# Occupancy-Measure LP

Online assignment of reusable resources such as reviewers, call agents and servers has long relied on greedy matching: dispatch whichever available resource scores highest now, ignoring the value lost when it is occupied later. LAG casts the problem as a contextual restless multi-armed bandit and uses an occupancy-measure linear program, together with its dual, to compute a closed-form index for each task-type and resource pair, equal to the match score minus the opportunity cost of keeping that resource free. On peer-review data and structured synthetic instances, LAG reaches 83–99% of that linear program's upper bound and improves over greedy by up to 12.3% on journal data and up to 36% on synthetic instances.

Online assignment of reusable resources such as reviewers, call agents and servers has long relied on greedy matching: dispatch whichever available resource scores highest now, ignoring the value lost when it is occupied later. LAG casts the problem as a contextual restless multi-armed bandit and uses an occupancy-measure linear program, together with its dual, to compute a closed-form index for each task-type and resource pair, equal to the match score minus the opportunity cost of keeping that resource free. On peer-review data and structured synthetic instances, LAG reaches 83–99% of that linear program's upper bound and improves over greedy by up to 12.3% on journal data and up to 36% on synthetic instances.