Combined Life Cycle Assessment and Costing Analysis Optimization Model Using Multiple Criteria Decision Making in Earth-Resource Systems

Life Cycle Assessment (LCA) is a comprehensive method to evaluate all attributes or aspects of potential environmental impact throughout a product’s lifecycle. Financial impacts are often added to the systemic evaluation process to reflect both environmental and economic assessment. For the specific application of LCA informing design of new technologies, when numerous variables are undecided or under defined, the process of forming an inventory of complete dataset is very difficult. Accumulating the early data consumes time, and limits application of LCA to new technologies and projects. As such, LCA may not normally be associated with forecasting or guiding a design/production process with an incomplete data set. Here, a life cycle assessment optimization model (LCAO) is described for incomplete data sets, based on the life cycle inventory (LCI) hybrid method and a modified multiple criteria decision making (MCDM) approach. The approach requires data, but can proceed in the given an incomplete or uncertain data set. The model of the algorithm also shows promising results to reveal previously unknown key variables within the dataset, which can then facilitate the minimization of environmental impact while maximizing economic benefits in product design.

Bi-Criteria Optimization Technique in Stochastic System Maintenance Allocation Problem

In this paper, the problem of optimum allocation of repairable and replaceable components in a system is formulated as a Bi-objective stochastic non linear programming problem. The system maintenance time and cost are random variable and has gamma and normal distribution respectively. A Bi-criteria optimization technique, weighted Tchebycheff is used to obtain the optimum allocation for a system. A numerical example is also presented to illustrate the computational details.

An Application of the Maximum Theorem in Multi-Criteria Optimization, Properties of Pareto-Retract Mappings, and the Structure of Pareto Sets

In this paper we consider three problems in continuous multi-criteria optimization: An application of the Berge Maximum Theorem, properties of Pareto-retract mappings, and the structure of Pareto sets. The key goal of this work is to present the relationship between the three problems mentioned above. First, applying the Maximum Theorem we construct the Pareto-retract mappings from the feasible domain onto the Pareto-optimal solutions set if the feasible domain is compact. Next, using these mappings we analyze the structure of the Pareto sets. Some basic topological properties of the Pareto solutions sets in the general case and in the convex case are also discussed.

Multi-Criteria Decision Analysis for Usage Based Optimization of Powertrains

The electrification of powertrains leads to an increasing diversification of powertrain configurations. Each single configuration has its specific advantages which appear depending on the usage profile. To find the usage based optimal powertrain in consideration of a variety of evaluation criteria, the powertrains have to be optimized for the usage profile and characteristics have to be extracted from the usage profile. The carbon dioxide emissions of the optimized powertrains and usage based criteria are used in a multi-criteria decision analysis to determine the optimal powertrain for a specific usage profile. The description of characteristic maps forms the objective function of a minimization problem. The determined carbon dioxide emissions are one criterion in a multi-criteria decision process. All considered criteria are at least partly objective so that subjective ratings are eliminated as far as possible. The result is an optimized powertrain for a desired usage under the consideration of objective criteria that are extracted from the usage profile.

Three-dimensional physical simulation and optimization of water injection of a multi-well fractured-vuggy unit

With complex fractured-vuggy heterogeneous structures, water has to be injected to facilitate oil pro- duction. However, the effect of different water injection modes on oil recovery varies. The limitation of existing numerical simulation methods in representing fractured- vuggy carbonate reservoirs makes numerical simulation difficult to characterize the fluid flow in these reservoirs. In this paper, based on a geological example unit in the Tahe Oilfield, a three-dimensional physical model was designed and constructed to simulate fluid flow in a fractured-vuggy reservoir according to similarity criteria. The model was validated by simulating a bottom water drive reservoir, and then subsequent water injection modes were optimized. These were continuous （constant rate）, intermittent, and pulsed injection of water. Experimental results reveal that due to the unbalanced formation pressure caused by pulsed water injection, the swept volume was expanded and consequently the highest oil recovery increment was achieved. Similar to continuous water injection, intermit- tent injection was influenced by factors including the connectivity of the fractured-vuggy reservoir, well depth, and the injection-production relationship, which led to a relative low oil recovery. This study may provide a constructive guide to field production and for the devel- opment of the commercial numerical models specialized for fractured-vuggy carbonate reservoirs.

