This paper discusses staffing under annualized hours. Staffing is the selection of the most cost-efficient workforce to cover workforce demand. Annualized hours measure working time per year instead of per week, relaxing the restriction for employees to work the same number of hours every week, To solve the underlying combinatorial optimization problem this paper develops a Cross-Entropy optimi...

Consider a network of unreliable links, each of which comes with a certain price and reliability. Given a fixed budget, which links should be bought in order to maximize the system’s reliability? We introduce a Cross-Entropy approach to this problem, which can deal effectively with the noise and constraints in this difficult combinatorial optimization problem. Numerical results demonstrate the ...

We present new theoretical convergence results on the Cross-Entropy method for discrete optimization. Our primary contribution is to show that a popular implementation of the Cross-Entropy method converges, and finds an optimal solution with probability arbitrarily close to 1. We also give necessary conditions and sufficient conditions under which an optimal solution is generated eventually wit...

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The cross entropy (CE) method is a model based search method to solve optimization problems where the objective function has minimal structure. The Monte-Carlo version of the CE method employs the naive sample averaging technique which is inefficient, both computationally and space wise. We provide a novel stochastic approximation version of the CE method, where the sample averaging is replaced...

Obtaining a fullest possible representation of solutions to a multiobjective optimization problem has been a major concern in Multi-Objective Genetic Algorithms (MOGAs). This is because a MOGA, due to its very nature, can only produce a discrete representation of Pareto solutions to a multiobjective optimization problem that usually tend to group into clusters. This paper presents a new MOGA, o...

Combinatorial optimization algorithms are used in many and diverse applications; for instance, in the planning, management, and operation of manufacturing and logistic systems and communication networks. For scalability and dependability reasons, distributed and asynchronous implementations of these optimization algorithms have obvious advantages over centralized implementations. Several such a...

The management of Multi-sensors in information fusion system occupies an important role with the development of modern weapon platform. Therefore, scientific and rational management of limited sensors resources is essential or urgent, and improvement the capability of air defense operations is necessary. According to the management optimization problem in Multi-sensor optimal resource, we analy...

Abstract In the present paper we discussed the parameters optimization in medical decision making using maximum entropy weight. Currently, most medical decision models rely on point estimates for input parameters, although the uncertainty surrounding these values is well-recognized. However, it still left some challenge problems that are commonly involved in computational problems involving exp...

Owing to considering the distribution of the gray information and the spatial neighbor information with using the two-dimensional (2-D) histogram of the image, The 2-D maximum Tsallis entropy(2DMTE) method often gets better segmentation results, and owing to a controllable parameter, it has better flexibility than other 2-D entropy methods. However, its performance is sensitive to its parameter...