An oracle-based algorithm for robust planning of production routing problems in closed-loop supply chains of beverage glass bottles

The demand for glass bottles is exhibiting an upward trend over time. The manufacturing of glass bottles is costlier in terms of time and resources and is associated with a higher level of heat generation and environmental pollution compared to recycling processes. In response to the aforementioned challenges, companies that use glass bottles need to implement strategies to manage their reverse supply chains in conjunction with their traditional supply chains, as the economic and environmental benefits of returned products are unquestionable. Closed-loop supply chains (CLSCs) integrate forward and reverse flows of products and information. This integration helps companies to have a broader view of the whole chain. Despite these advantages, managing CLSCs can be challenging as they are exposed to many uncertainties regarding supply and demand processes, travel times, and quantity/quality of returned products.In this study, we consider the production planning, inventory management, and vehicle routing decisions of a CLSC of beverage glass bottles. We propose an MILP model and rely on a multi-stage adjustable robust optimization (ARO) formulation to deal with the randomness in both the demand for filled bottles and the requests for pickups of empty bottles. We develop an exact oracle-based algorithm to solve the ARO problem and propose a heuristic search algorithm to reduce the solution time. Our numerical experiments not only show the incompetency of the customary method, namely the affine decision rule approach, but also illustrate how our algorithms can solve the small-size problems and significantly improve the quality of the obtained solution for large problems. Furthermore, our numerical results show that robust plans tend to be sparse, meaning the routes are chosen so that empty bottles are transported to production sites in such a way that fewer new bottles need to be ordered. Thus, robust planning makes the CLSCs more environmentally friendly.     

Reconstruction of populations by stochastic optimization: Sensitivity analysis

The reconstruction of populations by stochastic optimization solves the nontrivial problem of finding demographic flows from population registers or vital statistics and censuses, if available. These flows allow the reconstruction of stocks (age pyramids and vital statistics). After a review of reconstruction methods, the sensitivity analysis shows the robustness of the method by stochastic optimization to flawed or missing values, to the length of the reconstruction period, and to variations in the actual demographic flows.     

A note on the non-negativity of continuous-time ARMA and GARCH processes

A general approach for modeling the volatility process in continuous-time is based on the convolution of a kernel with a non-decreasing Lévy process, which is non-negative if the kernel is non-negative. Within the framework of Continuous-time Auto-Regressive Moving-Average (CARMA) processes, we derive a necessary condition for the kernel to be non-negative, and propose a numerical method for checking the non-negativity of a kernel function. These results can be lifted to solving a similar problem with another approach to modeling volatility via the COntinuous-time Generalized Auto-Regressive Conditional Heteroscedastic (COGARCH) processes.     

混合学习法前向网络多属性储层参数预测

应用前向网络描述地震属性和储层参数间的非线性映射关系时，经典的误差反向传播算法存在收敛速度慢，易陷入局部极值等诸多不足。研究了融合粒子群优化算法和误差反向传播算法的混合学习法前向网络多属性储层参数预测技术。粒子群优化算法是一种群体随机搜索演化计算技术，具有较快的收敛速度和较强的全局搜寻能力；误差反向传播算法本质上是梯度下降算法，注重局部搜索。混合学习法为两种学习算法交替执行，首先以粒子群优化算法训练网络，当误差能量在规定的迭代次数内不再发生变化时，采用误差反向传播算法实现局部寻优。理论函数逼近测试和实际储层参数预测实验说明了混合学习法具有学习时间短、求解效率高、可靠性强的优点，具有良好的应用前景。     

教育媒体与技术在机械制图课程教学中的优化运用

为了获得良好的教学效果,提高教育教学质量,关键是如何实现教育媒体与技术在课程教学中的优化运用。文章在调查研究和理论分析的基础上,建立了基于教育媒体与技术的优化教学模型,重点介绍了教育媒体与技术在机械制图课程教学中优化运用的一些做法和经验。     

Older Drivers Retiring From the Road: An Application of the Selection,Optimization, and Compensation Model

Purpose: This study uses the selection, optimization, and compensation (SOC) model to compare older adults who have given up driving, those who still drive, and those who drive less to identify differences in the three groups. Design and Methods: This cross-sectional study of 281 older adults is part of the Naturally Occurring Retirement Community Demonstration Project. Study participants completed interviews that included questions on demographics, health and functioning, mental health, social support, and current levels of engagement in activities including current driving status. Results: Data suggest that driving retirement is a process and that a substantial number of people are in the transitional stage, where they are less functional than those who still drive and more similar to those who have retired from the road. Implications: Given the similarity of those who drive less to those who have given up driving, front-line professionals and policy makers must work together to ease the transition to driving retirement for those without adequate ability to effectively compensate for their inability to continue to drive safely.     

计算气井储量新方法

提出了采用弹性二相法结合物质平衡方程作为计算全井储量的新方法。弹性二相法主要用于气藏开发进入拟稳态以后，要求压力和产量的测量精确，气藏的工作制度对弹性二相法储量也有很大影响，其计算可靠程度已被矿场实际证实；物质平衡方程在开发初期计算精度不高。将弹性二相法与物质平衡法结合建立多目标优化函数，该方法既能很好地应用弹性二相法，又能体现物质平衡，使得该方法适用范围更广，计算结果更可靠，对致密气藏具有更好的推广应用价值。通过实例气井动态储量进行的验证对比表明，该方法在理论上是合理的，实际验证是可行的。     

利用专门用途英语理论（ESP）进行高职英语课程改革

大学生是祖国的未来，为国家培养宝贵人才是每个教师的重任。在我国教育改革的洪流下。一线教师应充分掌握和利用先进的教学方法和教学资源。作者盼望通过本文能够与广大教育教学工作者们进行学术交流．为国家培养更多、更优秀的人才。     

多心理账户下的群体投资决策规则优化研究

从决策者有限理性角度,引入行为金融于群体投资决策系统,研究了多心理账户下群体投资决策规则优化问题。研究给出了群体投资决策系统诸多非标准金融实证异象行为的金融解释,并指出了决策者金字塔式心理账户结构;研究提出了各个心理账户下离散随机变量的信息度量方法,以行为证券组合理论为内核进行集成建模,揭示了多心理账户下群体投资决策规则的优化机理。     

An environmental life cycle optimization model for the European pulp and paper industry

Will paper recycling reduce the environmental impact of the European pulp and paper sector? If so, is maximal paper recycling the best policy to optimize the life cycle of the pulp and paper sector? We explore these questions using an approach that combines materials accounting methods and optimization techniques. Environmental impact data are inputs for a linear programming network flow model to find optimal configurations for the sector. These configurations consist of a mix of different pulping technologies, a geographical distribution of pulp and paper production, and a level of recycling consistent with the lowest environmental impacts. We use the model to analyse scenarios with different recycling strategies. Recycling offers a reduction in environmental impact in regions with a high population and a large production of paper and board products. Regions with a large production of graphic products should focus on cleaner virgin pulp production with energy recovery. We conclude that relocation of paper production also offers a reduction in environmental impact. However, the severe effects on the economy make this policy less attractive than a combination of recycling, cleaner pulp production and energy recovery.