Efficient meta-heuristics based on various dominance criteria for a single-machine bi-criteria scheduling problem with rejection

In this paper, we try to fill in the gap between theory and practice in production scheduling by defining a new term as “rejection” and treating the corresponding scheduling problem with multi-objective optimization approach. We study a bi-objective single machine scheduling problem with rejection. At the beginning of scheduling time horizon, scheduler needs to decide which job shall be rejected due to the resource constraints regarding two objective functions: minimization of total weighted completion time of accepted jobs and total rejection penalty of rejected jobs. We develop different algorithms to find the best estimation of Pareto-optimal front for this problem. In order to improve the quality of the solutions, on the one hand, and facilitate the process of selecting best solution for the final decision maker, on the other hand, we integrate various dominance criteria into our proposed algorithms. Finally we compare the performance of those methods by testing on a large set of instances and highlight the advantages and weak points of each one.     

Multi-objective optimization of shield construction parameters based on random forests and NSGA-II

Metro shield construction will inevitably cause changes in the stress and strain state of the surrounding soil, resulting in stratum deformation and surface settlement (SS), which will seriously endanger the safety of nearby buildings, roads and underground pipe networks. Therefore, in the design and construction stage, optimizing the shield construction parameters (SCP) is the key to reducing the SS rate and increasing the safe driving speed (DS). However, optimization of existing SCP are challenged by the need to construct a unified multiobjective model for optimization that are efficient, convenient, and widely applicable. This paper innovatively proposes a hybrid intelligence framework that combines random forest (RF) and non-dominant classification genetic algorithm II (NSGA-II), which overcomes the shortcomings of time-consuming and high cost for the establishment and verification of traditional prediction models. First, RF is used to rank the importance of 10 influencing factors, and the nonlinear mapping relationship between the main SCP and the two objectives is constructed as the fitness function of the NSGA-II algorithm. Second, a multiobjective optimization framework for RF-NSGA-II is established, based on which the optimal Pareto front is calculated, and reasonable optimized control ranges for the SCP are obtained. Finally, a case study in the Wuhan Rail Transit Line 6 project is examined. The results show that the SS is reduced by 12.5% and the DS is increased by 2.5% with the proposed framework. Meanwhile, the prediction results are compared with the back-propagation neural network (BPNN), support vector machine (SVM), and gradient boosting decision tree (GBDT). The findings indicate that the RF-NSGA-II framework can not only meet the requirements of SS and DS calculation, but also used as a support tool for real-time optimization and control of SCP.     

Conditional Preference Networks with User's Genuine Decisions

User's choices involve habitual behavior and genuine decision. Habitual behavior is often expressed using preferences. In a multiattribute case, the Conditional Preference Network (CP-net) is a graphical model to represent user's conditional ceteris paribus (all else being equal) preference statements. Indeed, the CP-net induces a strict partial order over the outcomes. By contrast, we argue that genuine decisions are environmentally influenced and introduce the notion of “comfort” to represent this type of choices. In this article, we propose an extension of the CP-net model that we call the CP-net with Comfort (CPC-net) to represent a user's comfort with preferences. Given that preference and comfort might be two conflicting objectives, we define the Pareto optimality of outcomes when achieving outcome optimization with respect to a given CPC-net. Then, we propose a backtrack search algorithm to find the Pareto optimal outcomes. On the other hand, two outcomes can stand in one of six possible relations with respect to a CPC-net. The exact relation can be obtained by performing dominance testing in the corresponding CP-net and comparing the numeric comforts.     

极值超出量服从广义Pareto分布的载荷谱外推方法研究

提出了极值超出量服从广义Pareto分布的载荷谱外推方法。将所提方法运用到汽车用户典型路面载荷谱的外推中，分别拟合了形状参数为零和不为零的广义Pareto分布函数，对比分析了不同形状参数的分布函数对载荷谱外推结果的影响。将外推前后的载荷谱、采集载荷谱分别从幅值域和频率域进行了对比分析，研究结果表明：基于广义Pareto分布函数形状参数为零的载荷谱外推方法可得到偏于保守的外推结果；基于广义Pareto分布函数形状参数不为零的外推方法得到的外推载荷谱幅值比采集载荷谱幅值偏大。     

ICT and cities revisited

This paper tests whether or not adoption of information and communication technologies (ICT) has offset agglomeration benefits and led to more dispersed spatial structures worldwide. The paper returns to Ioannides et al. (2008) and confirms, first by relying on the Pareto (Zipf) coefficient of the city size distribution as a proxy of spatial dispersion, that the diffusion of fixed telephony has caused more dispersed urban structures worldwide, in other words, greater urban decentralization. Similar causal effects are established for mobile telephony, which are novel, and the internet, which extend previous research. They are confirmed for such alternative measures of dispersion as the Gini coefficient, the Herfindahl index, and the coefficient of variation.     