Optimal control approach to stability criteria on Hill's equations

This paper gives a tutorial on how to prove Lyapunov type criteria by optimal control methods. Firstly, we consider stability criteria on Hill’s equations with nonnegative potential. By optimal control methods developed in 1990s, we obtain several stability criteria including Lyapunov’s criterion, Neǐgauz and Lidskiǐ’s criterion. Secondly, we present stability criteria on Hill’s equations with sign-changing potential in which Brog’s criterion and Krein’s criterion are included.

Generating Epsilon-Efficient Solutions in Multiobjective Optimization by Genetic Algorithm

We develop a new evolutionary method of generating epsilon-efficient solutions of a continuous multiobjective programming problem. This is achieved by discretizing the problem and then using a genetic algorithm with some derived probabilistic stopping criteria to obtain all minimal solutions for the discretized problem. We prove that these minimal solutions are the epsilon-optimal solutions to the original problem. We also present some computational examples illustrating the efficiency of our method.

Reactive Search Optimization;Application to Multiobjective Optimization Problems

During the last few years we have witnessed impressive developments in the area of stochastic local search techniques for intelligent optimization and Reactive Search Optimization. In order to handle the complexity, in the framework of stochastic local search optimization, learning and optimization has been deeply interconnected through interaction with the decision maker via the visualization approach of the online graphs. Consequently a number of complex optimization problems, in particular multiobjective optimization problems, arising in widely different contexts have been effectively treated within the general framework of RSO. In solving real-life multiobjective optimization problems often most emphasis are spent on finding the complete Pareto-optimal set and less on decision-making. However the com-plete task of multiobjective optimization is considered as a combined task of optimization and decision-making. In this paper, we suggest an interactive procedure which will involve the decision-maker in the optimization process helping to choose a single solution at the end. Our proposed method works on the basis of Reactive Search Optimization (RSO) algorithms and available software architecture packages. The procedure is further compared with the excising novel method of Interactive Multiobjective Optimization and Decision-Making, using Evolutionary method (I-MODE). In order to evaluate the effectiveness of both methods the well-known study case of welded beam design problem is reconsidered.

Towards Multi-Faceted Test Cases Optimization

The target of software engineering is to produce high quality software product at low cost. Software testing is labour-intensive, ambiguous and error prone activity of software development. How to provide cost-effective strategies for software test cases optimization problem such as classification, minimization, selection, and prioritization has been one of the research focuses in software testing for a long time. Many researchers and academicians have addressed the effectiveness/fitness and optimization of test cases, and obtained many interesting results. However, one issue of paramount importance in software testing i.e. the intrinsic imprecise and uncertainty of test cases fitness, fitness parameters, multi-objective optimization, is left unaddressed. Test cases fitness depends on several parameters. Vagueness of fitness of test cases and their fitness parameters have created the uncertainty in test cases optimization. Cost and adequacy values are incorporated into multi-faceted optimization of test cases. This paper argues test cases optimization requires multi-faceted optimization in order to adequately cater realistic software testing. In this paper, authors have identified several parameters for test cases fitness and multiple objectives for test cases optimization. In addition above, authors have formulated the test cases optimization problem in three different ways using multi-faceted concept. These formulations can be used in future by authors and researchers.

Feasibility of underground NPP and its MCDA-based optimization

The underground nuclear power plant(NPP)makes full use of land resources, reduces costs, makes better use of its passive safety, and avoids radioactivity release into the atmosphere in serious nuclear accidents.In this paper, for obtaining comprehensive and integrated analyses on this new NPP design, we introduce four kinds of underground NPP designs, analyze the feasibility of each design from various aspects, and use the multiple criteria decision analysis method to choose the best option.

Topology Optimization in Damping Structure Based on ESO

The damping material optimal placement for the structure with damping layer is studied based on evolutionary structural optimization (ESO) to maximize modal loss factors. A mathematical model is constructed with the objective function defined as the maximum of modal loss factors of the structure and design constraints function defined as volume fraction of damping material. The optimal placement is found. Several examples are presented for verification. The results demonstrate that the method based on ESO is effective in solving the topology optimization of the structure with unconstrained damping layer and constrained damping layer. This optimization method suits for free and constrained damping structures.

