Identification of managerial shaping factors in a petrochemical plant by resilience engineering and data envelopment analysis

Resilience engineering (RE) is a novel approach that is capable of controlling and limiting incidents and accidents. This study identifies managerial shaping factors in a petrochemical plant by RE and data envelopment analysis (DEA). To do this, a standard questionnaire containing resilient factors is completed by managers of a petrochemical plant. Then, the best DEA model is selected based on average efficiency and statistical test. In addition, sensitivity analysis are performed to identify the most important shaping factors. Reporting culture, management commitment, and preparedness are identified as the most important factors in this paper, respectively. Finally, the proposed model is validated and verified through statistical experiment. The proposed approach would help managers to have a comprehensive understanding of the plant with respect to the RE. To the best of our knowledge this is the first paper that examines resilient shaping factors in a petrochemical plant with respect to management and organization by DEA.     

Assessing the efficiency of Chilean water and sewerage companies accounting for uncertainty

Efficiency assessment of water and sewerage companies (WaSCs) has attracted considerable attention both for water company managers and water regulators. Within the methodological approaches that can be applied to estimate efficiency scores, data envelopment analysis (DEA) is the most widely applied technique. In spite of the positive features of DEA, it presents a major drawback which is its deterministic nature. In other words, conventional DEA models do not account for uncertainty in the data. To overcome this limitation, we assess, for the first time, the efficiency of a sample of Chilean WaSCs by using a DEA model with statistical tolerance in the data. Hence, 81 efficiency scores are estimated for each WaSC rather than a single score as with conventional DEA models. The results illustrate that outputs exhibit larger uncertainty than inputs. Moreover, WaSCs efficiency scores change significantly under the best-case and worst-case scenarios. The ranking of the WaSCs allows for the identification of which of them has the highest performance based on their efficiency scores. This information is essential to enhance efficiency and innovation in the water industry. Moreover, the introduction of uncertainty in the efficiency assessment allows for the prediction and ranking of future performance of WaSCs.     

Measuring and analyzing adaptive capacity at management levels of resilient systems

Resilience engineering (RE) is capable of handling disruptive events and controlling their consequences in process industries such as petrochemical plants. This study aims at analyzing the level of adaptive capacity and identifying effective factors on developing adaptive capacity in the organizational structure of process industries. The data of this study were obtained through direct observation and a structured questionnaire in a petrochemical plant. Managers at all levels participated in the survey. Data envelopment analysis (DEA), which is a mathematical approach, was used to compute and analyze the role of the factors contributing to adaptive capacity. The results indicated that reporting safety issues played a central role in enhancing adaptive capacity at all management levels. Both middle management and low-level management emphasized the importance of management commitment, whereas top-level management considered flexibility as a vital factor in managing disruptions and reducing accidents. The findings of this study could be useful for managers and other decision-makers to improve safety in process industries.     

绿色经济视角下环渤海经济圈经济效率的再评价——基于熵权法和DEA—Malmquist方法

长期以传统GDP作为衡量经济发展的单一评价指标,造成了一系列环境问题,已不能满足科学发展观的要求。以绿色经济为视角,通过计量方法重新计算包含环境成本的相对绿色GDP,发现环渤海地区除北京市以外,在经济总量扩张的同时环境压力不断增加。将计算得到的相对绿色GDP作为DEA—Malmquist模型的产出指标,重新评价环渤海地区的经济效率,发现新的效率值明显低于传统GDP下的经济效率,并有不断下降的趋势。     

基于DEA模型的太湖流域企业污水治理效率

运用数据包络分析DEA方法,建立了太湖殷村港流域工业园区企业污水治理效率评价体系,并利用该体系对园区内企业污水治理效率进行了实证研究。利用效率评价函数对园区内20家企业进行评价,结果表明,其中4家企业达到环境治理效率相对高效;20家企业的废水排放量、COD和NH3-N排放削减总量分别为3 691 860、217.53和14.64 t/a,削减潜力较大。实证研究结果表明,该环境绩效评价系统可为园区企业污水治理调控提出定量化的调整建议。     

基于DEA的我国能源R＆D经费配置评价研究

在对我国能源R＆D强度分析的基础上,运用DEA理论的C^2R模型对我国能源产业及其5个行业2001--2010年的R＆D经费投入、产出状况进行了实证分析。研究中用R＆D经费存量值来代替当年的R＆D经费值,得到各年的R＆D经费配置的相对效率和规模收益情况,找出R＆D经费配置配置存在的问题,有利于对我国能源R＆D经费的投人产出结构进行分析,提出优化国家能源R＆D经费配置的对策和建议。     

欠发达地区农户生产效率研究——以西藏自治区为例

以西藏自治区为例,在2009年农户抽样调查数据基础上,利用数据包络法(DEA)探讨西藏不同区域农户生产效率,认为西藏农户生产效率差异与农业生产中自用价值的计算有关,若考虑自用价值,在固定生产规模报酬(CRS)假设下,平均生产效率为0.653,其中拉萨市最高(0.784),日喀则市最低(0.277);若不计入自用价值,山南与那曲地区呈现最高生产效率。表明西藏农户收入中自用价值占了较大比例,扣除自用价值,收入大幅减少,影响生产效率。因此,提高农牧户生产商品率是增加农牧民收入的有效途径。     

应急避难场所建设进度优化模型研究

以规划效率评价为基础,定义了应急避难场所的公益收益,体现在服务性、可达性和安全性3个方面。以全部应急避难场所在整个建设规划期的总公益收益最大化为目标,建立了无偏好和有偏好两类建设进度优化模型,对应急避难场所在规划期内各年的投资额度、建设对象和建设数量进行优化。案例研究结果表明,无偏好模型能最大限度地获得公益收益,但不能很好地表达公众的真实需求;有偏好模型,不可避免会造成一定公益收益损失,但能更多地考虑实际存在的各方面影响因素,更接近真实的决策。实际应用中对模型的选择应根据具体情况确定。     

长三角地区石油和化工行业上市公司绿色竞争力的实证研究

企业绿色竞争力是企业在“绿色化”的前提下，与对手相比的一种竞争优势。石油和化工行业是能源和资源的生产和消耗大户．也是众所周知的重污染行业．对石油和化工企业的绿色竞争力进行实证研究具有重要意义。本文以长三角地区的石油和化工行业上市公司为案例。构建基于DEA法的绿色竞争力评价模型．评估了它们的绿色竞争力。实证结果显示：长三角地区石油和化工企业的绿色竞争力还有待加强．在此基础上提出了石油和化工企业提高绿色竞争力的一些具体措施。     

中国城市污水处理厂资源配置效率的比较

采用平均生产函数模型、边界生产函数以及数据包络分析(DEA)模型3种方法,对中国81个污水处理厂的样本观测数据,从不同角度进行了比较研究,分别得出上述3种模型对产出弹性、技术效率和规模经济性等主要指标的模拟结果.研究发现,资本对我国城市污水处理厂的产出弹性最大(0.507),其次是运营电耗(0.415),再次为劳动力;我国污水厂普遍处于规模报酬率递增阶段,样本总体的规模报酬率为1.11;根据DEA的分析结果,列出了81个样本污水厂的效率排序清单,这将有助于制定行业绩效标准.