A simple, robust and e cient sorting algorithm that can be used to perform the principle function of group technology is presented. Several numeric examples are given, and methods for optimizing the sort to increase the density of grouping explored. The robustness of the algorithm is demonstrated in a series of examples, most of which are extracted from examples cited by others. The algorithm is implemented on MATLAB and the software is available from the authors. Nomenclature A = A mn = {aij} incidence matrix B row = {bpq} row closeness matrix B col = {bpq} column closeness matrix w = {wi} weighting vector d ordering vector F = {fij} association matrix gr i row group i, a row vector gc i column group i, a row vector Gr = {gri} matrix of row groups Gc = {gci} matrix of column groups J
In both manufacturing and remanufacturing systems, exploiting bulk buying and avoiding delivery delays due to material shortages are crucial. One method that aids in these processes is component standardization. Additionally, company managers seek to reduce labor costs and mitigate the risk of sudden worker resignation or absence due to, for example, reasons associated with the COVID-19 pandemic. The aforementioned problems could be solved using the sorting algorithm proposed in this study. The concept of the proposed algorithm is based on group technology. One numerical example and two case studies are presented to demonstrate the utility of the proposed algorithm. The first example suggested that the performance of the algorithm proposed in this study is superior to another one in the literature. The second one demonstrated that the algorithm in this work achieves component standardization by reducing an initial number of 12 components down to 6. The final case study provides an effective means of grouping workers with similar operational abilities and suggests how to assign new tasks to other skilled workers if a worker resigns suddenly or cannot attend work due to pandemic prevention measures.
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