Optimization in Site Selection of Wind Turbine for Energy Using Fuzzy Logic System and GIS—A Case Study for Gujarat

The development of new wind energy project requires studying of many parameters to achieve maximum benefits at the cost of minimum environmental impacts. Using Geographic Information System (GIS), an analytical framework has been developed in this paper with fuzzy logic to evaluate the suitable site for turbines for optimum energy output. The criteria for suitable site for energy optimization are environmental, physical and human factors. The present study helps to assess the appropriate sites for the wind turbines in Gujarat. The result obtained from the study conveys the suitability of the development of wind turbines along the western parts of Gujarat. The suggested model could be used for the future site selection of the wind turbine which in turn could be of orientation for energy planners and decision makers.

气流分选技术研究进展

双碳背景下,气流分选技术因污染低、成本低、不用水、投资省、工艺简单等优势备受关注。为提升气流分选技术在多领域、多地区、多工况的适应性,解决气流分选物料适用范围窄、细粒分选效率低、设备大型化难等问题迫在眉睫。围绕气流分选理论、技术应用和技术改进3方面系统评述气流分选技术研究进展。首先,介绍了气流分选技术原理,分析气流分选过程颗粒受力及分选理论进展;其次,综述了气流分选在固废、矿业、农业等领域的应用进展,并评析了技术优势和技术短板;再次,系统分析了气流分选供风方式优化和设备结构优化等技术改进进展;最后,给出了通过单体并联和相似放大准则提高设备处理量,通过预干燥、分选干燥一体化、改进入料方式提高潮湿物料适应性与分散性,通过窄粒级分选和保持物料形状一致性的预处理过程提高细粒和薄片物料分选效率等路径。展望了未来气流分选技术在线检测与控制智能化、分选装备大型化、分选效率精细化、分选工艺联合化等方面的发展方向,为气流分选技术进一步研究和推广应用提供重要依据。

热电器件性能的权衡优化研究

热电器件能够实现热能与电能的直接转换,在废热利用和发展洁净能源技术方面具有特殊优势,而增强热电器件的热力学性能更具有实践性意义.本文利用权衡优化方法(也称Ω判据)研究了热电器件的热力学特征,在构建权衡优化目标函数的基础上,得到了权衡优化判据下的效率和制冷系数表达式,详细阐释了热电品质因子对于效率和制冷系数的影响.与最大功率(最大制冷功率)方法进行比较,发现权衡优化下的热电器件容易实现热力学性能的提高.

基于NSGA-Ⅱ的复合材料防撞梁碰撞损伤多目标优化

为在设计时考虑低速碰撞损伤对碳纤维复合材料(CFRP)防撞梁力学性能的影响,发展了改进Hashin失效准则的VUSDFLD子程序来判定低速碰撞时CFRP防撞梁7个方向的损伤程度,采用壳单元模拟了复合材料的力学性能,建立了CFRP防撞梁低速碰撞有限元显式动力学模型。采用该方法对相同实验条件下的复合材料层合板低速冲击剩余强度进行了计算,并将计算结果与实验结果进行了对比,仿真结果与实验结果吻合较好。在此基础上,以复合材料防撞梁铺层厚度和铺层角度为设计变量,将复合材料防撞梁的质量和低速冲击时产生的损伤单元数作为优化目标,建立了CFRP防撞梁低速碰撞损伤动力学多目标优化模型,采用基于Pareto策略改进的NSGA-Ⅱ非支配排序遗传算法对其进行多目标优化求解。优化后,CFRP防撞梁质量由1.02 kg降为0.76 kg,减重率为25%;损伤单元数由30238个降为23206个,损伤降低率为23%。结果表明,所发展基于Hashin失效准则的VUSDFLD子程序和对CFRP防撞梁低速碰撞损伤多目标优化是行之有效的,为复合材料防撞梁结构的优化设计提供了参考。

