Narrow Results

We consider a single-machine scheduling problem such that the due dates are assigned not to the jobs but to the position at which the job is processed. We focus on the case with identical due date intervals. The objective is to minimize the weighted number of early and tardy jobs. First, we show that the problem is strongly NP-hard and has no $\rho$-approximation algorithm for any fixed value $\rho >1$. Then, we investigate polynomially solvable cases. Finally, we show that the preemption version is weakly NP-hard through its equivalence to the problem of minimizing the weighted number of tardy jobs.

Software defect prediction is an effective approach to save testing resources and improve software quality, which is widely studied in the field of software engineering. The effort-aware just-in-time software defect prediction (JIT-SDP) aims to identify defective software changes in limited software testing resources. Although many methods have been proposed to solve the JIT-SDP, the effort-aware prediction performance of the existing models still needs to be further improved. To this end, we propose a differential evolution (DE) based supervised method DEJIT to build JIT-SDP models. Specifically, first we propose a metric called density-percentile-average (DPA), which is used as optimization objective on the training set. Then, we use logistic regression (LR) to build a prediction model. To make the LR obtain the maximum DPA on the training set, we use the DE algorithm to determine the coefficients of the LR. The experiment uses defect data sets from six open source projects. We compare the proposed method with state-of-the-art four supervised models and four unsupervised models in cross-validation, cross-project-validation and timewise-cross-validation scenarios. The empirical results demonstrate that the DEJIT method can significantly improve the effort-aware prediction performance in the three evaluation scenarios. Therefore, the DEJIT method is promising for the effort-aware JIT-SDP.

This paper introduces the rationale and the fundamental elements and algorithms of a reliability engineering methodology combining analytical and simulation tools, and discusses its application to the design of a large, multi-cell and heterogeneous production system with just-in-time (JIT) deliveries. In order to cope with the inherent complexity of such analysis, a two level hierarchical modeling and evaluation framework was developed. Local models are first obtained from the failure and repair processes of the manufacturing equipment. Then, these models are combined with the failure propagation delays introduced by the work-in-process buffers in order to obtain the system level model. The evaluation algorithm is able to deal with reliability models containing stochastic processes with generalized distributions. This fundamental requirement comes from the fact that repair and failure propagation processes typically present hyper-exponential distributions, e.g., lognormal distributions, that cannot be assessed using the conventional reliability techniques. The second part of the paper addresses several design issues of the production system that directly impact the reliability of the deliveries, and explains how the behavioral and structural characteristics of JIT production systems were explored in order to implement effective evaluation algorithms.