In many realistic problem domains, the main variable of interest behaves monotonically in the observable variables, in the sense that higher values for the variable of interest become more likely with higher-ordered observations.
Distributed Constraint Optimization Problems (DCOPs) are an elegant model for representing and solving many realistic combinatorial problems that are distributed by nature.
They arise in many realistic models of problems in science, engineering, and medicine, where there is a time lag or after-effect.
Within the context of mathematics teaching, problem-based learning (PBL) and MEAs share many characteristics including realistic problems, open-ended tasks, higher-order thinking, metacognitive coaching, self-directed learning, self-assessment, group work, and interconnectedness of disciplines (Chamberlain and Moon 2008).
Although algorithms are potentially able to consider many features of realistic problems and to effectively explore the solution space, a lack of precision in their objective function evaluation (which usually includes a performance parameter, as the throughput) limits in fact their capability to find good solutions.
In the present work, we study a multi-period facility location problem featuring many realistic constraints.
No-wait scheduling problems arise in many realistic production environments such as hot metal rolling, the plastic molding, chemical and pharmaceutical, food processing and several other industries.
These are very common and realistic problems encountered in conceptual design of many engineering systems.
I try to write about people with realistic problems in realistic settings.