陕西地震行业网优化的实践

陕西地震行业网承担地震数据汇集与转发、地震速报与编目、公共信息服务、地震应急等功能,经过多年的建设运行,已具备一定的安全防御能力。但随着公共网络安全行业的发展变化,陕西地震行业网在网络结构、安全防御、设备配置、机房环境、制度建设等方面存在的不同程度的缺陷与隐患逐一显现。本文以安全等级保护测评、密码应用测评为依据,对陕西地震行业网在重塑网络结构、优化设备配置、健全制度机制、升级机房环境等方面进行了一系列的优化改造,为陕西省地震局网络优化技术升级、运维保障、安全运行、有效管理提供了良好基础。此次陕西地震行业网的优化升级改造经验,可为其他单位及行业网的网络优化升级改造建设提供一定经验借鉴。

复合材料机翼多工况下的自适应优化设计

为获得具有优良气动性能且兼具结构强度及轻量化的复合材料飞机机翼,提出考虑气动分析和结构分析多目标多工况优化方法。分别对机翼进行气动分析及结构强度分析,以机翼展弦比、锥度比、后掠角为几何优化变量,以机翼上下机翼蒙皮的-45°、90°、45°、0°层厚度和夹芯厚度为结构优化变量,建立以应力、位移为约束,以升阻比最大化和质量最小化为目标的协同优化模型。针对复合材料机翼多目标优化设计存在的计算量大难以取舍的问题,提出基于多准则和物理规划的自适应约束Kriging模型多目标优化算法(adaptive constraint kriging model multi-objective optimization algorithm based on multi-criteria and physical programming,AKBCP)。该算法引入了物理规划法和多准则加点,通过测试算例对比分析表明该算法具有较好的优化效果。将该算法应用到机翼多目标优化中,与初始机翼相比,优化后机翼升阻比提高3.12%,质量减轻31%,研究结果可为复合材料机翼优化设计提供参考。

面向供水管网漏损监测的传感器优化布置多准则决策分析方法

供水管网漏损精准监测对于避免水资源浪费具有重要意义,为此,提出一种面向供水管网漏损监测的传感器优化布置多准则决策分析方法。基于充分考虑传感器布置优化的权重、差异阈值和偏好阈值的不确定性,利用信息熵在整个决策空间内对传感器的初始位置进行筛选再根据漏损监测目标,通过定义一组传感器布置优化准则,利用多准则决策分析方法对初始方案进行排序,得到顾及多组参数及不同偏好场景下各种初始方案的概率排序。为了验证该方法的适用性,采用基准测试管网k1模型开展仿真试验并比较了几种不同的偏好场景。结果表明所提出的方法能够适应顺序优先和最重要的优先顺序,而且能够对方案进行排序和成对比较,避免了使用“黑匣子”对传感器布置方案进行选择。

基于多准则决策方法的作战方案优选方法

作战方案评估优选的过程往往存在主观性过强和忽略指标间内部关联关系等问题,这些问题会对作战决策的结果产生一定影响。针对作战决策中作战方案优选的问题,通过结合最优最劣法(BWM)、基于指标相关性的指标权重(CRITIC)确定法、多准则优化妥协决策(VIKOR)和梯形模糊数,提出一种用于作战方案评估优选的决策方法。首先,根据作战方案核心要素构建评估指标体系。然后,综合运用模糊BWM和基于距离相关系数的CRITIC为评估指标综合赋权,根据指标的组合权重信息和评价信息通过VIKOR法对各候选方案进行优选排序。最后,应用该方法解决了防御作战方案优选的实际问题,并通过对比分析和讨论,验证了该文所提作战方案优选决策方法的有效性。

HCO-ADE求解单点交叉口信号配时问题

为克服信号配时问题求解中单一约束优化方法的局限性,提出混合约束优化自适应差分进化算法(HCO-ADE)。以交叉口通行效率为目标建立信号配时优化模型,依据模型中约束条件被满足程度,采用外罚函数法和可行性准则法相结合的混合约束优化策略(HCOS)对约束条件进行处理,并提出饱和度偏好准则作为自适应差分进化算法(ADE)的选择策略,构建HCO-ADE。最后,以哈尔滨市四相位交叉口高峰时段5 min采集标段交通量数据为例,验证HCO-ADE。结果表明:提出的信号配时问题求解方法可行、有效,优于基于饱和度区间可能度的罚函数约束优化自适应差分进化算法(PFCO-ADE),所得信号配时方案较实测方案平均延误减少3.32%,通行能力提高2.16%,能够更好地提高信号配时方案稳定性。