36,120 research outputs found

    Common operation scheduling with general processing times: A branch-and-cut algorithm to minimize the weighted number of tardy jobs

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    Common operation scheduling (COS) problems arise in real-world applications, such as industrial processes of material cutting or component dismantling. In COS, distinct jobs may share operations, and when an operation is done, it is done for all the jobs that share it. We here propose a 0-1 LP formulation with exponentially many inequalities to minimize the weighted number of tardy jobs. Separation of inequalities is in NP, provided that an ordinary min Lmax scheduling problem is in P. We develop a branch-and-cut algorithm for two cases: one machine with precedence relation; identical parallel machines with unit operation times. In these cases separation is the constrained maximization of a submodular set function. A previous method is modified to tackle the two cases, and compared to our algorithm. We report on tests conducted on both industrial and artificial instances. For single machine and general processing times the new method definitely outperforms the other, extending in this way the range of COS applications

    One-Dimensional Cutting Stock Optimisation by Suborders

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    This paper introduces a method for solving a one-dimensional cutting stock problem by suborders. The method is used for large orders that for technological and logistical reasons cannot be filled in a single order, but only in several successive suborders. The method has two stages. In the first stage, the suborders are generated and in the second the trim-loss is minimised. All leftovers longer than D are returned to stock and reused. Shorter leftovers are treated as trim-loss and discarded. A detailed description of the method is provided by using a practical case. The method is tested by solving 108 randomly generated problem instances

    CEO turnover in a competitive assignment framework

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    There is considerable and widespread concern about whether CEOs are appropriately punished for poor performance. The empirical literature on CEO turnover documents that CEOs are indeed more likely to be forced out if their performance is poor relative to the industry average. However, CEOs are also more likely to be replaced if the industry is doing badly. We show that these empirical patterns are natural and efficient outcomes of a competitive assignment model in which CEOs and firms form matches based on multiple characteristics, and where industry conditions affect the outside options of both managers and firms. Our model also has several new predictions about the type of replacement manager, and their pay and performance. We construct a dataset which describes all turnover events during the period 1992-2006 and show that these predictions are also born out empirically.Executive Turnover; Matching Models; Competitive Assignment; CEO Labor Market
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