Post-Pareto Optimality Analysis to Efficiently Identify Promising Solutions for Multi-Objective Problems Heidi A. Taboada and David W. Coit Department of Industrial and Systems Engineering, Rutgers University, 96 Frelinghuysen Rd. Piscataway, NJ 08854, USA ABSTRACT: Techniques have been developed and demonstrated to efficiently identify particularly promising solutions from among a Pareto-optim...

Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each person’s assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, t...

This paper studies the allocation of shared resources between ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) in emerging 5G beyond cellular networks. In this paper, we design a unique queuing mechanism for joint eMBB/URLLC system. The aim is to flexibly schedule URLLC traffic enhance total eMBB throughput reliability packets (i.e., probability not dropping...

Ex ante willingness to pay to prevent adverse environmental change depends on people's perceptions about just how bad the situation will become if mitigation policies are not adopted. These perceptions are influenced by an array of external information sources that might be deemed more or less credible by individuals. I describe an empirical game plan wherein people's native prior distributions...

Bayesian networks have been extensively used in active information fusion that selects the best sensor based on expected utility calculation. However, inference degradation happens when the same sensors are selected repeatedly over time if the applied strategy is not well designed to consider the history of sensor engagement. This phenomenon decreases fusion accuracy and efficiency, in direct c...