Robust scheduling for multi-objective flexible job-shop problems with flexible workdays

<p>This study determines a robust schedule for a flexible job-shop scheduling problem with flexible workdays. The performance criteria considered in this study are tardiness, overtime and robustness. Furthermore, the problem is addressed in a Pareto manner, and a set of Pareto-optimal solutions is determined for this purpose. In consideration of all the aforementioned features, a goal-guided neighbourhood function is proposed based on efficient problem-dependent move-filtering methods. Two metaheuristic algorithms, named goal-guided multi-objective tabu search and goal-guided multi-objective hybrid search, are proposed in this work based on this neighbourhood function. The effectiveness of these approaches is demonstrated via empirical studies.</p

Soil carbon response to woody plant encroachment

Please see the metadata tab in the excel file for data description

Structure and composition of Gmelin larch forests in different sites.

<p>Values represent mean ± standard error (n = 3). Different letters in each column indicate significant differences among sites (post-Duncan test, P<0.05).</p

Variables and their abbreviations used in the study.

<p>Variables and their abbreviations used in the study.</p

Location of study sites in Heilongjiang Province of Northeast China.

<p>Location of study sites in Heilongjiang Province of Northeast China.</p

Significant mathematical equations and regression coefficients (a, b, c, d) used to predict structure and composition of <i>Pinus koraiensis</i> mixed forests from linear and quadratic components of MeanDBH_Pinus, PrecipWettestMonth, TempWarmestQuater and TempWarmestMonth.

<p>Equations from stepwise regression analyses (n = 30). S.E., standard error; , adjusted multiple coefficient of determination. Significance level:^NS (Non-significant) P>0.05.</p><p>*P<0.05.</p><p>**P<0.01.</p><p>***P<0.001.</p

Matrix of Pearson's two-tailed correlation coefficients between variables.

<p>Significance level:^NS Non-significant (P>0.05), *P<0.05, **P<0.01 (n = 12).</p

A Varying-Coefficient Expectile Model for Estimating Value at Risk

<div><p>This article develops a nonparametric varying-coefficient approach for modeling the expectile-based value at risk (EVaR). EVaR has an advantage over the conventional quantile-based VaR (QVaR) of being more sensitive to the magnitude of extreme losses. EVaR can also be used for calculating QVaR and expected shortfall (ES) by exploiting the one-to-one mapping from expectiles to quantiles, and the relationship between VaR and ES. Previous studies on conditional EVaR estimation only considered parametric autoregressive model set-ups, which account for the stochastic dynamics of asset returns but ignore other exogenous economic and investment related factors. Our approach overcomes this drawback and allows expectiles to be modeled directly using covariates that may be exogenous or lagged dependent in a flexible way. Risk factors associated with profits and losses can then be identified via the expectile regression at different levels of prudentiality. We develop a local linear smoothing technique for estimating the coefficient functions within an asymmetric least squares minimization set-up, and establish the consistency and asymptotic normality of the resultant estimator. To save computing time, we propose to use a one-step weighted local least squares procedure to compute the estimates. Our simulation results show that the computing advantage afforded by this one-step procedure over full iteration is not compromised by a deterioration in estimation accuracy. Real data examples are used to illustrate our method. Supplementary materials for this article are available online.</p></div

Matrix of two-tailed partial correlation coefficients between every dependent variable and the independent variables excluded from its model, controlling for the independent variable(s) included in its model.

<p>“–” indicates controlling for that variable. Significance level:^NS (Non-significant) P>0.05.</p><p>*P<0.05.</p><p>**P<0.01 (n = 30).</p

Composition and structure of <i>Pinus koraiensis</i> mixed forests in different sites.

<p>Values represent mean ± standard error (n = 3). Different letters in each column indicate significant differences among sites (post-Duncan test, P<0.05).</p