Production Line Capacity Planning Concerning Uncertain Demands for a Class of Manufacturing Systems with Multiple Products

In this paper, we study a class of manufacturing systems which consist of multiple plants and each of the plants has capability of producing multiple distinct products. The production lines of a certain plant may switch between producing different kinds of products in a time-sharing mode. We optimize the capacity configuration of such a system s production lines with the objective to maximize the overall profit in the capacity planning horizon. Uncertain demand is incorporated in the model to achieve a robust configuration solution. The optimization problem is formulated as a nonlinear polynomial stochastic programming problem, which is difficult to be efficiently solved due to demand uncertainties and large search space. We show the NP-hardness of the problem first, and then apply ordinal optimization(OO) method to search for good enough designs with high probability. At lower level, an mixed integer programming(MIP) solving tool is employed to evaluate the performance of a design under given demand profile.

A Multi-agent Based Evaluation Framework and Its Applications

The evaluation system is significant for assessment of technologies, experiments, energy cost and effectiveness, especially for the complicated engineering systems, such as energy systems, weapon systems and spacecraft systems. However, as engineering systems become more and more distributed and heterogeneous, evaluation frameworks need to be more universal,distributed and interactive. In this paper, we compare several typical evaluation frameworks and propose a novel evaluation framework based on multi-agent technology. We provide two case studies, indoor comfort system and technology assessment of spacecraft systems, respectively. The results show that the proposed framework can work efficiently.

Using knowledge inference to suppress the lamp disturbance for fire detection

Fire detection in buildings is crucial for people’s lives and property.Conventional temperature and smoke sen-sors have many disadvantages:the limited cover range;detection delays;the difficulty in distinguishing smoke and fire.Recently,research on convolutional neural networks(CNN)for fire image detection has become a hot topic.However,existing fire classification and object detection methods are often interfered with by flash-lights,red objects and the high-brightness background,resulting in a high false alarm rate.Besides,light and lamps often exist in buildings.To address this issue,this paper focuses on introducing scene prior knowledge and causal inference mechanisms to suppress the lamp disturbance.Firstly,we train the YoloV3 network to detect and recognize lamps.Secondly,to reduce the dataset bias,we mask the lamp regions with the pro-posed Local Grabcut segmentation method.Last,compared with direct fire classification methods,our proposed methods reduce about 34.6%false alarm rate based on InceptionV4 networks.The experimental results verify the effectiveness among different CNN architectures(Resnet101,Firenet,Densenet121).The code is online at https://github.com/kailaisun/fire-detection-without-lamp.

ENERGY-EFFICIENT SENSING COVERAGE AND COMMUNICATION FOR WIRELESS SENSOR NETWORKS

无线传感器网络有大量应用程序。察觉到范围和通讯范围是服务的二基本质量。在这篇论文，我们在场我们精力上的工作有效察觉到范围和通讯。我们为分别地察觉到范围题目到不同要求和限制设计几个计划。我们也建议基于小世界网络理论为大规模传感器网络与高精力效率和低潜伏扔通讯协议的国外。模拟和实验结果证明我们的计划和协议有好性能。

Remanufacturing planning based on constrained ordinal optimization

Resource planning for a remanufacturing system is in general extremely difficult in terms of problem size,uncertainties,complicated constraints,etc.In this paper,we present a new method based on constrained ordinal optimization(COO)for remanufacturing planning.The key idea of our method is to estimate the feasibility of plans by machine learning and to select a subset with the estimated feasibility based on the procedure of horse racing with feasibility model(HRFM).Numerical testing shows that our method is efficient and effective for selecting good plans with high probability.It is thus a scalable optimization method for large scale remanufacturing planning problems with complicated stochastic constraints.

Complexity of comparing expressions in max-plus algebra

Max-plus algebra has been widely used in the study of discrete-event dynamic systems.Using max-plus algebra makes it easy to specify safety constraints on events since they can be described as a set of inequalities of state variables,i.e.,firing times of relevant events.This paper proves that the problem of solving max-plus inequalities in a cube(MAXINEQ)is nondeterministic polynomial-time hard(NP-hard)in strong sense and the problem of verifying max-plus inequalities(VERMAXINEQ)is co-NP.As a corollary,the problem of solving a system of multivariate max-algebraic polynomial equalities and inequalities(MPEI)is shown to be NP-hard in strong sense.The results indicate the difficulties in comparing max-plus formulas in general.Problem structures of specific systems have to be explored to enable the development of efficient algorithms.

Research opportunities arising from control and optimization of smart buildings

Control and optimization plays an important role in automation science and engineering for buildings. The area is important due to the growing threat of global warming effects to the sustainable development of human society as a whole indicated by the Paris Climate Treaty and the fact the buildings account for about 40% of total energy consumption in US, for example [1].