This paper considers constrained optimization over a renewal system. A controller observes a random event at the beginning of each renewal frame and then chooses an action that affects the duration of the frame, the amount of resources used, and a penalty metric. The goal is to make frame-wise decisions so as to minimize the time average penalty subject to time average resource constraints. Thi...

The one-step anticipatory algorithm (1s-AA) is an online algorithm making decisions under uncertainty by ignoring future non-anticipativity constraints. It makes near-optimal decisions on a variety of online stochastic combinatorial problems in dynamic fleet management, reservation systems, and more. Here we consider applications in which the 1s-AA is not as close to the optimum and propose Ams...

As the Internet has evolved into a valuable and critical service platform for business and daily life, the research community has enthusiastically applied data mining methods to improve application performance by analyzing and optimizing the behaviors of the underlying systems (e.g., datacenter design, network resource provisioning, network security, etc.) These data mining procedures often use...

We study online combinatorial decision problems, where one must make sequential decisions in some combinatorial space without knowing in advance the cost of decisions on each trial; the goal is to minimize the total regret over some sequence of trials relative to the best fixed decision in hindsight. Such problems have been studied mostly in settings where decisions are represented by Boolean v...

We study social decision problems and Nash games for a class of linear-quadratic-Gaussian (LQG) models with N decision makers possessing different dynamics. For the social decision case, the basic objective is to minimize a social cost as the sum of N individual costs containing mean field coupling, and the exact social optimum requires centralized information. Continuing from the previous work...

Operations Research / Management Science (OR/MS) can be described as the discipline of applying advanced analytical methods to help making better decisions and has been around in the agricultural and forestry management sectors since the fifties, approaching decision problems that range from more strategic sectorlevel planning to farm operation issues and integrated supply chain management. In ...

This paper studies an online linear optimization problem generalizing the multi-armed bandit problem. Motivated primarily by the task of designing adaptive routing algorithms for overlay networks, we present two randomized online algorithms for selecting a sequence of routing paths in a network with unknown edge delays varying adversarially over time. In contrast with earlier work on this probl...

this paper attempts to rank the effective factors in making trust in social networks to provide the possibility of attracting and increasing users’ trust on these social networks for providers and designers of online social networks. identifying the effective factors in making trust in social networks is a multi-criteria decision making problem and most of effective factors are ambiguous and un...

We consider the online matching problem with concave returns. This problem is a significant generalization of the Adwords allocation problem and has vast applications in online advertising. In this problem, a sequence of items arrive sequentially and each has to be allocated to one of the bidders, who bid a certain value for each item. At each time, the decision maker has to allocate the curren...

The formation of efficient and reliable processes to be executed in modern complex and highly volatile logistic systems requires computational support. An important prerequisite of computer based planning is the representation of the real world problem into a formal decision model. Automatic decision algorithms are then able to derive suitable solutions of such a model and one solution is selec...