Fuzzy TOPSIS Approaches for Assessing the Intelligence Level of IoT-Based Tourist Attractions

Abstract

With the application of Internet of Things (IoT) in tourism, the functions and management modes of tourist attractions are being greatly updated. It becomes a faced problem to assess the intelligence level of IoT-based tourist attractions. The assessment is helpful for managers to equip their tourist attractions with smart services which further improve the management efficiency and tourist satisfaction. However, there are few recognized standards for the implementation of IoT-based tourist attractions, and the common practice of using the average value to replace multiple assessment scores has a shortage. Motivated by these observations, we present a framework of IoT-based intelligent tourist attractions and recognize specific intelligent functions brought by IoT techniques to tourist attractions. Then, two fuzzy TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) approaches, that is, a centroid-based fuzzy TOPSIS and an integral-based fuzzy TOPSIS, are formulated to deal with the inconsistent assessment scores from multiple experts. An application study shows the effectiveness and advantage of our approaches in comparison with the classical TOPSIS. Both the centroid-based fuzzy TOPSIS and the classical TOPSIS cannot reflect the preferences of decision-makers, but their assessment results are not fully consistent. The assessment results by the integral-based fuzzy TOPSIS are subject to the given optimism level which may make difference on assessment orders. We observe some insightful findings helpful for improving the intelligence level of IoT-based tourist attractions.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 24 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.