In this paper, a two-echelon newsvendor problem is considered. Many real-life spheres including fashion, food industries, and healthcare services run into problems similar to newsvendor problem. Our problem is determining inventory levels in order to optimize the pro, t and service level in selling a product. This product is made up of several raw materials. Only the distribution of demand is known and the hot season of selling the product is just a short period,after that, the price of the product drops dramatically. The storage space and initial budget are limited. The problem is modeled and solved as an unconstrained nonlinear optimization problem using two nonlinear techniques: the Sequential Unconstrained Minimization Technique (SUMT) and Steepest Descent (SD).

In this paper, two-echelon newsvendor problem is considered. Many real-life situations like fashion, food industries, and healthcare services match newsvendor problem. Our problem is determining levels of inventory in order to optimize the profit and service level in selling a product. This product is made up of several raw materials. Only the distribution of demand is known, and the hot season of selling the product is just a short period and after that, the price of the product drops dramatically. The storage space and initial budget are limited. We modeled and solved the problem as an unconstrained nonlinear optimization problem using two nonlinear techniques, the sequential unconstrained minimization technique (SUMT) and steepest descent (SD).

In industrial and commercial settings, inventory systems often involve managing multiple products with diverse demand patterns, making the direct application of the single-item newsvendor model inefficient. To address this complexity, this study proposes an adaptation of the newsvendor model through demand aggregation, where related items are grouped into a product family. By aggregating demand and financial parameters, the traditional newsvendor approach can be extended to multi-item systems, simplifying the inventory management process. This method was tested in two different case studies—a coffee roaster company and a meatball producer—demonstrating its validity and applicability. The aggregated newsvendor model was found to enhance inventory accuracy and efficiency, reducing random error and improving operational performance. This approach offers a valuable extension of the newsvendor model, with potential for broader application across various industries.

Objective The problem of selecting the suppliers along with their Economic Order Quantity (EOQ) has always been an important issue in the Newsvendor model. Multiple qualitative and quantitative criteria are needed to be regarded for making a better supplier selection while performing trade-offs among them. An approach that can take all criteria into account in the selection of suppliers has not been developed yet in the Newsvendor model. Multiple criteria decision-making (MCDM) can be leveraged to make trade-offs among multiple criteria. Methods This study sought to develop a solution to the Newsvendor problem, accounting for the most significant criteria in supplier selection and product reliability so that the total cost of the chain would be minimized in a multi-product and multi-period model with multiple suppliers. This approach adopted the Bayesian Best Worst Method (BWM), which is one of the MCDM methods, to rank the criteria and also implemented the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to prioritize the suppliers. Then, the suppliers' weights were used as the proposed model inputs. The results obtained from Bayesian BWM validated the effectiveness of the presented approach. The objective function was set in a way that a retailer's profit and weight value of products considering the ranking of suppliers were maximized. The goal was to find optimal order quantity allocating to suppliers. To convert the multi-objective model to a single-objective, the Lp-metric method was used. Results To evaluate the derived model, a case study with real data in the electronic supply chain was investigated. The developed model for the Newsvendor problem could find the best suppliers by considering a combination of qualitative and quantitative criteria in selecting them. In this research, the real-world case study was considered in the electronics industry, which allowed the researchers to make a better evaluation of the model. Conclusion The obtained results indicated that the retailer uses a combination of suppliers in each period for optimal order allocation to profit-maximizing considering product's reliability and the weight value of products.

In this paper, a two-stage newsvendor model with one type of product in presence of stochastic demand under quantity flexibility (QF) contract is investigated. Under the proposed model, the manufacturer allows the retailer to update its order size upward or downward. Under this mechanism, the manufacturer is committed to provide a certain level more than the retailer’s primary order to deliver when large demand is observed; in addition, the retailer has the authority to cancel a limited amount of its initial order in the beginning of selling season when the observed demand is small. Under these circumstances, overstocking/shortage risks are shared between two members. By sharing risks, it will be possible to optimize decisions globally. In this paper, a new approach for optimal adjustment of QF parameters (i.e. upward and downward adjustment parameters) is developed. Expected profit functions of both channel members under QF contract is mathematically modeled and optimal closed-form relationship between upward and downward adjustment parameters is determined. The obtained closed-form relation guarantees more profit for the whole supply chain and at the same time assures more profit for both channel members.Under the proposed model, both members benefit from the coordinated decision making while risk of demand uncertainty is shared. Our investigations revealed that increasing flexibility on cancelling initial order causes less flexibility of the manufacturer in providing more products. On the other hand, decrease of cancelling flexibility results in more flexibility for oversupply volume.

