Corporate financial decision-makers' perceptions of workplace safety

This study, through a random national survey, explored how senior financial executives or managers (those who determined high-level budget, resource allocation, and corporate priorities) of medium-to-large companies perceive important workplace safety issues. The three top-rated safety priorities in resource allocation reported by the participants (overexertion, repetitive motion, and bodily reaction) were consistent with the top three perceived causes of workers' compensation losses. The greatest single safety concerns reported were overexertion, repetitive motion, highway accidents, falling on the same level and bodily reaction. A majority of participants believed that the indirect costs associated with workplace injury were higher than the direct costs. Our participants believed that money spent improving workplace safety would have significant returns. The perceived top benefits of an effective workplace safety program were increased productivity, reduced cost, retention, and increased satisfaction among employees. The perceived most important safety modification was safety training. The top reasons senior financial executives gave for believing their safety programs were better than those at other companies were that their companies paid more attention to and emphasized safety, they had better classes and training focused on safety, and they had teams/individuals focused specifically on safety.     

A study on manufacturing of aluminum automotive piston by thixoforging

The manufacture of many products related to the automobile and airplane industries by semi-solid forming is nowadays increasing from environmental and economical points of view. Especially in the automobile industry, to upgrade engine capability and reduce engine weight, the aluminum engine piston has already been developed by researchers and industrial technicians. Currently, forming methods to manufacture engine piston are casting, squeeze casting and hot forging. These methods, however, include several casting defects and need post machining as well. In this study, aluminum engine piston will be manufactured by thixoforging according to forming variables. In addition, it is very important to find the effects of forming variables on the final product by thixoforging. In order to determine the effects, however, researchers and industrial technicians have depended upon many experimental trials. In this study, a statistical approach for thixoforging has been adopted to improve experimental efficiency. Forming variables such as initial solid fraction, die temperature, and compression holding time were considered for manufacturing aluminum engine piston by thixoforging. Hardness and microstructure characterization were carried out so that the optimal forming condition could be found by the statistical approach. The experiment to make aluminum engine piston was performed under the optimal condition found by the statistical approach.     

Fuzzy optimization of units products in mix-product selection problem using fuzzy linear programming approach

In this paper, the modified S-curve membership function methodology is used in a real life industrial problem of mix product selection. This problem occurs in the production planning management where by a decision maker plays important role in making decision in an uncertain environment. As analysts, we try to find a good enough solution for the decision maker to make a final decision. An industrial application of fuzzy linear programming (FLP) through the S-curve membership function has been investigated using a set of real life data collected from a Chocolate Manufacturing Company. The problem of fuzzy product mix selection has been defined. The objective of this paper is to find an optimal units of products with higher level of satisfaction with vagueness as a key factor. Since there are several decisions that were to be taken, a table for optimal units of products respect to vagueness and degree of satisfaction has been defined to identify the solution with higher level of units of products and with a higher degree of satisfaction. The fuzzy outcome shows that higher units of products need not lead to higher degree of satisfaction. The findings of this work indicates that the optimal decision is depend on vagueness factor in the fuzzy system of mix product selection problem. Further more the high level of units of products obtained when the vagueness is low.     

Integrated Management of a Canal Command in a River Delta using Multi-Objective Techniques

Multi-criteria or multi-objective decision-making is becoming increasingly popular as a decision support tool for natural resource management.Stakeholders as well as the planners can be involved in the decision making process, using this approach. This article deals with the use of multi-criteria (multi-objective) technique in solving some complex problems related to water resource management. Five objectives were considered in the study. The benefit of combining these objective functions with the decisionsupport tool is that the management of land and water resourcescan be made more effectively. Based on this concept, a methodology was developed through this study, for the water managers and decision-makers, to obtain a compromising solutionin terms of area allocated under different crops and the magnitude of farming system variables in a canal command area. This study was under taken in the Mahanadi Delta of India. Multi-objective techniques such as Sequential Linear Fuzzy Programming and Goal Programming were used for their simplicity in computation and flexibility in application. Using Fuzzy programming technique, the objective function values under benefit maximization, production maximization, investmentminimization, labour maximization and labour minimizationwere found to be 44.26 M INR, 8795 tonnes, 42.00 M INR and548 150 man-days, respectively. These results were found tobe quite compromising in nature. Goal programming technique wasalso used to arrive at a consensus in allocation of the resources. It was used to decide the best out of the eight alternative priorities. Results indicated that only five alternative goals (Goal1, Goal2, Goal3, Goal6 and Goal8) had distinct allocations while the other three alternatives (Goal4,Goal5 and Goal7) had allocations similar to either of the abovefive alternatives irrespective of their priority levels. Croppingintensity was found to be the maximum (238%) for two of thegoals (Goal6 and Goal7). Though the results of the study were forthe specific site, the multi-criteria techniques used and therecommendations evolved are of objective nature and are applicable at any location for decision-making.     

