区间规划下的高残值易逝品供应链网络设计

从闭环供应链的核心企业出发,基于高残值易逝性产品生命周期理论,构建了不确定环境下的双渠道闭环供应链网络模型。采用了区间机会约束方法处理模型中的参数不确定性,分析不同置信水平条件下的目标值,发现该模型具有一定的稳健性。模型仿真结果能为决策者在设施选址、产品定价、回收时间、产品销量等方面提供参考。     

Quality: Philosophy and practice: Leslie Polgar of Bertram Labs in conversation with Roy Szweda

New Jersey based Bertram Labs, a small wafer company, has lately increased its, market share significantly, at multiples of the market growth rate, and while its primary customers are in the USA, it already exports over a third of its production to Europe and Asia. TFR spoke to Dr. Leslie Polgar, VP at Bertram Labs, who was keen to share his observations on the needs of supplier and customer in terms of quality, standards and pricing.     

A flow allocation strategy for routing over multiple flow classes with an application to air cargo terminals

Advances of information technology have enabled the utilization of automated material handling systems in the logistics industry. The increasing costs of labor in developing countries have accelerated this trend. Major cargo terminals are now installing more and more integrated automated shipment handling systems in order to increase their operational efficiency which can be measured by the average shipping times or the facility throughput, for example. Routing is clearly an important decision category that has significant impact on the operational efficiency. In this paper, motivated by a project with one of the busiest air cargo terminals in the world, we investigate a routing optimization problem for multiple flow classes with different levels of priority. We propose a flow allocation (FA) routing strategy in which when a shipment arrives at a decision point, a set of allocation ratios will be employed to direct it to the next location. These ratios are determined by solving a mathematical model that explicitly considers the congestion effect and the characteristics of the multi-commodity network. Comprehensive simulation experiments demonstrate that the proposed FA routing strategy significantly outperforms the one currently in use.     

Post-seismic supply chain risk management: A system dynamics disruption analysis approach for inventory and logistics planning

Post-seismic inventory and logistics planning under incomplete and fuzzy information is an important yet understudied area in supply chain risk management. The goal of this paper is to propose a system dynamics model to analyze the behaviors of disrupted disaster relief supply chain by simulating the uncertainties associated with predicting post-seismic road network and delayed information. The simulation results indicate: (1) information delay has different influences over the relief head-quarter (the upstream) and the disaster-affected areas (the downstream); and (2) the change of road conditions and shipment schedules have impact on the on-time transportation rate in supply chain management. Furthermore, this paper defined and tested supplies' replenishment solutions combined with three inventory planning strategies and four forecasting methods under different lead time uncertainties. The results show that: (1) a strategy that considers information from both the post-seismic management center and the affected areas can provide a better logistic plan than an one takes information from one side; (2) the smooth-the-trend forecasting method is suitable for inventory and logistic planning when the post-seismic situations are volatile, while the quick-response forecasting method has good performance in stable environments. In addition, this paper proposes decision tree to help decision makers choose the appropriate stocking strategies.     

Fault detection in dynamic systems using the Kullback–Leibler divergence

This paper proposes detecting incipient fault conditions in complex dynamic systems using the Kullback–Leibler or KL divergence. Subspace identification is used to identify dynamic models and the KL divergence examines changes in probability density functions between a reference set and online data. Gaussian distributed process variables produce a simple form of the KL divergence. Non-Gaussian distributed process variables require the use of a density-ratio estimation to compute the KL divergence. Applications to recorded data from a gearbox and two distillation processes confirm the increased sensitivity of the proposed approach to detect incipient faults compared to the dynamic monitoring approach based on principal component analysis and the statistical local approach.     

