Iowa Department of Corrections Performance Report, FY 2004

Agency Performance Report

Iowa Department of Corrections Performance Report, FY 2005

Agency Performance Report

Iowa Department of Education Performance Report, FY 2004

Agency Performance Report

Iowa Department of Education Performance Report, FY 2005

Agency Performance Report

State Library of Iowa Performance Report, FY 2004

Agency Performance Report

Human Rights Status of Women Performance Report, FY 2004

Agency Performance Report

Human Rights Status of Women Performance Report, FY 2005

Agency Performance Report

Department of Management Performance Report, FY 2004

Agency Performance Report

Department of Management Performance Report, FY 2005

Agency Performance Report

Board of Parole Performance Report, FY 2004

Agency Performance Report

Iowa Department of Transportation Performance Report, FY 2004

Agency Performance Report

Iowa Department of Transportation Performance Report, FY 2005

Agency Performance Report

Department of Transportation Performance Plan, Iowa Department of Transportation, 2007

Performance plan for Iowa Department of Transportation.

Parallel scheduling of multiclass M/M/m queues: Approximate and heavy-traffic optimization of achievable performance

We address the problem of scheduling a multiclass $M/M/m$ queue with Bernoulli feedback on $m$ parallel servers to minimize time-average linear holding costs. We analyze the performance of a heuristic priority-index rule, which extends Klimov's optimal solution to the single-server case: servers select preemptively customers with larger Klimov indices. We present closed-form suboptimality bounds (approximate optimality) for Klimov's rule, which imply that its suboptimality gap is uniformly bounded above with respect to (i) external arrival rates, as long as they stay within system capacity;and (ii) the number of servers. It follows that its relativesuboptimality gap vanishes in a heavy-traffic limit, as external arrival rates approach system capacity (heavy-traffic optimality). We obtain simpler expressions for the special no-feedback case, where the heuristic reduces to the classical $c \mu$ rule. Our analysis is based on comparing the expected cost of Klimov's ruleto the value of a strong linear programming (LP) relaxation of the system's region of achievable performance of mean queue lengths. In order to obtain this relaxation, we derive and exploit a new set ofwork decomposition laws for the parallel-server system. We further report on the results of a computational study on the quality of the $c \mu$ rule for parallel scheduling.

The importance of relative performance feedback information: Evidence from a natural experiment using high school students

We study the effect of providing relative performance feedback information onperformance, when individuals are rewarded according to their absolute performance. Anatural experiment that took place in a high school offers an unusual opportunity to testthis effect in a real-effort setting. For one year only, students received information thatallowed them to know whether they were performing above (below) the class average aswell as the distance from this average. We exploit a rich panel data set and find that theprovision of this information led to an increase of 5% in students grades. Moreover, theeffect was significant for the whole distribution. However, once the information wasremoved, the effect disappeared. To rule out the concern that the effect may beartificially driven by teachers within the school, we verify our results using nationallevel exams (externally graded) for the same students, and the effect remains.