A new approach to some possibilistic linear programming problems

Lai, Young-Jou; Hwang, Ching-Lai

doi:10.1016/0165-0114(92)90318-x

Cited by 347 publications

(192 citation statements)

References 12 publications

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“…the suitable values for these weights as well as α are usually determined subjectively by the experience and knowledge of the decision maker. Based on the most likely value concept proposed by Lai and Hwang [32] and considering several relevant works [42,43], we set these parameters to:…”

Section: Defuzzification Of Fuzzy Scheduling Modelmentioning

confidence: 99%

“…The universe U of fuzzy number is defined as Equation (28) Typically, the three prominent values, i.e., the most pessimistic, the most possible, and the most optimistic values for fuzzy numbers are estimated by decision maker [32,37,40]. In this paper, the three prominent values are defined according to determining method of universe [39] (8), (10)- (14), (16)- (23) (25), deterministic constraint (9) is replaced by fuzzy constraint (25) (26), deterministic constraint (15) is replaced by fuzzy constraint (26).…”

Section: Uncertainty Descriptionmentioning

confidence: 99%

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Uncertain Production Scheduling Based on Fuzzy Theory Considering Utility and Production Rate

et al. 2017

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Abstract:Handling uncertainty in an appropriate manner during the real operation of a cyber-physical system (CPS) is critical. Uncertain production scheduling as a part of CPS uncertainty issues should attract more attention. In this paper, a Mixed Integer Nonlinear Programming (MINLP) uncertain model for batch process is formulated based on a unit-specific event-based continuous-time modeling method. Utility uncertainty and uncertain relationship between production rate and utility supply are described by fuzzy theory. The uncertain scheduling model is converted into deterministic model by mathematical method. Through one numerical example, the accuracy and practicability of the proposed model is proved. Fuzzy scheduling model can supply valuable decision options for enterprise managers to make decision more accurate and practical. The impact and selection of some key parameters of fuzzy scheduling model are elaborated.

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Section: Defuzzification Of Fuzzy Scheduling Modelmentioning

confidence: 99%

Section: Uncertainty Descriptionmentioning

confidence: 99%

Uncertain Production Scheduling Based on Fuzzy Theory Considering Utility and Production Rate

et al. 2017

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show abstract

“…This assumption is made only for analytical convenience and does not limit the validity of the model. The total energy transmitted over the channel E b is the sum of the energies transmitted over all sub-carriers, i.e., 37) where N is the number of sub-carriers. The average bit error rate is given by the mean of the bit error probabilities per sub-carrier…”

Section: System Modelmentioning

confidence: 99%

“…In case the imprecise coefficients are represented by triangular fuzzy numbers (see section 3.6), we can solve equation C.2 using Zimmermann's fuzzy programming method [86] with the normalization process described in [37].…”

Section: Ubiquitous Communicationsmentioning

confidence: 99%