is a leading database marketing agency with over 1,000 employees and
about $250 million in annual revenues. One of the services
they provide clients is the processing of incoming mail, and Sid
took on the challenge of figuring out how to best allocate the
incoming mail for 150 clients to three forms of processing:
processing using Opex machines, Aggissar machines, and manually.
Each of the two kinds of machines had limited capacity, and the task
was how to allocate mail to the two kinds of machines so as to
minimize the use of manual processing while meeting the contractual
throughput requirements of each customer.
Sid quickly recognized this as an opportunity
to use Solver, but his first attempt at modeling resulted in a
problem too big for Solver to handle. The modeling challenge was
the number of integer decision variables reflecting the reality that
within any shift a single machine (station) could not be used to
sort mail from multiple customers. After several rounds of
modeling using creative simplifications and reformulations, Sid was
eventually able to create a Solver model “small” enough to be
solved. When tested on historical data, the model solutions saved,
on average, 20 hours of manual labor per week.