Joint Pricing and Inventory Control for Non-instantaneous Deteriorating Items with Stochastic Demand

In recent years inventory and pricing of deteriorating items has gained an enormous attention by many researchers. In this study, an inventory system for non-instantaneous deteriorating items with stochastic demand is modeled. This model has the assumptions that shortages are allowed and backlogging rate is variable where the last one is defined as a function of waiting time for the next replenishment. The objective is to maximize the total profit per unit time by finding the optimal selling price and replenishment schedule simultaneously. The concavity of the function is proved with a unique optimal solution. Thereby we provide an algorithm for finding the optimal solution. Finally, the authors present a numerical example to illustrate the theoretical results. A sensitivity analysis for the optimal solution with respect to major parameters is also carried out

Construction data mining methods in the prediction of death in hemodialysis patients using support vector machine, neural network, logistic regression and decision tree

Objectives: Chronic kidney disease (CKD) is one of the main causes of morbidity and mortality worldwide. Detecting survival modifiable factors could help in prioritizing the clinical care and offers a treatment decision-making for hemodialysis patients. The aim of this study was to develop the best predictive model to explain the predictors of death in Hemodialysis patients by using data mining techniques. Methods: In this study, we used a dataset included records of 857 dialysis patients. Thirty-one potential risk factors, that might be associated with death in dialysis patients, were selected. The performances of four classifiers of support vector machine, neural network, logistic regression and decision tree were compared in terms of sensitivity, specificity, total accuracy, positive likelihood ratio and negative likelihood ratio. Results: The average total accuracy of all methods was over 61%; the greatest total accuracy belonged to logistic regression (0.71). Also, logistic regression produced the greatest specificity (0.72), sensitivity (0.69), positive likelihood ratio (2.48) and the lowest negative likelihood ratio (0.43). Conclusions: Logistic regression had the best performance in comparison to other methods for predicting death among hemodialysis patients. According to this model female gender, increasing age, addiction, low Iron level, C-reactive protein positive and low urea reduction ratio were the main predictors of death in hemodialysis patients

Optimal selling price, replenishment lot size and number of shipments for two-echelon supply chain model with deteriorating items

This paper deals with a pricing and production-distribution model for a deteriorating item in a two-echelon supply chain. The profit function for the manufacturer and retailer in the integrated supply chain is derived. The manufacturer's production batch size is regulated to an integer multiple of the discrete delivery lot quantity to the retailer. The objective is to maximize the total profit per unit time by finding the optimal selling price, production lot size, total cycle time, number of deliveries, and delivery lot size, simultaneously. Based on the notion of optimal interval, we outline an effective algorithm for finding the optimal solution. Finally, the authors present a numerical example to illustrate the theoretical results of the model. Sensitivity analysis for the optimal solution with respect to major parameters is also carried out. The results show that, when the deterioration rate increases, both the optimal production lot size and cycle time decrease. It is interesting to note that an increase in the deterioration rate also tends to reduce the delivery lot size without affecting the number of deliveries per production batch. Also, the optimal interval for N does not change when deterioration rate changes. Reductions in the inventory cycle times for both parties demonstrate the negative effects of deterioration on the supply chain

Pricing of Complementary Products in Online Purchasing under Return Policy

In online purchasing, customers may return products due to dissatisfaction with the quality of the product, and receive a refund based on the return policy, which is determined by online distributors. Online distributors can offer generous policies to attract more customers, but at the cost of reducing total profits. In this paper, the effect of the pricing and quality of complementary products (products sold together with other items) in online selling under the return policy is investigated. For this purpose, a mathematical model is developed to obtain optimal values for selling price, refund amount, and quality of products. Based on analytical results, a solution algorithm is proposed to solve the numerical examples and perform sensitivity analysis. Findings reveal that, while increasing the sensitivity of demand with respect to the refund amount, the price, quality, and refund on returned products should be increased. In addition, the online distributor should increase the quality of products when customers are more sensitive to the quality of products. Among other results, the selling price is shown to be negatively affected by demand elasticity with respect to price. In this situation, the online distributor should reduce the quality level and the refund amount for returned products to avoid a sharp decline in profit. In addition, when the quality cost is high, the price and quality should be decreased and the refund amount unchanged