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A TOPSIS-Inspired Ranking Method Using Constrained Crowd Opinions for Urban Planning
Authors:Sujoy Chatterjee  Sunghoon Lim
Institution:1.Informatics Cluster, School of Computer Science, University of Petroleum and Energy Studies (UPES), Dehradun 248007, India;2.Department of Industrial Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea;3.Institute for the 4th Industrial Revolution, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea
Abstract:Crowdsourcing has become an important tool for gathering knowledge for urban planning problems. The questions posted to the crowd for urban planning problems are quite different from the traditional crowdsourcing models. Unlike the traditional crowdsourcing models, due to the constraints among the multiple components (e.g., multiple locations of facilities) in a single question and non-availability of the defined option sets, aggregating of multiple diverse opinions that satisfy the constraints as well as finding the ranking of the crowd workers becomes challenging. Moreover, owing to the presence of the conflicting nature of features, the traditional ranking methods such as the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) cannot always be feasible as the optimal solutions in terms of multiple objectives cannot occur simultaneously for the conflicting cases (e.g., benefit and cost criteria) for urban planning problems. Therefore, in this work, a multi-objective approach is proposed to produce better compromised solutions in terms of conflicting features from the general crowd. In addition, the solutions are employed to obtain a proper ideal solution for ranking the crowd. The experimental results are validated using two constrained crowd opinion datasets for real-world urban planning problems and compared with the state-of-the-art TOPSIS models.
Keywords:crowdsourcing  decision making  multi-attribute decision problems  urban planning
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