Using Markov Chains To Design Algorithms For Bounded-Space On-Line Bin Cover

We show how the on-line bounded-space bin cover problem can be modeled with a Markov chain. We then use this Markov chain formulation to derive an algorithm for the on-line bounded-space bin cover problem. Our algorithm is designed to perform well in a restrictive environment where it can utilize only very few open bins at each time. We analyze the performance of our algorithm and compare it to the Sum-of-Squares with Threshold algorithm. The experimental results show that our algorithm compares favorably with the Sum-of-Squares with Threshold algorithm, and the average waste incurred by our algorithm is very small even when it is forced to use only a handful of open bins.