The WHEAT has high nutritive materials and it is important to cover nutritious security for the people of our country .During the last years, WHEAT has dedicated itself the most cultivation area of farm product as well .It is one of strategic products for decision makers in agricultural sector in Iran .In this paper, effective factors in supplying WHEAT has been studied using NERLOVE MODEL and time series data related to 1984-2003 period. For this purpose, WHEAT supply took into function WHEAT price , WHEAT price and barley price with one lag , amount of annual rainfall , cultivation area of WHEAT , WHEAT supply with one lag and time trends. Results show that, although we shouldn’t ignore the effect of price factors, the WHEAT supply with one lag and amount of annual rainfall are the most important factors that affect the WHEAT supply. So, PRICING policy is not the sufficient instrument for increasing of WHEAT supply.

To protect Producers of agricultural products, particularly strategic products, various measures have been employed in the last tow decades. In order to maintain terms of trade in agricultur sector, PRICING policy on the basis of real production costs in a typical utility unit has been one of the measures that government had to fultil. By using the time series statistics of 1971-1998, the effect of WHEAT PRICING policy, as a strategic product, . on producers, consumers and government is studied. The obtained results say the supply ela~ticity of product relative to price and cultivation area have been 0.17 and 1.85 relatively, and WHEAT demand price elasticity-0.013. The results of this study demonstrate that the guaranteed price, set by the government, had been 26 percent lower than the market equilibrium Price. This resulted in Rials 1930 Billion loss for WHEAT producers and Rials 8300 Billion profit for consumers. The amount of Rials 136Billion extra cost was borne by the government (inefficiency cost) for its intelVention in the WHEAT market.

Objective: This study focuses on analyzing a PRICING problem within a two-echelon supply chain, comprising a manufacturer and multiple retailers. It also explores the integration of various channels, including traditional, electronic, and omni-channels such as "Buy-Online-Pickup-in-Store", "Buy-Online-Deliver-to-Home", and "Order-in-Store-Deliver-to-Home". In addition, it seeks to develop a demand function dependent on product price and return policy in electronic channels, as well as delivery times for products. Accordingly, the present study aims to investigate and evaluate a PRICING MODEL considering various distribution channels in a single-period single-product environment and to provide a solution approach with valuable performance that can be used in real problems as a decision-making tool. It also tries to investigate the impact of various factors on the number of decision variables and the profit of the entire supply chain. Methods: The problem was formulated as a non-linear mathematical programming MODEL and coded and implemented using GAMS software. Furthermore, several numerical examples were solved to investigate the effect of changes in some parameters on the values of decision variables, retailers' demand, and the total profit of the supply chain. Results: The results showed that the two parameters of price sensitivity of demand and return sensitivity of demand have a critical impact on decision variables including the retailer's sales price, return price, delivery time, and profit of the entire supply chain. Conclusion: This study proposed an integrated approach to evaluate the impact of PRICING decisions and return policy in the supply chain. Therefore, it can serve as a valuable resource for companies in making operational decisions regarding the concurrent implementation of PRICING decisions, product return policy, and delivery time. Online retailers can use the achieved results to make operational decisions regarding the application of the product return policy according to the presented analysis.

This paper develops an economic production quantity MODEL in a three-echelon supply chain composing of a supplier, a manufacturer and a wholesaler under two scenarios. As the first scenario, we consider a return contract between the outside supplier and the supplier and also between the manufacturer and the wholesaler, but in the second one, the return policy between the manufacturer and the wholesaler is not applied. Here, it is assumed that shortage is permitted and demand is price-sensitive. The principal goal of the research is to maximize the total profit of the chain by optimizing the order quantity of the supplier and the selling prices of the manufacturer and the wholesaler. Nash-equilibrium approach is considered between the chain members. In the end, a numerical example is presented to clarify the applicability of the introduced MODEL and compare the profit of the chain under two scenarios.

The main purpose of this study is to estimate the MODEL of WHEAT supply reaction with rational and adaptive price expectation during 1983- 2005 periods, and to investigate the role of expectation in farmers' behavior. The results showed that MODEL of supply reaction based on rational and adaptive price expectation was more suitable for estimating the supply MODEL. Land area, expectation prices and bred seeds had significant effect on WHEAT supply. Also, some factors such as rainfall had important role to increase production.

