The results show that this heuristic delivers service levels very close to the targets and its cost is always at the lower bound. Maximum entropy sampling , Jonathan Lee Maximum entropy sampling is the problem of choosing a most informative subset, of prespecified cardinality, from a finite set of random variables. We treat the Gaussian case, which is of practical interest and is already NP-hard. One application of the model is for the design of environmental monitoring networks. I will describe the branching framework, and a host of methods for producing upper bounds.
Orders arrive after a fixed and known lead time.
The costs of backlogging and inventory carrying are both linear in time. The objective is to minimize long run average cost. RAP applications include buffer management, database query optimization, load balancing, scheduling, communications and chip placement. Specifically, there is a fixed amount of some resource, which must be allocated amongst some number of competing activities. The objective function might be any of a number of metrics, such as average response time, throughput, cache hit rate, makespan and so on.