Personalization-Driven Consensus Reaching Model for Emergency Mission Scheduling Schemes Selection in Large-Group Emergency Decision-Making with Linguistic Distribution Preference Relationship

摘要

Earth observation satellites are playing an increasingly important role in emergencies and have become an essential means of obtaining information. Given the suddenness and high urgency of emergencies, evaluating and selecting the emergency mission scheduling schemes in consideration evaluators' individual preferences have become a high priority. Since the selection of emergency task scheduling scheme often involves many stakeholders and has the complex characteristics of a large group, how to obtain the common solution accepted by the majority of participants is the main challenge we face in the process of scheme selection. To cope with this challenge, this paper presents an original evaluation framework to address the selection problem of emergency mission scheduling schemes under a linguistic distribution preference relationship (LDPR) with numerous evaluators. In the proposed evaluation framework, a consistency-driven optimization model is proposed to produce interval numerical scales with personalized individual semantics by maximizing the consistency of the interval fuzzy preference relation transformed from the LDPR. Then, a personalization-driven consensus reaching model with acceptance probability is devised to model the individual willingness of evaluators to improve the satisfaction of consensus achievement. Following this, a collective method is designed to aggregate evaluators' individual preferences based on the principle of justifiable granularity by maximizing the product of the specificity and coverage of information granule. Finally, a case study and some comparative analyses are undertaken to demonstrate the feasibility and validity of the proposed evaluation framework.

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