In this paper, we investigate the PRICING and return policy issue of one-echelon green supply chain. The assumed supply chain contains a manufacture that produces two types of products: green and non-green products. These products represent the same function but in selling price and environmentally issues have different effects. The assumed return policy for both products stimulates the customer valuation. Different MODELs of PRICING strategy and return policy are developed in both green and hybrid production modes. System performance in both hybrid and green production mode are studied, and the return policy and its effect on these production modes are investigated. The optimal solutions are derived and several numerical examples and sensitivity analysis are performed to demonstrate the applicability of the developed MODEL and its solution method.

In the past, traditional channel or retailer was used for selling products but with the development of ecommerce, a large company in the world considers another sale channel like websites. Considering the existence of two channels for sale, choosing the right strategy for PRICING has become important. In PRICING and production planning, risk is a very important factor. In this paper, outsourcing policies have been used to deal with risks and a new mathematical MODEL is presented for simultaneous decision-making on PRICING and outsourcing in a threelevel and two-channel supply chain despite uncertainty. In this paper, a nonlinear MODEL is presented for supply chain profit function. According to the complexity of profit function, a meta-heuristic algorithm based on simulated annealing and scenario-based stochastic MODEL are used to solve the proposed MODEL. The initial parameters of this algorithm are set by Taguchi method. The computational results and sensitivity analysis indicate the effectiveness of the proposed solving method for problem solving.

PRICING of agricultural products, considered one of the most important tools for government intervention in agriculture. Government policies effect on PRICING of agricultural products in agriculture the motives production and performance. In this study, Positive dynamic equilibrium MODEL was used for estimated annual production for the dynamic supply functions, cropping pattern, Input used, Analysis of PRICING Policy 2007-2015 in the planning horizon. The results showed, Different scenarios show that farmers do not respond the same to different policies, And results showed that input and output price policies, The percentage change in interest farmers the scenario of rising prices agricultural inputs scenarios over rising prices of agricultural products, And percentage change in the total cost scenarios increase in agricultural prices over scenarios is the rising cost of agricultural inputs. Thus, according to the MODEL's ability to accurately estimate the amount of supply per year as well as the effectiveness of different policies, It is recommended that the Medal used in this study to other policies and other areas of test and be examined.

