the uncertain parameters with regards to all possible future scenarios. In this research, miscellaneous cost parameters of the cell formation and demand fluctuations are subject to uncertainty and a mixed-integer nonlinear programming model is developed to formulate the related robust dynamic cell formation problem. The objective function seeks to minimize total costs including machine constant, machine procurement, machine relocation, machine operation, inter-cell and intra-cell movement, overtime, shifting labors between cells and inventory holding. Finally, a case study is carried out to display the robustness and effectiveness of the proposed model. The tradeoff between solution robustness and model robustness is also analyzed in the obtained results.
Highlights
Today, global competitive environment has persuaded manufacturing practitioners to deliver low-cost and highquality products
Because of the uncertainty of the demand parameter and the cost parameters related to the cell formation, the model might be infeasible for some various scenarios
A mathematical model based on a robust optimization approach has been presented in dynamic cell formation problem with uncertain data to integrate Cell formation (CF), production planning (PP) and worker assignment
Summary
Global competitive environment has persuaded manufacturing practitioners to deliver low-cost and highquality products. Some recently applied approaches have been put into practice to cope with the ever growing manufacturing costs, such as location, material handling system, and energy. GT is one of the main building blocks to implementing Just-In-Time (JIT) philosophy This approach is based upon grouping parts and machines together with respect to their similarities in production processes, functionalities, etc. The first step associates with cell formation problem which comprises assigning parts to their families and machines to their corresponding machine cells based on some features, such as similar geometric design or processing requirements. Group Scheduling (GS) is accomplished to schedule parts within part families Required resources such as labors and material handling devices are assigned to the manufacturing cells
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