Seismic response analysis of damper-connected adjacent structures with stochastic parameters

Dynamic response analysis of damper connected adjacent multi-story structures with uncertain parameters is carried out.A formula of the multi degree of freedom(MDOF) for the structure-damper system with stochastic parameters is derived.The uncertainties of mass and stiffness are taken into consideration firstly.The ground acceleration is represented by Kanai-Tajimi filtered non-stationary process.The mean square random responses of structural displacement and story drift are chosen as the optimization objective.The variations of mean square responses of top floor displacements and bottom story drifts in neighboring structures with the damper stiffness and damping coefficient are analyzed in detail.Through the parametric study,the acquiring optimum parameters of damper are regarded as numerical results.Then,a reducing order model of the MDOF system for adjacent structures with mean parameters is presented.The explicit expressions for determining optimal parameters of Kelvin model-defined damper which is used to connect adjacent single degree of freedom(SDOF) structures subjected to a white-noise excitation are employed to achieve the appropriate damper parameters,which are called theory results.Through a comparative study,it can be found that the theory values of damper parameters are consistent with the results based on extensive parametric studies.The analytical results can be obtained by using the first natural frequencies and the total mass of the adjacent deterministic structures with mean parameters.The analytical formulas can be used to find appropriate parameters of damper between adjacent structures for engineering applications.The performance of damper is investigated on the basis of mitigations of mean square random responses of inter-story drifts,displacements and accelerations in adjacent structures.The numerical results demonstrate the robustness of coupled building control strategies.

Optimization and Operations Research in Mitigation of a Pandemic

The pandemic of COVID-19 initiated in 2019 and spread all over the world in 2020 has caused significant damages to the human society,making troubles to all aspects of our daily life.Facing the serious outbreak of the virus,we consider possible solutions from the perspectives of both governments and enterprises.Particularly,this paper discusses several applications of supply chain management,public resource allocation,and pandemic prevention using optimization and machine learning methods.Some useful insights in mitigating the pandemic and economy reopening are provided at the end of this paper.These insights might help governments to reduce the severity of the current pandemic and prevent the next round of outbreak.They may also improve companies'reactions to the increasing uncertainties appearing in the business operations.Although the coronavirus imposes challenges to the entire society at the moment,we are confident to develop new techniques to prevent and eradicate the disease.

An Improved Algorithm for Fixed-Hub Single Allocation Problems

This paper discusses the fixed-hub single allocation problem(FHSAP).In this problem,a network consists of hub nodes and terminal nodes.Hubs are fixed and fully connected;each terminal node is assigned to a single hub which routes all its traffic.The goal is to minimize the cost of routing the traffic in the network.In this paper,we propose a new linear programming(LP)relaxation for this problem by incorporating a set of validity constraints into the classical formulations by Ernst and Krishnamoorthy(Locat Sci 4:139–154,Ann Op Res 86:141–159).A geometric rounding algorithm is then used to obtain an integral solution from the fractional solution.We show that by incorporating the validity constraints,the strengthened LP often provides much tighter upper bounds than the previous methods with a little more computational effort and the solution obtained often has a much smaller gap with the optimal solution.We also formulate a robust version of the FHSAP and show that it can guard against data uncertainty with little costs.

A Gradient Descent Method for Estimating the Markov Chain Choice Model

In this paper,we propose a gradient descent method to estimate the parameters in a Markov chain choice model.Particularly,we derive closed-form formula for the gradient of the log-likelihood function and show the convergence of the algorithm.Numerical experiments verify the efficiency of our approach by comparing with the expectation-maximization algorithm.We show that the similar result can be extended to a more general case that one does not have observation of the no-purchase data.

Preface:Special Issue on Optimization Algorithms and Applications

Optimization is one of the fundamental and essential components of operations research,a highly interdisciplinary subject.As one of the first researchers of the interior-point methods,Professor Yin-Yu Ye is responsible not only for developing many fundamental results,which have tremendously advanced the optimization theory,but also for enriching the field by applications emerging from statistics,machine learning,signal and imaging processing,communications,computational economics and finance.Computational methods and theory using semidefinite programming have been demonstrated to be helpful for the localization of network sensors.In computational economics,new complexity results have been established for problems related to the computation of an economic equilibrium.We appreciate Ye for his insatiable curiosity,openness to new ideas and a keen interest in the success of young people in our field of operations research.