Objective Designing a multi-level supply chain network with efficient product flow management is an important issue in supply chain management. Determining the price of products, which can be affected by different factors such as environmental uncertainty, will have a significant impact on the strategic decision of designing the supply chain network. In recent research on dynamic PRICING, the majority focused on two-level supply chains, exploring the impact of advertising on the supply chain. In this study, the levels of the supply chain are upgraded to three levels, and the advertising and inventory variables as well as the PRICING issue in the supply chain are investigated. Hence, the overarching objective is to scrutinize the coordination of a supply chain involving three variables: advertising, warehouse inventory, and a comparison between static and dynamic PRICING. Given the accelerated expansion of e-commerce, dynamic PRICING emerges as a potent strategy for profit augmentation within the supply chain, an aspect that has received comparatively less attention. The supply chain in this research includes three levels: producer, supplier, and retailer. In addition, many sources, including Chen et al, 2018, have indicated that dynamic PRICING is being used in many innovative businesses, that 60% of market development managers around the world are familiar with dynamic PRICING methods, and that 35% of CEOs and business managers plan to base all their PRICING MODELs on dynamic PRICING methods in the next three years. This research initially aims to provide a stable MODEL for dynamic PRICING and compare it with static PRICING in multi-level supply chains using a game theory approach. Secondly, it examines the theoretical foundations, thirdly, the research methodology is presented, and fourthly, the discussion and conclusions are discussed.   Methods The present research falls under the category of applied research. It considers discount-oriented MODELs to examine the planning horizon, the use of dynamic PRICING in some actors at one level of the chain from the concept of dynamic PRICING, and the use of static PRICING by others at the same level and considering the demand in a non-deterministic (stable) manner. Finally, the use of Stackelberg’s game is presented to examine the core of the game and the Nash equilibrium point. The robust optimization MODEL is presented by Malloy et al. Initially, the MODEL introduces symbols: x as the vector of design variables, and y as the vector of control variables. Parameters A, B, and C are coefficient parameters, while b and e are parameter vectors. Values for A and B are predetermined, and B, C, and e involve uncertainty. A particular understanding of the parameter is called scenario uncertainty, which is assigned the symbol s, and its probability is specified by ps. K retailers operate in the supply chain, and each retailer supplies the desired product from only one manufacturer. In general, the outcome for each retailer is the disparity between their income and expenses. In such a situation, the retailer faces ordering, holding, and marketing costs for each product. The profit margin of each retailer is the difference between the price paid to the manufacturer for the bulk purchase and the selling price to the customer. It is worth noting that considering that the final product of each manufacturer is sent to only one retailer, the number of manufacturers and retailers is equal. For data analysis, a genetic algorithm, particle accumulation optimization, and MATLAB software were employed. Initially, the MODEL underwent analysis in static mode, followed by dynamic mode analysis using Games software. Ultimately, the outcomes of the two PRICING methods were compared.   Results This article introduces the development of a stochastic demand function utilizing genetic algorithms and particle optimization. It presents two distinct single-period MODELs designed for a competitive environment. In the first MODEL, retailers have only an intermediary role and do not decide on PRICING or optimal order quantity. In the mentioned MODEL, it is assumed that retailers transfer the demand exactly and to the same extent as it is from the customers to the suppliers. Based on the amount of their production, suppliers face shortages and excess supply. The purpose of this MODEL is to determine the optimal and desirable amount of production as well as the price for each supplier. In addition, routing costs are approximated. The results indicate that the presented approximation is very accurate and evaluates the equilibrium point in much less time than the original MODEL. In the second MODEL, retailers are assumed to incur shortage or maintenance costs according to the number of orders they send. Here, the suppliers determine the final price, and the goal of the retailers is to determine the optimal amount of the order. In this MODEL, it is assumed that suppliers’ production is customized, and inventory costs are considered only for retailers. A noteworthy aspect of this MODEL is the emphasis on dynamic PRICING—an inherently crucial and intricate element within the domain of supply chain dynamics theory   Conclusion The results underscore the substantial efficacy of the proposed combination in addressing the multi-level supply chain network design MODEL with both dynamic and static PRICING. The sensitivity analysis of the manufacturer’s profit showed that the changes indicating an increase in the market base of a manufacturer will lead to an increase in profit for both manufacturers. The graph of the change in the producer’s profit compared to the change in the costs showed that a decrease in the production cost leads to an increase in the profit for the producer, while the profit of the next producer decreases in these conditions. Like the producer’s profit, the distributor’s profit also increases with the market base. Expanding the market base not only leads to an increase in the distributor's profit through collaboration with producers but also occurs through two distinct mechanisms. Firstly, there's a profit increase due to raising retailer prices. Although wholesale prices increase with the expanded market base and heightened demand, the distributor, to maintain balance, can elevate retail prices at a higher ratio. Consequently, the value of the pi-wi term increases. Secondly, there's a profit increase resulting from boosting sales volume. The distributor's profit is positively influenced by the increase in sales volume, thereby reinforcing the overall financial outcome.

In today’ s highly competitive business environment, which is known by characteristics of low profitability, high customer expectations for high-quality products and minimum waiting time, make companies efforts to transform constraints into opportunities for gaining competitive advantage by optimizing their business processes. In such a situation, appropriate supplier selection can play a key role in the efficiency and effectiveness of the organization and have a direct impact on reducing costs, profitability, and flexibility of a company. The purpose of this research is to provide a supplier selection MODEL with simultaneous consideration of two sources of inventory control and PRICING in the supply chain. To assess the validity and reliability of the MODEL, the actual data of the Seven Diamond Industries Company including input materials (Hot Roll) and products (galvanized sheets) have been used. The proposed MODEL is coded in the GAMS software and its results have been analyzed.