scholarly journals A novel robust chance constrained possibilistic programming model for disaster relief logistics under uncertainty

2016 ◽  
pp. 649-670 ◽  
Author(s):  
Maryam Rahafrooz ◽  
Mahdi Alinaghian
Keyword(s):  
Programming Model ◽  
Disaster Relief ◽  
Possibilistic Programming

A robust possibilistic programming multi-objective model for locating transfer points and shelters in disaster relief

2018 ◽  
Vol 15 (2) ◽  
pp. 326-353 ◽  
Author(s):  
Fatemeh Sabouhi ◽  
Zeinab Sadat Tavakoli ◽  
Ali Bozorgi-Amiri ◽  
Jiuh-Biing Sheu
Keyword(s):  
Disaster Relief ◽  
Multi Objective ◽  
Possibilistic Programming ◽  
Objective Model

A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty

OR Spectrum ◽  
2011 ◽  
Vol 35 (4) ◽  
pp. 905-933 ◽  
Author(s):  
Ali Bozorgi-Amiri ◽  
M. S. Jabalameli ◽  
S. M. J. Mirzapour Al-e-Hashem
Keyword(s):  
Stochastic Programming ◽  
Programming Model ◽  
Disaster Relief ◽  
Multi Objective ◽  
Stochastic Programming Model ◽  
Robust Stochastic Programming

scholarly journals A fuzzy optimization approach to strategic organ transplantation network design problem: A real case study

2021 ◽  
Vol 10 (3) ◽  
pp. 195-216
Author(s):  
Samira Rouhani ◽  
Mir Saman Pishvaee ◽  
Naeme Zarrinpoor
Keyword(s):  
Programming Model ◽  
Organ Transplant ◽  
Real Case ◽  
Programming Approach ◽  
Optimization Approach ◽  
Primary Role ◽  
Model Based ◽  
Aspiration Levels ◽  
Possibilistic Programming

Designing an efficient supply chain for organ transplant networks which is intimately related to humans’ life plays a primary role in improving the network’s performance. This research is focused on proposing a new multi-period location-allocation modeling approach to make appropriate strategic decisions for designing organ transplant networks under supply and budget uncertainties. To serve this purpose, a bi-objective possibilistic programming model is formulated the aim of which is to maximize network responsiveness and minimize the total cost. A fuzzy goal programming approach is adopted to solve multiple objective function models and control their deviations from the corresponding aspiration levels. As an important contribution of this study, the chance of success of transplantation processes is taken into consideration by proposing appropriate utility functions according to transportation criteria. Moreover, for the purpose of coping with the inherent uncertainty of the input parameters, a possibilistic programming model based on Me measure converted to three optimistic, realistic and pessimistic models is developed. Three new formulations have also been developed to tackle equality chance constraints. Finally, the optimal solutions of the developed models are analyzed through conducting a real case study in Iran. According to the results, for the considered organ transplant network, the possibilistic programming model based on the realistic measure is better than the optimistic and pessimistic measure in most confidence levels.

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