  This paper addresses a complex parallel machine scheduling problem with jobs
divided into operations and operations grouped in families. Non-anticipatory
family setup times are held at the beginning of each batch, defined by the
combination of one setup-time and a sequence of operations from a unique
family. Other aspects are also considered in the problem, such as release dates
for operations and machines, operation's sizes, and machine's eligibility and
capacity. We consider item availability to define the completion times of the
operations within the batches, to minimize the total weighted completion time
of jobs. We developed Iterated Greedy (IG) algorithms combining destroy and
repair operators with a Random Variable Neighborhood Descent (RVND) local
search procedure, using four neighborhood structures to solve the problem. The
best algorithm variant outperforms the current literature methods for the
problem, in terms of average deviation for the best solutions and computational
times, in a known benchmark set of 72 instances. New upper bounds are also
provided for some instances within this set. Besides, computational experiments
are conducted to evaluate the proposed methods' performance in a more
challenging set of instances introduced in this work. Two IG variants using a
greedy repair operator showed superior performance with more than 70% of the
best solutions found uniquely by these variants. Despite the simplicity, the
method using the most common destruction and repair operators presented the
best results in different evaluated criteria, highlighting its potential and
applicability in solving a complex machine scheduling problem.
