The weak axiom of profit maximization (WAPM) is used to analyze the optimization behavior of 18 Iranian banks within 10 years (2000-2009). In general the banks in the Iranian economy can be divided into 2 groups, state and privately owned banks. Nonparametric nature of this test allows us to study the profit maximization behavior of the firms without the need for any assumption on functional form of underlying technology. The WAPM is checked under 2 assumptions: constant technology and nonregressive technological change. Although most of the banks violate the deterministic WAPM test, testing the stochastic WAPM with measurement error and believing in existence of at least 1 percent measurement error in data, show the behavior of the privately owned banks and one of the state owned banks is consistent with WAPM under nonregressive technological change assumption. Although the behavior of the state owned banks is consistent as well if one believes in 5 percent measurement error, the difference leads to this conclusion that performance of the state development policies and objectives by the state owned banks has been deviated from profit maximization behavior.

Introduction: To maximize the profitability of livestock industry decisions are based on individual animal yield and other factors such as overall health of animal as well as production and reproduction performance. To increase the revenue changes in replacement policies and breeding are essential (Nasr Esfahani 2018). Accordingly, in Iran issues such as feed price fluctuations and lack of coherent policies increase the economic risk of the dairy cow industry. To better make a decision on elimination or replacement of a cow, the producer shall compare the expected benefits of both keeping or replacing an individual animal. The most important objective of a livestock unit is to maximize the profit of the herd; one of the issues affecting this profit is the criteria and level of elimination (Rogers et al, 1988). If elimination and replacement is not optimal, namely the cows are eliminated sooner or later than the optimal time, the profitability of the herd will decreases. For determining the optimal elimination time point several biological and economic variables shall be concurrently taken into consideration (De Vries, 2006). The elimination decision must be based on predicted future incomes of the cow. Generally, keeping an animal in the herd for a longer time leads to more profit. The proper decision of optimal and reasonable elimination is made by comparing the present value of future liquidity flow (income and expenses) of the current livestock in the herd with the present value of the future liquidity flow of heifer as its replacement. Ultimately livestock with most current value will occupy the position (Groenendall et al. 2005). The application of dynamic programming in the animal sciences mostly is about animal replacement issues. The optimal policy at each stage represents best decision from that stage till the final stage. In this method, an expected value will be calculated for each of the situations that will happened. Furterhmore, the decision maker selects the best decision based on the calculated expected value and upcoming situations. For optimal replacement decision making in dairy herds, several dynamic programming models have been proposed by De Vries et al. (2006) and Van Arendonk et al. (1985). Cardoso et al. (1999) reported optimizing replacement and insemination policies in dairy cows by calculating income, costs and monthly probability of elimination. Material and methods: In this study, we estimated the biological parameters of the herd, including risk of forced elimination and the possibility of pregnancy in different lactation periods and different months after calving based on the data collected from industrial dairy farms (Ardabil city, Iran) between 2015 and 2018. Also, financial data of the herd was also obtained in the form of an economic questionnaire from the studied units and was developed by importing biological parameters and financial data into a bio economic model in Dairy Vip software. This software simulates the livestock over time and calculates the performance of herd based on optimal and non-optimal modes. The basis for optimal elimination decision is to minimize the cost of missed opportunity (cost of rejecting the best alternative when deciding). So that by negating the value of keeping livestock (obtained from the difference between the net present value of existing livestock and alternative heifers), optional removal will be done. The criteria of optional elimination in the studied herds were determined to reach the milk production of non-pregnant cows to less than 18 kg/day. Milk production was evaluated using daily milk production records and also fitting the incomplete gamma curve (Wood). The 21-day mean insemination rate and success insemination rate of cow were 49. 3% and 37%, respectively. By default, in Dairy Vip software, a livestock can be in the herd for 24 months maximum after calving. The risk of abortion from the second to the eighth month was 6. 24, 4. 16, 2. 08, 1. 11, 0. 45, 0. 19, and 0. 19%, respectively. Dynamic programming model was developed to determine the optimal replacement policy. The objective function in this study was to maximize the present value of net income from current cows and alternative heifers. In order to estimate the expected statistics under optimal policy, Markov chain simulation was used. Results and discussion: With the implementation of optimal policies, the annual elimination rate increased from 30. 11% to 43. 80%. However, the forced elimination rate slightly decreased )2. 3%(. The optimal removal rate can vary depending on the economic and biological conditions in the herd. With the implementation of optimal policies, pregnancy rate increased from 15. 31% to 18. 20% and increased by 2. 89%. Correspondingly, it can be concluded that the economic importance of increasing the pregnancy rate is more urgent in herds with weaker reproductive performance. Also, success rate of insemination dropped from 33. 9% in non-optimal mode to 37%, indicating higher likelihood of livestock pregnancy in different months. The 21-day insemination rate increased from 47. 6% to 49. 3%, indicating appropriate cow inoculation. milk production in dairy cows could be increased by reducing the pregnancy days. In this study pregnancy days decreased from 139 to 132 days by optimal policies leading to significantly higher milk production. Furthermore, open days (the interval from calving to the next gestation period) decreased from 167 to 161 days reducing the pregnancy days and subsequently increasing the pregnancy rate. Also, reducing the calving distance from 13. 6 to 13. 3 (0. 3 per month) resulted in higher annual milk production. Optimal policies compared with the nonoptimal policy resulted shorter lactation days thus significantly higher milk production (daily and annual) and performance (Table 3). Daily milk performance increased from 41. 4 to 44. 2 equaling to 3 Liters more milk per cow (annually 935 kg more milk). According to our observations, annual milk yield increased from 12, 548 to 13, 483 kg per cow via the implementation of optimal policies. Moreover, reducing the average lactation days resulted in increase in the daily and annual production of cows by 3 and, respectively. Conclusion: The most important goal of a livestock unit is maximizing the profit of the herd. One of the factors affecting profitability, is criteria and elimination rate. Implementing optimal policies is associated with increased livestock elimination rates and replacement costs and also increased feed costs. However, higher revenue from these policies can compensate the increased costs thus increasing the net profit per cow. One of the basic criteria in estimating the expected present value is to sort the cows in the herd based on future income and expenses, and according to these values, the decision is made to keep or eliminate the cows. So, regardless of these values, cows may be eliminated sooner or later than the optimal time, which reduces the profitability of the herd.

