  We introduce the problem of Task Assignment and Sequencing (TAS), which adds
the timeline perspective to expert crowdsourcing optimization. Expert
crowdsourcing involves macrotasks, like document writing, product design, or
web development, which take more time than typical binary microtasks, require
expert skills, assume varying degrees of knowledge over a topic, and require
crowd workers to build on each other's contributions. Current works usually
assume offline optimization models, which consider worker and task arrivals
known and do not take into account the element of time. Realistically however,
time is critical: tasks have deadlines, expert workers are available only at
specific time slots, and worker/task arrivals are not known a-priori. Our work
is the first to address the problem of optimal task sequencing for online,
heterogeneous, time-constrained macrotasks. We propose tas-online, an online
algorithm that aims to complete as many tasks as possible within budget,
required quality and a given timeline, without future input information
regarding job release dates or worker availabilities. Results, comparing
tas-online to four typical benchmarks, show that it achieves more completed
jobs, lower flow times and higher job quality. This work has practical
implications for improving the Quality of Service of current crowdsourcing
platforms, allowing them to offer cost, quality and time improvements for
expert tasks.
