-------Abstract---------
Motivated by both traditional and emerging application domains, such
as the charging of electric vehicles, construction industry, and cloud
computing industry, we study the scheduling and pricing of demand that
is inherently deferrable in time. We seek to properly schedule the
processing of multiple delay-tolerant tasks so as to minimize the sum
of the expected total processing cost and the penalty for not
completing the tasks before their requested deadlines.

Through a dynamic programming formulation we characterize an important structural
property of optimal scheduling policies. We show that priority should
be given to tasks that have shorter slack time and longer remaining
processing time, as long as the non-completion penalty of every task
is convex in the additional processing time needed to complete the task.

--------Bio---------------
Yunjian Xu received the Ph.D. degree from the Massachusetts Institute of Technology in 2012.
Before joining the Singapore University of Technology and Design as an Assistant Professor, he
was a Postdoctoral Scholar with the Center for the Mathematics of Information, California Institute
of Technology for one year. His research interests lie in energy systems and markets, with emphasis on the economics of demand-side management and the dynamic scheduling of storage-capable loads such as consumer-owned energy storage devices and the charging of plug-in (hybrid) electric vehicles.

He was the recipient of the MIT-Shell Energy Fellowship.