Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Nearest interval approximation of a fuzzy number
Fuzzy Sets and Systems - Fuzzy intervals
Methods and Applications of Interval Analysis (SIAM Studies in Applied and Numerical Mathematics) (Siam Studies in Applied Mathematics, 2.)
Computers and Industrial Engineering
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Product platform two-stage quality optimization design based on multiobjective genetic algorithm
Computers & Mathematics with Applications
Computers and Industrial Engineering
Fuzzy Preference Ordering of Interval Numbers in Decision Problems
Fuzzy Preference Ordering of Interval Numbers in Decision Problems
Fuzzy inventory model with two warehouses under possibility constraints
Fuzzy Sets and Systems
A deteriorating repairable system with stochastic lead time and replaceable repair facility
Computers and Industrial Engineering
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Mathematical and Computer Modelling: An International Journal
Engineering Applications of Artificial Intelligence
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The real-world inventory control problems are normally imprecisely defined and human interventions are often required in solving these decision-making problems. In this paper, a realistic inventory problem with an infinite rate of replenishment over a prescribed finite but imprecise time horizon is formulated considering time dependent ramp type demand, which increases with time. Lead time is also assumed as fuzzy in nature. Shortages are allowed and backlogged partially. Two models are considered depending upon the ordering policies of the decision maker (DM). The imprecise parameters are first transformed to corresponding nearest interval numbers depending upon some distance metric on fuzzy numbers and then following the interval mathematics, the objective function for total profit from the planning horizon is obtained (which is an interval function). Then interval objective decision making problem is reduced to multi-objective problems using different approaches. Finally a fast and elitist multi-objective genetic algorithm (FEMOGA) is used for solving these multi-objective models to find pareto-optimal decisions for the DM. The models are illustrated numerically. As a particular case, the results due to linear trended and constant demands have been presented.