In real world scheduling systems, task or job attributes are stochastic and sequence dependent, learning improves attributes, and schedulers use their cost (or disutility) functions to evaluate schedules with respect to multiple criteria. This paper addresses a stochastic bicriteria single...

An important routing problem is to determine an optimal path through a multi-attribute network which minimizes a cost function of path attributes. In this paper, we study an optimal path problem in a bi-attribute network where the cost function for path evaluation is fractional. The problem can...

This paper deals with a single machine scheduling problem with general past-sequence-dependent (psd) setup time and log-linear learning in which the setup times and learning effects are job-dependent. The setup times are unique functions of the length of already processed jobs, and the learning...