Discrete optimization
Resource allocation problems: algorithmic approaches
Resource allocation problems: algorithmic approaches
Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Stochastic discrete optimization
SIAM Journal on Control and Optimization
Admission-control policies for multihop wireless networks
Wireless Networks
Online surrogate problem methodology for stochastic discrete resource allocation problems
Journal of Optimization Theory and Applications
Concurrent Sample Path Analysis of Discrete Event Systems
Discrete Event Dynamic Systems
Nested Partitions Method for Global Optimization
Operations Research
Introduction to Discrete Event Systems
Introduction to Discrete Event Systems
Optimization of kanban-based manufacturing systems
Automatica (Journal of IFAC)
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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We consider stochastic discrete optimization problems where the decision variables are nonnegative integers and propose a generalized surrogate problem methodology that modifies and extends previous work in Ref. 1. Our approach is based on an online control scheme which transforms the problem into a surrogate continuous optimization problem and proceeds to solve the latter using standard gradient-based approaches while simultaneously updating both the actual and surrogate system states. In contrast to Ref. 1, the proposed methodology applies to arbitrary constraint sets. It is shown that, under certain conditions, the solution of the original problem is recovered from the optimal surrogate state. Applications of this approach include solutions to multicommodity resource allocation problems; in these problems, exploiting the convergence speed of the method, one can overcome the obstacle posed by the presence of local optima.