| | | | | | | |
200 | 123.69 | 400 | 63.72 | 200 | 57.44 | 400 | 59.79 |
This paper integrates the prospect theory with two-product ordering problem and adopts Bayesian forecasting model under Brownian motion to propose a loss-averse two-product ordering model with demand information updating in a two-echelon inventory system. We also derive all psychological perceived revenue functions for sixteen supply-demand cases as well as the expected value functions and prospect value function for the loss-averse retailer. To solve this model, a Monte Carlo algorithm is presented to estimate the high dimensional integrals with curved polyhedral integral region of unknown volume. Numerical results show that the optimal order quantities of both high-risk product and low-risk product vary across different psychological reference points, which are also affected by information updating, and the loss-averse retailer benefits considerably from information updating. All results suggest that our model provides a better description of the retailer$'$s actual ordering behavior than existing models.
Citation: |
Table 1. Updated Demand Information Values of Two products
| | | | | | | |
200 | 123.69 | 400 | 63.72 | 200 | 57.44 | 400 | 59.79 |
Table 2. Optimal Order Quantity with Different Psychological Reference Points and Information Updating
| | | | | |
0 | 271 | 281 | 427 | 429 | 3283.9 |
1000 | 270 | 278 | 426 | 428 | 2475.8 |
2000 | 265 | 273 | 421 | 427 | 1347.2 |
3000 | 270 | 268 | 413 | 427 | 643.9 |
4000 | 298 | 280 | 410 | 403 | -235.7 |
5000 | 315 | 285 | 460 | 305 | -785.4 |
8000 | 335 | 290 | 459 | 303 | -1436.7 |
10000 | 333 | 285 | 457 | 302 | -2578.8 |
30000 | 331 | 283 | 455 | 301 | -3521.6 |
50000 | 333 | 288 | 454 | 300 | -4076.4 |
Table 3. Optimal Order Quantity with Different Psychological Reference Points and No Information Updating
| | | | | |
0 | 40 | 23 | 15 | 25 | 8.5853 |
1000 | 36 | 22 | 14 | 25 | 0.1422 |
2000 | 37 | 23 | 16 | 23 | -32.983 |
3000 | 39 | 24 | 18 | 21 | -421.655 |
4000 | 41 | 25 | 20 | 20 | -1674.67 |
5000 | 43 | 25 | 22 | 18 | -1975.9 |
8000 | 45 | 28 | 23 | 15 | -3452.9 |
10000 | 43 | 27 | 25 | 13 | -3987.0 |
30000 | 41 | 25 | 26 | 12 | -5436.9 |
50000 | 43 | 27 | 27 | 11 | -6475.8 |
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