中国碳酸钙工业生产现状及其发展对策

概述中国碳酸钙生产方法,重质碳酸钙(简称GCC)和轻质碳酸钙(简称PCC)的区别,GCC与PCC按粒径、按用途、按改性等分别执行的标准。进一步说明中国碳酸钙具有资源、能源、自主技术、国产设备、生产管理等优势;也同时存在规模小、设备陈旧、产品单一、精细化率低、产品质量不稳定、经济效益不佳等差距;提出加强联合;采用先进技术及设备,走大型化之路;研发新型活性剂,发展各类活性碳酸钙;开发新型化学漂白剂,提高碳酸钙白度;提高精细化率,大力发展专用、功能、系列产品,满足国内外市场需求等对策。     

岩体随机不连续面产状数据划分方法研究

动态聚类算法从本质上讲是单目标组合优化算法,一般需要事先给定目标分类数和初始聚类中心,且初始聚类中心的选择对数据划分结果影响较大。为了解决该问题,提出将产状数据的划分问题转化为多目标优化问题,并采用小生境Pareto遗传算法进行求解。针对聚类问题的特殊性,采用基于链表的编码方案,并建议相应的遗传操作算子;通过引入小生境技术和Pareto支配集理论,仅通过一次求解可由Pareto支配集给出对应于不同目标组数的最优分组结果,而且不用事先给定目标组数以及初始聚类中心。最后,将算法应用于三峡船闸高边坡岩体实测不连续面产状数据的划分,得到较为符合实际的优势结构面分组。     

基于多目标粒子群算法的电力系统环境经济调度研究

提出了一种新的多目标粒子群优化(Multi-Objective Particle Swarm Optimization, MOPSO )算法,用于求解电力系统的环境/经济调度问题。通过设计特定的约束修正因子,将不可行解修正成可行解,并在此基础上用惩罚函数法构建了新的适用于多目标粒子群的适应度函数模型。根据帕累托占优条件形成历史帕累托最优解集和全局帕累托最优解集,引入稀疏度排序法选择全局最优解,基于帕累托最优前沿的斜率特性,提出用斜率法筛选非劣解,采用基于模糊数学的满意度评价模型选择POF的折衷最优解。最后,用IEEE-30节点标准测试系统对所提算法进行了仿真测试,并与其他算法进行了对比。仿真结果表明所提算法可行、有效。     

Korean civil engineers: handling of natural disaster management and its implication

Economic damages, which are influenced by diverse natural disasters, have had quite an impact in Korea. Civil engineers in the country have played a major role in managing floods accompanied by typhoons. This study aims to improve the role of Korean civil engineers in natural disaster management. Comparative perspective was used as the major methodology. Civil engineers' dominance‐oriented management was compared to coordination‐oriented management through four variables, namely civil engineering employees, civil engineering researchers, civil engineering businessmen and other professionals. The key finding is that Korea has to change its civil engineers' dominance‐oriented management into coordination‐oriented management. Similarly, other nations may apply the lessons learned from Korea to their cases via the social impact of disaster, trust relations, participatory approach, various networks, emergency plans, training and exercises and the value of equity.     

Time–cost optimization: using GA and fuzzy sets theory for uncertainties in cost

Uncertainties should be considered in any time–cost trade‐off problems when minimizing project cost and duration, which leads to the so‐called stochastic time–cost trade‐off problem. A new approach to investigate stochastic time–cost trade‐off problems employing fuzzy logic theory is presented. The proposed approach fully embeds the fuzzy structure of the uncertainties in total direct cost into the model. An appropriate GA is used to develop a solution to the multi‐objective fuzzy time cost model. The accepted risk level of the project manager is defined through α cut approach for which a separate Pareto front with set of non‐dominated solutions has been developed. To compare the alternative set of options for any assumed project duration, associated fuzzy costs for different values of α cut are ranked employing two appropriate approaches for fuzzy costs comparison. The proposed models are applied to solve two benchmark test problems. It is shown that the models facilitate the decision‐making process by selecting specified risk levels and employing the associated Pareto front.