Parallel computational optimization in operations research: A new integrative framework, literature review and research directions

Abstract

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.Keywords computing science · parallel optimization · computational optimization · literature review * I am grateful for the support provided by Abdullah Burak, Philip Empl, Constanze Hilmer, Gerhard Rauchecker, Richard Schuster, Henning Siemes, and Melih Yilmaz, who supported me substantially in searching and coding research articles.2 Impressive computational results of applying parallelization to the traveling salesman problem (TSP) are reported by Crainic et al. [2006, p.2]. arXiv:1910.03028v1 [cs.DC] 3 Oct 2019Parallel computational optimization in operations research is challenging in general from both the algorithmic and the computational perspective, and ii) a viable alternative to parallelizing algorithms has been the exploitation of ongoing increases of clock speed of single CPUs of modern microprocessors. But this growth process reached a limit already several years ago due to heat dissipation and energy consumption issues . This development makes parallelization efforts (not only in optimization) much more important than it was in earlier times.Fortunately, the need for parallelization has been acknowledged and accompanied by an increased availability of parallel computing resources. This availability is rooted in two phenomena: a) the rapid development of para...

Big Data Analytics Using Cloud Computing Based Frameworks for Power Management Systems: Status, Constraints, and Future Recommendations

“…The framework combines algorithmic design and parallel optimization computational implementation viewpoints [ 104 ]. Unsurprisingly, the application of parallel optimization has been hesitant because (i) parallelizing algorithms is challenging in general from both the algorithmic and the computational perspective, and (ii) a viable alternative to parallelizing algorithms has been the exploitation of ongoing increases of clock speed of single CPUs of modern microprocessors [ 105 ].…”

“…Although the existing traditional parallel computing programming models such as Message Passing Interface (MPI) and OpenMP have been encapsulated at the bottom, and that data storage management, data division, task allocation and scheduling, data synchronization and communication, fault tolerance, and many other technical details need to be handled by users themselves, which is still very cumbersome [ 139 ]. Users are entangled in many underlying technical details while considering the application problem itself, which makes parallel programming not easy [ 105 ].…”

MCDM Filter with Pareto Parallel Implementation in Shared Memory Environment

“…As far as we know, few references can be found about parallel implementations of Multicriteria Decision Support Method [9], even less with the Open MP paradigm. Some works focus on very specific methods : Electre III [10], AHP [11], PROMETHEE [12].…”

Overall Optimization and Sustainable Development of Resources/Energy/Environment under Polygeneration Energy System

“…The economic performance evaluation one mainly takes the estimated construction investment, the cost of chemical fuel and electricity as the evaluation index. In addition, the income of by-products, internal rate of return, payback period, and net present value can also be considered as indicators [18].…”