The purpose of this dissertation is to develop a dynamic pricing model which can help foodservice distributors increase profitability by better managing their perishable inventory. This study extends the existing research in revenue management and dynamic pricing. Specifically, the replenishment of perishable inventory, a concept which much of the research in optimal dynamic pricing models does not address, has been included in the dynamic programming model developed in this study. In addition, a simulation is constructed that optimizes policy parameters using genetic algorithms.; The dynamic programming model proposed in this study determines the appropriate discount rates, quantities, and timing that will maximize profitability of the sale of a single perishable product from a foodservice distributor to multiple customers. The state space of the problem is too large to be solved analytically. Data on perishable products of a large broadline distributor over a six-month period were collected and analyzed. The information gleaned from this analysis was used to develop a simulation. Then the policy parameters were optimized using genetic algorithms.; Profitability for this product was shown to increase by over 25% when the discount policies resulting from the simulation were used. These results suggest that the proposed simulation, which uses discounting, offers industry practitioners the opportunity to greatly improve profitability by selling perishable product before it must be disposed of. By implementing an appropriately designed discount policy, distributors can realize decreased inventory holding costs, increased sales revenue and increased gross profits. The analysis also indicates the need for further study of the effects of discounting on competitive products, customers and long-term profitability. Perishable inventory exists in many other industries and the extension of the results of the model developed in this study to those industries may also prove beneficial.
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