Abstract
AbstractReal‐world problems are becoming highly complex and therefore have to be solved with combinatorial optimization (CO) techniques. Motivated by the strong increase in publications on CO, 8393 articles from this research field are subjected to a bibliometric analysis. The corpus of literature is examined using mathematical methods and a novel algorithm for keyword analysis. In addition to the most relevant countries, organizations, and authors as well as their collaborations, the most pertinent CO problems, solution methods, and application areas are presented. Publications on CO focus mainly on the development or enhancement of metaheuristics like genetic algorithms. The increasingly problem‐oriented studies deal particularly with real‐world applications within the energy sector, production sector, or data management, which are of increasing relevance due to various global developments. The demonstration of global research trends in CO can support researchers in identifying the relevant issues regarding this expanding and transforming research area.
Highlights
Combinatorial optimization (CO) has always been of great interest in the scientific community (Cacchiani et al, 2018)
While no clear trend can be identified for integer programming, the TSP is mainly investigated in connection with the metaheuristics ant colony optimization (20), genetic algorithms (18) as well as local search (16), and the scheduling problem with tabu search (13) and simulated annealing (10)
The analysis demonstrates the importance of CO methods, which are applied in many different fields and are used to solve many of the current global problems
Summary
Combinatorial optimization (CO) has always been of great interest in the scientific community (Cacchiani et al, 2018). New challenges are emerging, such as the consideration of uncertain conditions, the combination of hard problems, and the solving of problems in real time (Cacchiani et al, 2018) In this context, the number of publications on CO problems has increased significantly in recent years (8393 in 2019, cf Section 3.1), with over 150 reviews on the subject. A bibliometric analysis is one of these approaches and has the potential to provide a systematic, reproducible, and transparent review process based on statistical measurements of research activities and researchers (Aria and Cuccurullo, 2017) This type of review can be classified as more objective and reliable than traditional review methods. AND LANGUAGE: (English) AND DOCUMENT TYPES: (Article) Indexes = SCI-EXPANDED, SSCI Timespan = 1990–2019
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