A fuzzy-stochastic OWA model for robust multi-criteria decision making
Fuzzy Optimization and Decision Making
Computers and Industrial Engineering
Modelling redundancy allocation for a fuzzy random parallel-series system
Journal of Computational and Applied Mathematics
Two-stage fuzzy stochastic programming with Value-at-Risk criteria
Applied Soft Computing
International Journal of Approximate Reasoning
Solution of fuzzy integrated production and marketing planning based on extension principle
Computers and Industrial Engineering
Information Sciences: an International Journal
Solving multi-objective fuzzy probabilistic programming problem
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Multi-item stochastic and fuzzy-stochastic inventory models are formulated under total budgetary and space constraints. Here, the inventory costs are directly proportional to the respective quantities, unit purchase/production cost is inversely related to the demand and replenishment/production rate is assumed to vary directly with demand. Shortages are allowed but fully backlogged. Here, for both models, demand and budgetary resource are assumed to be random. In fuzzy-stochastic model, in addition to the above assumptions, available storage space and total expenditure are imprecise in nature. Impreciseness in the parameters have been expressed with the help of linear membership functions. Assuming random variables to be independent and to follow normal distributions, the models have been formulated as stochastic and fuzzy-stochastic non-linear programming problems. The stochastic problem is first reduced to the equivalent single objective or multiple objectives problems following chance-constraint method. The problem with single objective is solved by a gradient-based technique whereas fuzzy technique is applied to the multi-objective one. In the same way, the fuzzy-stochastic programming problem is first reduced to a corresponding equivalent fuzzy non-linear programming problem and then it is solved by fuzzy non-linear programming (FNLP) following Zimmermann technique. The models are illustrated numerically and the results of different models are compared.