  With the increase of order fulfillment options and business objectives taken
into consideration in the deciding process, order fulfillment deciding is
becoming more and more complex. For example, with the advent of ship from store
retailers now have many more fulfillment nodes to consider, and it is now
common to take into account many and varied business goals in making
fulfillment decisions. With increasing complexity, efficiency of the deciding
process can become a real concern. Finding the optimal fulfillment assignments
among all possible ones may be too costly to do for every order especially
during peak times. In this work, we explore the possibility of exploiting
regularity in the fulfillment decision process to reduce the burden on the
deciding system. By using data mining we aim to find patterns in past
fulfillment decisions that can be used to efficiently predict most likely
assignments for future decisions. Essentially, those assignments that can be
predicted with high confidence can be used to shortcut, or bypass, the
expensive deciding process, or else a set of most likely assignments can be
used for shortlisting -- sending a much smaller set of candidates for
consideration by the fulfillment deciding system.
