Abstract
This paper studies the dynamic pricing problem of selling fixed stock of perishable items over a finite horizon, where the decision maker does not have the necessary historic data to estimate the distribution of uncertain demand, but has imprecise information about the quantity demand. We model this uncertainty using fuzzy variables. The dynamic pricing problem based on credibility theory is formulated using three fuzzy programming models, viz.: the fuzzy expected revenue maximization model, alpha-optimistic revenue maximization model, and credibility maximization model. Fuzzy simulations for functions with fuzzy parameters are given and embedded into a genetic algorithm to design a hybrid intelligent algorithm to solve these three models. Finally, a real-world example is presented to highlight the effectiveness of the developed model and algorithm. Copyright (C) 2009 John Wiley & Sons, Ltd.
| Original language | English |
|---|---|
| Pages (from-to) | 758-774 |
| Number of pages | 17 |
| Journal | APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY |
| Volume | 26 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - Nov 2010 |
Keywords
- dynamic pricing
- revenue management
- credibility theory
- fuzzy programming
- fuzzy simulation
- genetic algorithm
- ROBUST OPTIMIZATION APPROACH
- EXPECTED VALUE