Optimal lot sizing, process quality improvement and setup cost reduction
Operations Research
The economic production lot size model under volume flexibility
Computers and Operations Research
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Nearest interval approximation of a fuzzy number
Fuzzy Sets and Systems - Fuzzy intervals
Controlled Elitist Non-dominated Sorting Genetic Algorithms for Better Convergence
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
An optimal production run time with imperfect production processes and allowable shortages
Computers and Operations Research
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Fuzzy Mathematical Programming and Fuzzy Matrix Games (Studies in Fuzziness and Soft Computing)
Fuzzy Mathematical Programming and Fuzzy Matrix Games (Studies in Fuzziness and Soft Computing)
Interval oriented multi-section techniques for global optimization
Journal of Computational and Applied Mathematics
Computers and Industrial Engineering
An EMQ model in an imperfect production process
International Journal of Systems Science
A production-inventory model of imperfect quality products in a three-layer supply chain
Decision Support Systems
An economic order quantity model of imperfect quality items with partial backlogging
International Journal of Systems Science
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
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In this paper, an Economic Production Quantity (EPQ) model is developed with flexibility and reliability consideration of production process in an imprecise and uncertain mixed environment. The model has incorporated fuzzy random demand, an imprecise production preparation time and shortage. Here, the setup cost and the reliability of the production process along with the backorder replenishment time and production run period are the decision variables. Due to fuzzy-randomness of the demand, expected average demand is a fuzzy quantity and also imprecise preparation time is represented by fuzzy number. Therefore, both are first transformed to a corresponding interval number and then using the interval arithmetic, the single objective function for expected profit over the time cycle is changed to respective multi-objective functions. Due to highly nonlinearity of the expected profit functions it is optimized using a multi-objective genetic algorithm (MOGA). The associated profit maximization problem is illustrated by numerical examples and also its sensitivity analysis is carried out.