Marginal productivity (MP) estimation is utilized to plan maximum output levels and allocate resources to address fluctuating demand for supply fuel in the power plant sector as well as adjust transferring and dispatching in the transmission and distribution networks. In this paper, a data envelopment analysis (DEA) model is introduced for estimating the directional marginal productivity of supply chain divisions. The proposed model for estimating the directional marginal productivity in the supply chain tries to find the optimal direction of efficient divisions on the frontier so that marginal profit is maximized. This model measures efficiency by maximizing marginal profit for multiple outputs in predetermined directions based on multiple inputs. The purpose of this study is to develop acceptable techniques for responding to demand fluctuations, especially in the energy and power plant sectors. This is when confronted with efficiency losses from climate change and critical conditions. The results suggested that the oil field division of one of the supply chains had fundamental capacities to respond to peak demand. Furthermore, the power plant division of this supply chain also had a considerable structure for the marginal profit maximization of outputs. Additionally, there were transmitters and distribution lines that obtained marginal profit maximization by adding one extra unit to the line's length in the determined direction.

This paper concerns with a mathematical model considering customer satisfaction requirements in a supply chain. The model tradeoffs the supply chain ability to fulfill the customers’ requirements and the limited budget in a variety of production phases. The decisions made here help to determine the profit provider price and economic quantity while the effective supplier and priori requirements are found. A numerical example illustrates the applicability of the proposed mathematical model.

In this paper, the electric vehicle (EV) charging station scheduling process is designed to maximize the profit of EVs owners and the station operator in two steps. First, a complete model is proposed to formulate the problem of charging and discharging EVs at charging stations in one-day-ahead 24-hours. The purpose of the program is to increase the profits of EVs owners charging station operator. In this manner, the charging behaviour of EVs such as arrival time to the station, the initial charge, the departure time from the station and the amount of requested energy are known as inputs of the problem. In the second stage, uncertainty is considered. Monte Carlo and Genetic Algorithm have been used to model the uncertainties in the problem and optimization, respectively. The output of the first stage is the optimal hourly load of the station. Then in the second stage, the optimal location of the charging station is determined by the obtained optimal load on the standard distribution network. So that the losses and voltage deviation index are minimized and the voltage stability index is maximized.

