Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Global optimization and simulated annealing
Mathematical Programming: Series A and B
Aspiration Based Simulated Annealing Algorithm
Journal of Global Optimization
Journal of Global Optimization
Population set-based global optimization algorithms: some modifications and numerical studies
Computers and Operations Research
Global optimization models in data networks: a case study
Computers and Operations Research
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In a competitive market investors in a data network need to give utmost considerations on profitability. They must have clear picture of the size, growth rate and demand for different services. However, the investors' budget may be limited, and therefore the speed at which the network is rolled out, must be carefully planned to ensure that they can meet profitability targets. We model first the roll out order as combinatorial optimization problems and then extend them as continuous optimization problems. We then implement these models in a practical problem. Numerical studies suggested that the optimization problems have multiple local minima. Therefore, a global optimization technique is used to obtain the global minimum for the continuous variable problem and a combinatorial optimization technique is used to solve the discrete variable problem. Optimal financial indicators are obtained to assess the commercial viability of the network. Finally, we demonstrate that the solution of these optimization problems can provide an investment policy to the investors in data networks.