M.Sc Thesis | |

M.Sc Student | Sambur Michael |
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Subject | Analysis of the Newsvendor Problem with Info-Gap Theory |

Department | Department of Industrial Engineering and Management |

Supervisors | ASSOCIATE PROF. Yale Herer |

PROFESSOR EMERITUS Yakov Ben-Haim | |

Full Thesis text |

The newsvendor problem is one of the classical operations research models. It can be found in almost any operations management and inventory textbook. It is a single-period, single-product inventory problem, with a single procurement before a stochastic demand realization. Several empirical studies have shown that students and professional buyers do not order a quantity similar to that proposed by the classical solution of the newsvendor problem, which has the objective function of maximizing the expected profit. There are many possible explanations. This research was motivated by a desire to explain the deviation from the expected profit maximizing order quantity with one simple decision making criterion. Furthermore, we explored a new decision paradigm. It is to this end that we studied the newsvendor problem with the info-gap decision methodology. The info-gap theory has been shown, in some cases, to explain the behavior of animals better than other decision making methodologies. Our study compares, both quantitatively and qualitatively, the solutions obtained by the classical, maximum probability of achieving a critical profit level and Info-Gap methodologies. In our analysis of the newsvendor problem with the basic Info-Gap formulation we do not assume any probabilistic demand distribution. We only use an estimate of the nominal demand and the error of this estimate. We do not assume knowledge of a worst case and do not perform a max-min analysis. Info-Gap decision theory allows us to determine the robustness of the decision to the uncertainty, namely, how much the demand can deviate from the nominal quantity and we can still assure achieving the critical profit. A critical profit is an additional input required for the analysis with info-gap decision theory. We successfully explain the empirical results in some cases, whereas in other cases there is more work to be done. Two additional analyses include a hybrid model that incorporates uncertain probabilistic information in an info-gap analysis and a multi-item model.