Loss-averse behavior makes the newsvendors avoid the losses more than seeking the probable gains as the losses have more psychological impact on the newsvendor than the gains. In economics and decision theory, the classical newsvendor models treat losses and gains equally likely, by disregarding the expected utility when the newsvendor is loss-averse. Moreover, the use of unbounded utility to model risk attitudes fails to explain some decision-making paradoxes. In contrast, this paper deals with the utility maximization of the newsvendor using a class of bounded utility functions to study the effect of loss aversion on the newsvendor certainty equivalents and risk premiums. New formulas are introduced to find the utility-optimal order quantity of the normal distribution. The results show that when an exponential loss aversion exists, the classical newsvendor optimal quantity serves as a lower bound when the overage costs are high and as an upper bound when the underage costs are high. In addition, we show that high loss aversion entails higher risk premiums. Similar conclusion holds when the overage/underage costs increase. Higher standard deviations, on the other hand, mean lower utility-optimal quantities and higher risk premiums. The presented formulas are advantageous in finding the optimal order quantities and risk premiums of a stochastic short-shelf life inventory when the loss is a key factor in the decision-making process.

In this paper, we consider a newsvendor who is going to invest on dedicated or flexible capacity, our goal is to find the optimal investment policy to maximize total profit while the newsvendor faces uncertainty in lead-time and demand simultaneously. As highlighted in literature, demand is stochastic, while lead-time is constant. However, in reality lead-time uncertainty decreases newsvendor's performance and increases purchasing cost. Analytical results suggest an approach for decision makers to decide which situation is optimal to invest in flexible capacity. Furthermore, we derive a closed-form solution for optimal production and capacity under dedicated and flexible policy when demand and lead-time follow uniform and normal distribution. An approximation method introduced in this paper to find the optimal production quantity and investment policy results show that this approximation is useful when the coefficient of variation is low under uniform distribution, and it is useful when the coefficient of variation is high under normal distribution. Finally, we show a threshold, considering the fact that it is optimal for the newsvendor to invest on flexible capacity when flexible capacity cost is less than the threshold. To sum up, we measure the effect of lead-time variability on optimal solution.

The aim of this study is to provide a comprehensive model for economic Decision in a laboratory environment with a behavioral approach. This study attempts to evaluate the existing empirical evidence, In a controlled test environment and the attitudes of people towards risk with respect to the timing of payments made to measure and The profitability of models of inventory management in terms of acceptance or avoid risk, on the behavioral approach. This research is in the field of positive research and the method of laboratory research (using a controlled environment).Payment plans and managers risk preferences is effective on the decision-making process of optimal order level. The research model presented in this study is based on behavioral models based on triple risk preferences and three payment plan in whole nine behavioral models that were compared with classical regression model. The end result of paired sample t test showed that Behavioral models developed in this study is more predictive than the classic model and also the end goal of research is Behavioral model developed in this study leads lower opportunity cost than classical models and also Order level inventory profitability is increased.

In this paper, a supply chain, including a manufacturer, a distributor and some retailers, is considered. The manufacturer produces a single product and outsources distribution operations to a distributor in order to deliver products demanded by retailers. Each retailer has a time window to receive the products and faces the Newsvendor problem with stochastic demand. The manufacturer aims to serve retailers providing that the maximum lateness doesn’ t exceed a predetermined value. All players in the supply chain are willing to maximize their own profit. The model simultaneously includes pricing, order quantity and routing decisions. First, the manufacturer announces the whole sale price, then the distributor declares the unit transportation cost to the retailers, and finally each retailer decides on the amount of his order quantity. The profit functions of the players are formulated and linearized; then the solution is determined in three stages using game theory. Finally, a numerical example is presented and the equilibrium decisions of the players are determined using GAMS software.