  Temporal planning is an extension of classical planning involving concurrent
execution of actions and alignment with temporal constraints. Durative actions
along with invariants allow for modeling domains in which multiple agents
operate in parallel on shared resources. Hence, it is often important to avoid
resource conflicts, where temporal constraints establish the consistency of
concurrent actions and events. Unfortunately, the performance of temporal
planning engines tends to sharply deteriorate when the number of agents and
objects in a domain gets large. A possible remedy is to use macro-actions that
are well-studied in the context of classical planning. In temporal planning
settings, however, introducing macro-actions is significantly more challenging
when the concurrent execution of actions and shared use of resources, provided
the compliance to temporal constraints, should not be suppressed entirely. Our
work contributes a general concept of sequential temporal macro-actions that
guarantees the applicability of obtained plans, i.e., the sequence of original
actions encapsulated by a macro-action is always executable. We apply our
approach to several temporal planners and domains, stemming from the
International Planning Competition and RoboCup Logistics League. Our
experiments yield improvements in terms of obtained satisficing plans as well
as plan quality for the majority of tested planners and domains.