大型复杂项目建筑策划“群决策”的计算机数据分析方法研究

在分析既有建筑策划方法的基础上探索了新型的计算机数据分析决策方法,关注如何科学定量地将各利益群体的多个不同意见统合成为一个群体意见,并将此计算机分析方法应用于上海虹桥综合交通枢纽,对其原有策划内容进行评估性实验.     

Evolutionary risk preference inference model using fuzzy support vector machine for road slope collapse prediction

Road slope collapse events are frequent occurrences in Taiwan, often exacerbated by earthquakes and/or heavy rainfall. Such collapses disrupt transportation, damage infrastructure and property, and may cause injuries and fatalities. While significant efforts are regularly invested in reducing road slope collapse risk, most focus exclusively on limiting the potential for slope failure. Collapse prediction efforts may result in inference errors that cause allocated road slope maintenance resources to be expended inefficiently, resulting in relatively higher collapse risk than should be achievable under ideal circumstances. Most maintenance programs rely on decision maker risk preferences, as his/her knowledge and experience can contribute to risk assessment decision making. The decision maker is capable of choosing an acceptable balance between two types of inference error, i.e., α and β errors. This preference may later be used as guidance to minimize inference error. This paper proposed the evolutionary risk preference fuzzy support vector machine inference model (ERP-FSIM) as a hybrid AI system able to make predictions regarding road slope collapse that takes decision maker risk preference into account. Validation results demonstrate ERP-FSIM viability, as level of average error both for the training set and validation set conform to the decision maker risk preference ratio and is significantly lower than the error tolerance of ±10%.     

Extension of TOPSIS to determine weight of decision maker for group decision making problems with uncertain information

In traditional TOPSIS method, the ideal solutions for alternatives are expressed in vectors. An important step in the process of group decision making is to determine the relative importance of each decision maker. In this paper, the weights of decision makers derived from individual decision are determined by using an extended TOPSIS method with interval numbers. The ideal decisions for all individual decisions are expressed in matrices. The positive ideal decision is the intersection of all individual decisions; the negative ideal decision is the union of all individual decisions. Comparisons with other methods are also made. A numerical example is examined to show the potential applications of the proposed method.     

Intelligent Multi-Objective Nonlinear Model Predictive Control (iMO-NMPC): Towards the ‘on-line’ optimization of highly complex control problems

The benefits of using the Nonlinear Model Predictive Control (NMPC) for the response optimization of highly complex controlled plants are well known. Nevertheless the complexity and associated high computational cost make its implementation and reliability the focus of the discussion. This paper proposes an Intelligent and Multi-Objective NMPC (iMO-NMPC) scheme where several, and often conflicting, control objectives can be competing simultaneously in the control problem. In the iMO-NMPC, the combination of a Neural Network, a Multi-Objective Genetic Algorithm and a Fuzzy Inference System, help us in the nonlinear search for near-optimal control actions, aiming to fulfil all the specified control objectives simultaneously. The proposed scheme adds an expert stage that can adaptively change the degree of importance (weight) of each control objective according to the state of the plant. Therefore, once the nonlinear multi-objective optimization problem is solved at each sampling time and the non-inferior control solutions belonging to the set of Pareto are obtained, the most appropriate one is selected by using the control objectives weights inferred from the expert stage. Some experimental results showing the iMO-NMPC effectiveness and the details about its implementation over control systems with low sampling times are also presented and discussed in this paper.     

一种基于前景理论的三参数区间灰数型群体灰靶决策方法

考虑决策者关于指标满意域和风险态度对群体决策的影响, 提出基于前景理论的三参数区间灰数型群体灰靶决策方法. 该方法利用“奖优罚劣”的三参数区间灰数线性变换算子对原始决策信息进行规范化处理, 能够充分反映决策值是否中靶. 在此基础上, 以规范化区间的零点作为参考点, 定义前景价值函数. 依据群体一致性和极大熵原理构建决策者权重模型, 根据综合前景值的正负判断方案是否中靶. 最后, 通过实例表明了所提出方法的有效性和优越性.     

基于引力搜索算法的含分布式电源配电网重构

分布式电源接入配电网将直接改变潮流分布,而网络重构通过改变开关的开合状态以提高供电的可靠性和经济性。以降低网损和缺供电量为目标,以可调度DG的有功输出和开关状态为优化变量,建立多目标协调优化的配电网络重构的数学模型。将引力搜索算法、Pareto最优解理论和模糊决策理论相结合,提出了求解上述模型的多目标混合优化方法,以实现配电网结构和DG出力的协同优化。通过对IEEE33节点测试系统的仿真结果,表明所提出方法能够实现DG出力和网络重构的综合优化和多目标优化,并且在全局寻优能力和收敛速度上表现出色。