During the past few decades, developing efficient methods to solve dynamic facility layout problems has been focused on significantly by practitioners and researchers. More specifically meta-heuristic algorithms, especially genetic algorithm, have been proven to be increasingly helpful to generate sub-optimal solutions for large-scale dynamic facility layout problems. Nevertheless, the uncertainty of the manufacturing factors in addition to the scale of the layout problem calls for a mixed genetic algorithm–robust approach that could provide a single unlimited layout design. The present research aims to devise a customized permutation-based robust genetic algorithm in dynamic manufacturing environments that is expected to be generating a unique robust layout for all the manufacturing periods. The numerical outcomes of the proposed robust genetic algorithm indicate significant cost improvements compared to the conventional genetic algorithm methods and a selective number of other heuristic and meta-heuristic techniques.
Index tracking is an investment approach where the primary objective is to keep portfolio return as close as possible to a target index without purchasing all index components. The main purpose is to minimize the tracking error between the returns of the selected portfolio and a benchmark. In this study, quadratic as well as linear models are presented for minimizing the tracking error. The uncertainty is considered in the input data using a tractable robust framework that controls the level of conservatism while maintaining linearity. The linearity of the proposed robust optimization models allows a simple implementation of an ordinary optimization software package to find the optimal robust solution. The proposed model of this study employs Morgan Stanley Capital International Index as the target index and the results are reported for six national indices including Japan, the USA, the UK, Germany, Switzerland and France. The performance of the proposed models is evaluated using several financial criteria e.g., information ratio, market ratio, Sharpe ratio and Treynor ratio. The preliminary results demonstrate that the proposed model lowers the amount of tracking error while raising values of portfolio performance measures.
Spinoff firms are a common phenomenon in entrepreneurship where employees leave incumbent parent firms to found their own. Like other types of new firms, such new spinoffs face liabilities of newness and smallness. Previous research has emphasised the role of the initial endowments from their parent firm to overcome such liabilities. In this study, we argue and are the first to show, that, in addition to such endowments, growing an alliance network with firms other than their parents’ is also critical for spinoff performance. Specifically, we investigate the performance effect of alliance network growth in newly founded spinoffs using a longitudinal sample of 248 spinoffs and 3370 strategic alliances in the mining industry. Drawing on theory based on the resource adjustment costs of forming alliances, we posit and find a U-shaped relationship between the alliance network growth and spinoff performance, above and beyond the parent firm’s influence. We further hypothesise and find that performance effects become stronger with increased time lags between alliance network growth and spinoff performance, and when spinoffs delay growing their alliance networks. Implications for theory and practice are discussed.
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