Considering group preference for law enforcement and service quality management based on SWOT analysis

Abstract

This study proposes an architecture in which a SWOT analysis is employed to consider group preference via the concept of web services The SWOT analysis is based on unanimity including adopting an objective and neutral stance and creates a consensus among officers and the insights of police academics through active participation in discussions prior to police decision-making However because SWOT analysis may be insufficient to formulate an action plan or strategy SWOT analysis is integrated into Group Decision Web Service Selection (GDWSS) to form a new GDSWOT for law enforcement and service quality management to rank factors by order of preference The specific study finds that assigning linguistic variables to decision makers (DMs) is more intuitive for representing their important weights with fuzzy sets for SWOT analysis; doing so helps eliminate the drawbacks in semantic ambiguity and provide better model options The specific study considers the preferences of DMs and ensures the consistency of the selection Every task/policy can be finished by a set of services that expose the operations required to analyze composite process-services in multiple tasks/policies The corresponding optimization approaches for each task/policy select a service to optimize the overall evaluation function In an effort to provide greater consistency different DMs assign different task/policy weights to generate global optimization parameters Then a new hybrid method named SWOTS-TOPSIS is used to reduce the need for a large amount of input preference data In addition improving the disagreement and setting preferences using GDSWOT can alleviate the DMs' workload The combined methodology is thus a helpful tool for DMs The expected performance is to improve the quality of police effectively and create a high-quality work environment in response to time and social changes The study tries to analyze its fundamental theoretical basis and proposes relevant ideas to perfect its theories

Date of Award	2018 Aug 22
Original language	English
Supervisor	Hei-Chia Wang (Supervisor)