Iterative ranking aggregation using quality improvement of subgroup ranking
Jiankun Ding,
Deqiang Han and
Yi Yang
European Journal of Operational Research, 2018, vol. 268, issue 2, 596-612
Abstract:
Ranking aggregation concerns the combination of rankings to obtain a consensus ranking that best represents all the input rankings according to a specific criterion. However, finding the optimal aggregated ranking, i.e., the one with the highest quality, is usually NP-hard. To reduce the computational cost, many heuristic aggregation methods have been proposed. They cannot ensure the optimality and the qualities of aggregated rankings obtained actually can be further improved. To find a better aggregated ranking, a novel iterative ranking aggregation method (IRAM) is proposed in this paper using the quality improvement of the subgroup ranking. IRAM starts from an aggregated ranking generated by a traditional heuristic ranking aggregation method. In each iteration step, IRAM attempts to improve the ranking’s quality of a subgroup of items. If the ranking’s quality of a subgroup of items is improved, the full aggregated ranking’s quality is consequently improved. Moreover, a window iterative ranking aggregation method (W-IRAM) is designed, which is simpler than the IRAM. We prove that IRAM and W-IRAM bring better (or at least the same) aggregation qualities compared with the traditional heuristic ranking aggregation methods. Simulation results show that our iterative ranking aggregation approaches perform well.
Keywords: Decision analysis; Ranking aggregation; Subgroup ranking; Average ranking distance criterion (ARDC) (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:268:y:2018:i:2:p:596-612
DOI: 10.1016/j.ejor.2018.01.031
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