İkame ve bozunabilir ürünler için ortak fiyatlandırma ve envanter kararları

This study investigates the problem of jointly determining the order size and optimal prices for a perishable inventory system under the condition that demand is time and price dependent. The inventory is also assumed to decay at a certain rate. We assume that a decision-maker has the opportunity to adjust prices during the sales season to influence demand and to improve revenues. In this system the seller is allowed to change the prices for a discrete number of times at a certain cost. We develop a mathematical model to find the optimal times to change the prices, the optimal prices and the order quantity. We analyze both single and double product system where items are procured from a single supplier with respect to the economic order quantity model. This research investigates the operational efficiency of the supply chain by considering the effects of price change throughout the timeline. We develop analytical results for the optimal prices when the times of price changes are given. However, due to the complexity of the problem, we design heuristic algorithms in order to find best times to change the prices in order to obtain the highest profit values. We develop two different heuristics which are Simulated Annealing and Genetic Algorithm. We observe that better results can be obtained by genetic algorithm for most of the parameter settings. For the double product system, we analyze multiple pricing at equal time intervals and obtain similar results as in the single product case. We again apply Simulated Annealing and Genetic Algorithm heuristic methods in order to find the best time arrays and we again observe that genetic algorithm gives better results than simulated annealing. We observe that the profit values can be significantly increased by using multiple prices over the season rather than using a constant price for both single product and double product cases. The system can be managed much more efficiently and the decay rates can be decreased significantly with the application of multiple prices over the season. Through numerical experiments, we also analyze the effects of different parameters on this system and extract managerial insights.