Chance constrained programming with fuzzy parameters
Fuzzy Sets and Systems
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
A genetic algorithm for the Flexible Job-shop Scheduling Problem
Computers and Operations Research
A fuzzy-based lifetime extension of genetic algorithms
Fuzzy Sets and Systems
Computers and Industrial Engineering
Computers & Mathematics with Applications
Information Sciences: an International Journal
Engineering Applications of Artificial Intelligence
Computers and Industrial Engineering
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A production inventory model for a newly launched product is developed incorporating inflation and time value of money. It is assumed that demand of the item is displayed stock dependent and lifetime of the product is random in nature and follows exponential distribution with a known mean. Here learning effect on production and setup cost is incorporated. Model is formulated to maximize the expected profit from the whole planning horizon. Following [Last, M. & Eyal, S. (2005). A fuzzy-based lifetime extension of genetic algorithms. Fuzzy Sets and Systems, 149, 131-147], a genetic algorithm (GA) with varying population size is used to solve the model where crossover probability is a function of parent's age-type (young, middle-aged, old, etc.) and is obtained using a fuzzy rule base and possibility theory. In this GA a subset of better children is included with the parent population for next generation and size of this subset is a percentage of the size of its parent set. This GA is named fuzzy genetic algorithm (FGA) and is used to make decision for above production inventory model in different cases. The model is illustrated with some numerical data. Sensitivity analysis on expected profit function is also presented. Performance of this GA with respect to some other GAs are compared.