Decision making in fuzzy discrete event systems

Abstract

The primary goal of the study presented in this paper is to develop a novel and comprehensive approach to decision making using fuzzy discrete event systems (FDES) and to apply such an approach to real-world problems. At the theoretical front, we develop a new control architecture of FDES as a way of decision making, which includes a FDES decision model, a fuzzy objective generator for generating optimal control objectives, and a control scheme using both disablement and enforcement. We develop an online approach to dealing with the optimal control problem efficiently. As an application, we apply the approach to HIV/AIDS treatment planning, a technical challenge since AIDS is one of the most complex diseases to treat. We build a FDES decision model for HIV/AIDS treatment based on expert's knowledge, treatment guidelines, clinic trials, patient database statistics, and other available information. Our preliminary retrospective evaluation shows that the approach is capable of generating optimal control objectives for real patients in our AIDS clinic database and is able to apply our online approach to deciding an optimal treatment regimen for each patient. In the process, we have developed methods to resolve the following two new theoretical issues that have not been addressed in the literature: (1) the optimal control problem has state dependent performance index and hence it is not monotonic, (2) the state space of a FDES is infinite.

Analysis and Prioritization of Effective Strategies for Agile Supply Chain Case Study: Pharmaceutical Industry in Iran

“…3-3-2-Fuzzy Logic: Fuzzy Logic was presented by professor Lotfi Zadeh in 1965 as a mathematical theory to model the ambiguity and uncertainty of human perception and ideas (Lin et al, 2007).…”

Nonblocking check in fuzzy discrete event systems based on observation equivalence

“…3., here state (q i1 , q j2 ) ∈ Q 1 × Q 2 , is denoted as (i1, j2), and so on. E m (11,12) m (11,22) m (21,12) m (21,22) m (31,12) We will useà 1 ×à 2 to denote the reachable part ofà 1 à 2 , which contains all states reachable from (q 01 ,q 02 ) by δ 1 δ 2 and transitions among these states. Also…”

Fuzzy Expert Systems (FES) for Medical Diagnosis

“…[140][141][142] The architectures are proposed for the implementation and improving performance of a rule based diagnostic decision support systems related to medical diagnosis. [143][144][145] The powerful, generic as well as hybrid expert systems for diagnosis and remodeling of existing systems are also been reported. [146][147][148] The design of expert system frameworks for medical treatment and prevention of high risks related with the human health widened the scope for implementation of fuzzy concept in medical field.…”