广州市市售水产品氯霉素、孔雀石绿、呋喃唑酮和甲醛残留量分析

目的调查2018～2019年广州市市售水产品质量与安全现状,分析水产品氯霉素、孔雀石绿、呋喃唑酮和甲醛残留量。方法依据胶体金快速检测法对水产品氯霉素、孔雀石绿、呋喃唑酮残留量进行定性检测,依据SC/T 3025-2006《中华人民共和国水产行业标准水产品中甲醛的测定》(第一法分光光度法)并结合文献方法对水产品中甲醛进行定性和定量检测。结果 200件水产品共有30件的样品检出有违禁药物的残留,总检出率为15.0%,其中氯霉素检出5件,检出率为2.5%,孔雀石绿检出3件,检出率为1.5%,呋喃唑酮检出10件,检出率为5.0%,甲醛检出13件,检出率为6.5%,另外,有1件的样品同时检出氯霉素与孔雀石绿的残留量,其中呋喃唑酮检出率最高的主要集中在鱼类和虾类。结论部分水产品存在氯霉素、孔雀石绿、呋喃唑酮和甲醛污染现象,有关部门应该加强对水产品养殖、运输和销售过程中的监督管理。     

Decomposition of intensity of energy-related CO2 emission in Chinese provinces using the LMDI method

Uncovering the driving factors of CO₂ emission intensity declining is important for China. This paper improves the logarithmic mean Divisia index technique, which includes energy density and energy consumption intensity, to explore the driving factors of carbon emission intensity (CI) in 29 Chinese provinces from 1995–2012. The main results are: (1) energy consumption intensity plays a more important role than carbon emission density (CD) for a rapid decrease in CI during the research period, so a much room is left for a significant CD reduction through carbon emission reduction technology, energy structural reduction, and energy consumption proportional reduction. (2) The decrease in energy consumption technology and energy structure in secondary industries contributes the most reduction in energy consumption intensity. (3)The energy consumption proportions of secondary and tertiary industries are the two most important drivers to decrease CD. (4) During the research period, the energy consumption proportions of secondary industries result in the most decrease in CD, whereas the energy consumption proportions of tertiary industries cause the most increase in CD.     

Energy paradox and political intervention: A stochastic model for the case of electrical equipments

This paper develops a model that explains the delay of decisions to adopt profitable energy-saving investments. This problem is known as the energy paradox. The model rationalizes the profitability requirements raised by the irreversibility, the uncertainty and the decrease of costs as a result of learning by doing. In this context, the wait gives investors more visibility and more lower investment costs, which gives them an option value. The representative agent has an interest to postpone its energy saving decision until future benefits increase and equalize its required option value. Formally, we internalize these explanatory factors in a stochastic model where the updated energy saving benefits follows a geometric Brownian motion. To affirm the capacity of the model, we generate simulation results for two equipments for electrical uses. Beyond that, we extend the model to simulate the effects of energy policy instruments to promote adoption of such equipments. Simulations prove that the taxation of energy prices is likely to be more effective than the subsidy for energy-saving equipments. It is also found that the combination of these instruments amplifies the adoption of energy-saving equipments and generates very favorable economic and environmental externalities.     

SHOMAS: Intelligent guidance and suggestions in shopping centres

This paper introduces the SHOMAS Multiagent System that provides guidance on leisure facilities and suggestions for shopping in malls. The multiagent architecture incorporates reactive and deliberative agents that take decisions automatically. The developed deliberative agent provides suggestions in execution time, with the help of case-based planners. This agent is described together with its guidance and suggestion mechanism. SHOMAS has been tested successfully, and the results obtained are presented in this paper.     

A simulation-based approach for dynamic process management at web service platforms

Web services technology is being adopted as a viable deployment approach for future distributed software systems that enable business-to-business and business-to-consumer interactions across the open and dynamic internet environment. Recent research is focused on developing support technologies for web service discovery, on-demand service composition, and robust execution to facilitate web services based deployment of business processes. Developing techniques to cope with the volatile and open nature of the web during execution of composite services at the service platform is essential for delivering reliable and acceptable performance in this new process delivery framework. In this paper, we propose a simulation-based framework to guide scheduling of composite service execution. Online simulation of the dynamics of the open environment is used for scheduling service requests at the service platform. Comparison of the look-ahead simulation for different scheduling policies with the current execution state provides guidelines for service execution in order to cope with system volatility. We have implemented a prototype of the proposed framework and illustrate the feasibility of our approach with experimental studies.