A hybrid approach to configure eco-efficient supply chains under consideration of performance and risk aspects

Formal models that support multi-criteria decision making represent a strongly growing area in sustainable supply chain management research. However, uncertainties and risks are seldom considered in quantitative models for green supply chain (SC) design. The paper at hand suggests a hybrid approach to configure an eco-efficient SC for a new product under consideration of economic and environmental risks. Discrete-event simulation is applied to assess the financial, operational and environmental performance of different SC configuration options while the value-at-risk concept is adapted to evaluate related SC risks. The analytic hierarchy process is employed to solve the resulting multi-criteria decision problem of choosing the best option. The approach is illustrated at a case example of a fast moving consumer goods manufacturer.     

A multicriteria sorting approach based on data envelopment analysis for R&D project selection problem

In this paper, multiple criteria sorting methods based on data envelopment analysis (DEA) are developed to evaluate research and development (R&D) projects. The weight intervals of the criteria are obtained from Interval Analytic Hierarchy Process and employed as the assurance region constraints of models. Based on data envelopment analysis, two threshold estimation models, and five assignment models are developed for sorting. In addition to sorting, these models also provide ranking of the projects. The developed approach and the well-known sorting method UTADIS are applied to a real case study to analyze the R&D projects proposed to a grant program executed by a government funding agency in 2009. A five level R&D project selection criteria hierarchy and an assisting point allocation guide are defined to measure and quantify the performance of the projects. In the case study, the developed methods are observed to be more stable than UTADIS.     

The Use of Simulation to Reduce the Domain of “Black Swans” with Application to Hurricane Impacts to Power Systems

Recently, the concept of black swans has gained increased attention in the fields of risk assessment and risk management. Different types of black swans have been suggested, distinguishing between unknown unknowns (nothing in the past can convincingly point to its occurrence), unknown knowns (known to some, but not to relevant analysts), or known knowns where the probability of occurrence is judged as negligible. Traditional risk assessments have been questioned, as their standard probabilistic methods may not be capable of predicting or even identifying these rare and extreme events, thus creating a source of possible black swans. In this article, we show how a simulation model can be used to identify previously unknown potentially extreme events that if not identified and treated could occur as black swans. We show that by manipulating a verified and validated model used to predict the impacts of hazards on a system of interest, we can identify hazard conditions not previously experienced that could lead to impacts much larger than any previous level of impact. This makes these potential black swan events known and allows risk managers to more fully consider them. We demonstrate this method using a model developed to evaluate the effect of hurricanes on energy systems in the United States; we identify hurricanes with potentially extreme impacts, storms well beyond what the historic record suggests is possible in terms of impacts.     

Construction Safety Risk Modeling and Simulation

By building on a genetic‐inspired attribute‐based conceptual framework for safety risk analysis, we propose a novel approach to define, model, and simulate univariate and bivariate construction safety risk at the situational level. Our fully data‐driven techniques provide construction practitioners and academicians with an easy and automated way of getting valuable empirical insights from attribute‐based data extracted from unstructured textual injury reports. By applying our methodology on a data set of 814 injury reports, we first show the frequency‐magnitude distribution of construction safety risk to be very similar to that of many natural phenomena such as precipitation or earthquakes. Motivated by this observation, and drawing on state‐of‐the‐art techniques in hydroclimatology and insurance, we then introduce univariate and bivariate nonparametric stochastic safety risk generators based on kernel density estimators and copulas. These generators enable the user to produce large numbers of synthetic safety risk values faithful to the original data, allowing safety‐related decision making under uncertainty to be grounded on extensive empirical evidence. One of the implications of our study is that like natural phenomena, construction safety may benefit from being studied quantitatively by leveraging empirical data rather than strictly being approached through a managerial perspective using subjective data, which is the current industry standard. Finally, a side but interesting finding is that in our data set, attributes related to high energy levels (e.g., machinery, hazardous substance) and to human error (e.g., improper security of tools) emerge as strong risk shapers.     

Data-driven operations management: organisational implications of the digital transformation in industrial practice

Abstract

The ongoing digital transformation on industry has so far mostly been studied from the perspective of cyber-physical systems solutions as drivers of change. In this paper, we turn the focus to the changes in data management resulting from the introduction of new digital technologies in industry. So far, data processing activities in operations management have usually been organised according to the existing business structures inside and in-between companies. With increasing importance of Big Data in the context of the digital transformation, the opposite will be the case: business structures will evolve based on the potential to develop value streams offered on the basis of new data processing solutions. Based on a review of the extant literature, we identify the general different fields of action for operations management related to data processing. In particular, we explore the impact of Big Data on industrial operations and its organisational implications.     

大型医院血液库存系统订货点决策的仿真研究

血液是一种典型的易变质物品。本文以国内某大型医院的血液库存系统为对象,依照其运作流程,基于该系统运行的历史数据建立了一个离散事件系统仿真模型对这一系统进行研究,得到了该系统的最优订货点。本文的研究有助于血液库存管理措施的制定与改进,对其它易变质物品库存系统的管理也有一定的借鉴作用。     

企业管理信息系统与P3、P3e/c数据交换的研究与实现

本文首先讨论P3、P3e/c项目管理软件与企业管理信息系统集成的必要性和重要性,然后详细地介绍了采用RA引擎(OLE对象)技术来实现P3软件与企业管理信息系统的数据交换,以及与P3e/c交互的原理。分析P3、P3e/c在数据组织上的差异,提出解决方案。最后以实例讨论和分析了这种方法在实际系统中的应用。     

制造业项目结构分解过程模型研究

针对项目分解中工作分解结构方法的不足,以制造业项目为例,考虑项目产品本身的构造及特点的基础上,综合项目产品的工艺装配过程、产品数据管理技术以及项目组织结构,构建了项目结构分解的过程模型,并对该模型进行了详细分析,应用该模型能够更加科学、合理地进行项目分解,确定项目范围。     

模糊车辆路径问题的一种混合遗传算法

在对模糊车辆路径问题进行简单描述的基础上,通过引入决策者主观偏好值的概念,给出了解决该问题的基本思路,建立了具有模糊特征的车辆路径问题的模糊机会规划模型,提出了求解该问题的一种基于模糊模拟的混合遗传算法。同时,在最小化总行驶距离的目标下,通过随机模拟方法研究了决策者主观偏好值的选择对最终决策目标的影响作用,并给出了其最佳取值范围。     

重尾索赔条件下现代风险模型的破产概率估计:多险种混合情形

破产概率是非寿险保险风险理论的核心问题。与经典的Cramér-Lundberg模型相比, 由Li Zehui等建立的现代风险模型更为准确地描述了非寿险保险运营的主要特征, 对现实保险业务具有较好的解释力。本文基于现代风险模型, 考虑保险公司多个险种混合经营这一更为现实的情形, 在索赔额服从正则尾分布条件下获得了破产概率的渐近等价估计。我们发现, 在具有大额索赔特征的多个险种混合的条件下, 公司面临的极端索赔风险将由索赔额分布尾部最厚的那些险种决定, 而索赔额分布尾部相对较薄的那些险种的影响作用将被淹没。该结论的有效性可用MATLAB数值模拟得到理想的验证。本文结果是对风险模型研究的重要推广, 也为多险种混合情形下保险公司的风险控制与初始保证金界定提供了依据。