With the formation of the competitive electricity markets in the world, optimization of bidding strategies has become one of the main discussions in studies related to market designing. Market design is challenged by multiple objectives that need to be satisfied. The solution of those multi-objective problems is searched often over the combined strategy space, and thus requires the simultaneous optimization of multiple parameters. The problem is formulated analytically using the Nash equilibrium concept for games composed of large numbers of players having discrete and large strategy spaces. The solution methodology is based on a characterization of Nash equilibrium in terms of minima of a function and relies on a metaheuristic optimization approach to find these minima. This paper presents some metaheuristic algorithms to simulate how generators bid in the spot electricity market viewpoint of their profit maximization according to the other generators’ strategies, such as genetic algorithm (GA), simulated annealing (SA) and hybrid simulated annealing genetic algorithm (HSAGA) and compares their results. As both GA and SA are generic search methods, HSAGA is also a generic search method. The model based on the actual data is implemented in a peak hour of Tehran’s wholesale spot market in 2012. The results of the simulations show that GA outperforms SA and HSAGA on computing time, number of function evaluation and computing stability, as well as the results of calculated Nash equilibriums by GA are less various and different from each other than the other algorithms.

Maintenance can be the factor of either increasing or decreasing system's availability, so it is valuable work to evaluate a maintenance policy from cost and availability point of view, simultaneously and according to decision maker's priorities. This study proposes a Partially Observable Markov Decision Process (POMDP) framework for a partially observable and stochastically deteriorating system in which inspection and maintenance optimal policies of Condition Based Maintenance (CBM) must be determined to maximize the average profit and availability of the system simultaneously. A recent exact method named Accelerated Vector Pruning method (AVP) and some other popular estimating and exact methods are applied and compared in solving such problems.

We address the throughput maximization problem for downlink transmission in DF-relay-assisted cognitive radio networks (CRNs) based on simultaneous wireless information and power transfer (SWIPT) capability. In this envisioned network, multiple-input multiple-output (MIMO) relay and secondary user (SU) equipment are designed to handle both radio frequency (RF) signal energy harvesting and SWIPT functional tasks. Additionally, the cognitive base station (CBS) communicates with the SU only via the MIMO relay. Based on the considered network model, several combined constraints of the main problem complicate the solution. Therefore, in this paper, we apply heuristic guidelines within the convex optimization framework to handle this complexity. First, consider the problem of maximizing throughput on both sides of the relay separately. Second, each side progresses to solve the complex problem optimally by adopting strategies for solving sub-problems. Finally, these optimal solutions are synthesized by proposing a heuristic iterative power allocation algorithm that satisfies the combinatorial constraints with short convergence times. The performance of the optimal proposed algorithm (OPA) is evaluated against benchmark algorithms via numerical results on optimality, convergence time, constraints’ compliance, and imperfect channel state information (CSI) on the CBS-PU link.

Data mining and knowledge discovery are important technologies for business and research. Despite their benefits in various areas such as marketing, business and medical analysis, the use of data mining techniques can also result in new threats to privacy and information security. Therefore, a new class of data mining methods called privacy preserving data mining (PPDM) has been developed. The aim of researches in this field is to develop techniques those could be applied to databases without violating the privacy of individuals. In this work we introduce a new approach to preserve sensitive information in databases with both numerical and categorical attributes using fuzzy logic. We map a database into a new one that conceals private information while preserving mining benefits. In our proposed method, we use fuzzy membership functions (MFs) such as Gaussian, P-shaped, Sigmoid, S-shaped and Z-shaped for private data. Then we cluster modified datasets by Expectation Maximization (EM) algorithm. Our experimental results show that using fuzzy logic for preserving data privacy guarantees valid data clustering results while protecting sensitive information. The accuracy of the clustering algorithm using fuzzy data is approximately equivalent to original data and is better than the state of the art methods in this field.

An important export production of our country is pistachio which plays a great role in non-oil exporting, with almost over 400 million dollars annually foreign currency income. The shortage of water has been a serious problem in producing pistachio which can be solved by managing survey inputs and their optimum application. In this research through an empirical analysis and a two-stage-cluster sampling, 228 producers were interviewed. Then, based on two scenarios; profit maximization and cost minimization, optimum qualities of inputs were determined. The results show that optimum quantity of water is 7791 m3 per ha for profit maximization. With respect to the area of land under pistachio cultivation in Rafsanjan (100,000 ha), in case the farmers consume water based on optimum quantity, the problem of make over use of underground water source (nearly 154 million) can be solved. In this paper, water optimum quantity was investigated by using cost minimization and Data Envelopment Analysis (DEA).The results of this scenario show that if the farmers act based on cost minimization, water optimum quantity will be 7294/ha on the average. Focusing on the present water consumption average of9104.8 per producer, if the farmers act based on cost minimization, quantity of underground water used will be reduced to 181 million. Thus the problem of over using of underground sources will be removed if the farmers act on economic principles. The technical, allocative and cost efficiency of producers was calculated, and the results show that the producers" efficiency average is 0.721, 0.819, 0.6 1 3respectively. Difference of producers" efficiency show that we can increase yields by optimum use of inputs