Abstract
Machine learning, neural networks, and metaheuristic algorithms are relatively new subjects, closely related to each other: learning is somehow an intrinsic part of all of them. On the other hand, cell formation (CF) and facility layout design are the two fundamental steps in the CMS implementation. To get a successful CMS design, addressing the interrelated decisions simultaneously is important. In this article, a new nonlinear mixed-integer programming model is presented which comprehensively considers solving the integrated dynamic cell formation and inter/intracell layouts in continuous space. In the proposed model, cells are configured in flexible shapes during the planning horizon considering cell capacity in each period. This study considers the exact information about facility layout design and material handling cost. The proposed model is an NP-hard mixed-integer nonlinear programming model. To optimize the proposed problem, first, three metaheuristic algorithms, that is, Genetic Algorithm (GA), Keshtel Algorithm (KA), and Red Deer Algorithm (RDA), are employed. Then, to further improve the quality of the solutions, using machine learning approaches and combining the results of the aforementioned algorithms, a new metaheuristic algorithm is proposed. Numerical examples, sensitivity analyses, and comparisons of the performances of the algorithms are conducted.
Highlights
Facility layout (FL) considers the layout of machines within cells (Intracell layout) and the layout of cells (Intercell Layout) on the shop floor which can be counted as an essential element to plan a CMS. e cutdown in material handling cost, work-in-process, and throughput rate is the result of an efficient FL [1]. e system’s performance would have the capacity to be boosted by designing a capable layout and the expense of the production would fall around 40% to 50% on average [2]
Layout design has been somehow neglected in the CMS as many of the related researches have only investigated the cell formation (CF), the vital point in this design could be using the FL problem [3, 4]
Inter/intracell layouts and dynamic cell formation in a steady space were investigated concurrently in this paper by a novel mixed-integer nonlinear programming model. is model was performed somehow to minimize the cost of the number of exceptional elements (EEs), parts total transportation expense, and cell redesigning
Summary
Facility layout (FL) considers the layout of machines within cells (Intracell layout) and the layout of cells (Intercell Layout) on the shop floor which can be counted as an essential element to plan a CMS. e cutdown in material handling cost, work-in-process, and throughput rate is the result of an efficient FL [1]. e system’s performance would have the capacity to be boosted by designing a capable layout and the expense of the production would fall around 40% to 50% on average [2]. The decisions that are made in the FL and CF problem are Mathematical Problems in Engineering interrelated, and tellingly to have a favored CMS design inscribing these two concurrently is of high importance [5]. Us, employing and proposing exact mathematical approaches to tackle the models of CMS is usually ineffective. To this end, different heuristic, metaheuristic, and machine learning approaches have been applied, which can effectively handle the NP-hard models of CMS. Neural networks, and metaheuristic algorithms are relatively new subjects employed broadly in different fields of industrial engineering and management studies. As the proposed mixed-integer nonlinear model in this paper is NP-hard, four metaheuristic algorithms are employed to tackle the problem.
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