  Solving optimization problems with parallel algorithms has a long tradition
in OR. Its future relevance for solving hard optimization problems in many
fields, including finance, logistics, production and design, is leveraged
through the increasing availability of powerful computing capabilities.
Acknowledging the existence of several literature reviews on parallel
optimization, we did not find reviews that cover the most recent literature on
the parallelization of both exact and (meta)heuristic methods. However, in the
past decade substantial advancements in parallel computing capabilities have
been achieved and used by OR scholars so that an overview of modern parallel
optimization in OR that accounts for these advancements is beneficial. Another
issue from previous reviews results from their adoption of different foci so
that concepts used to describe and structure prior literature differ. This
heterogeneity is accompanied by a lack of unifying frameworks for parallel
optimization across methodologies, application fields and problems, and it has
finally led to an overall fragmented picture of what has been achieved and
still needs to be done in parallel optimization in OR. This review addresses
the aforementioned issues with three contributions: First, we suggest a new
integrative framework of parallel computational optimization across
optimization problems, algorithms and application domains. The framework
integrates the perspectives of algorithmic design and computational
implementation of parallel optimization. Second, we apply the framework to
synthesize prior research on parallel optimization in OR, focusing on
computational studies published in the period 2008-2017. Finally, we suggest
research directions for parallel optimization in OR.
