جواد بهنامیان

In this paper, the problem of real-time scheduling of multi-factury production network in the smart manufacturing system with job shop environment is studied. In this smart manufacturing system, a number of independently owned factories are joined together to form a multi-agent production network, which is also called a virtual production network. In such a network, each factory focuses on its interests and communicates with each other by sharing information such as machine breakdowns and job transfers. Therefore, it can be stated that studying the problem of distributed scheduling in the environment of smart factories is important and will have a significant effect on obtaining desirable and ideal results. At first, a bi-objective mixed integer linear programming model is presented; then an approach to solve the dynamic real-time scheduling problem is proposed. Considering the successful applications of the Lagrangian relaxation algorithm in solving scheduling problems, in this research, the improved Lagrangian relaxation algorithm is used to solve the problem. To examine the performance of the proposed algorithm, its results were compared with solving the original model that was solved by the augmented epsilon constraint method. The obtained results showed that the proposed Lagrangian relaxation algorithm has a better performance than the augmented epsilon constraint method

When a disaster occurs, collecting and sharing disaster information about affected areas is one of the most important activities in order to optimal decision making for relief operations. Also because the infrastructures are destroyed or damaged after a disaster, WSNs and UAVs can be used to receive online data from the affected areas. This results in making a lot of data with high rates of production through social networks as well as mobile phones of affected people in affected areas as well as WSNs and UAVs which indicates the necessity of Big Data. On the other hand cloud computing is a service, independent of device and location where computing can be performed at high speed and this shows the need for cloud computing as well. In other words these two technologies provide real-time data analysis not just for emergency situation identification in the affected areas but also to rescue the affected people. In this paper we try to provide a comprehensive analysis of application of Big Data, Cloud Computing, Wireless Sensor Networks (WSN) and Unmanned Aerial Vehicles (UAV) in relief problems.

Receiving fast, flexible, reliable and low-cost delivery services by customers is one of the important challenges for the distribution of goods, especially in urban areas. After that, with the increase in demand and as a result, the increase in vehicles for the purpose of goods' transportation, it causes congestion in urban transportation networks. Therefore, in this study, the multi-fleet feeder vehicle routing problem is investigated in a situation where several trucks and motorcycles cooperate with each other to satisfy the demand at the same time. The feeding vehicle routing problem consists of a heterogeneous fleet of vehicles, including trucks and it makes it possible for motorcycles to pass in high-traffic areas and distribute urban traffic easily.  In fact, the feeder approach in the VRP is to reduce the number of times of returning to the main depot for loading and to save the cost and time of tours. Here, at first, a mathematical model is presented, then, due to the high complexity of the mixed integer programming model and in order to reduce the runtime of solving the model in large dimensions, the Lagrangian relaxation algorithm with the sub-gradient optimization approach is proposed. The results showed that with the increase in the dimensions of the problem, the runtime of the proposed algorithm is less compared to the outputs of GAMS. Also, the runtime saving resulting from solving the model with the Lagrangian relaxation algorithm is significant, and as a result, this algorithm is effective for solving the model.

Today, due to some challenges and competition, such as external pressures, factories are forced to reduce production time, traditional centralized production scheduling is not flexible enough to respond to rapid market changes. In such an environment, factories decide to merge and form a multi-factory production network to work more closely together.  In this research, the multi-factory scheduling problem is considered, which factories belong to a company. The problem is assigning the jobs to appropriate factory and scheduling jobs on machines in each factory. In this paper, it is assumed machines in each factory are unrelated parallel machines. For scheduling jobs on machines sequence-dependent setup times are considered. After proposing a novel mixed integer linear programming model for the problem which is a combination of two types of modeling based on sequence and assignment, we developed an evolutionary metaheuristic namely imperialist competitive algorithm (ICA) to minimize the maximum completion time or makespan among the factories. We compare the obtained solutions using the proposed ICA with those using an adopted genetic algorithm to show the efficiency of the proposed algorithm. Finally, the results are reported. Numerical results show that the proposed algorithm has good performance.

The aggregate production planning has been constantly among the inseparable elements of the production. Regarding the complexity of production conditions, this planning plays a critical role in the success of large manufacturing companies. Demand forecasting is among the effective factors that lead to cost reduction in aggregate production planning. Since the forecast does not exactly match reality, it is necessary to minimize the prediction error as much as possible. A wrong forecast of demand leads to a production stock-out or backlog. On the other hand, the correct forecasting in the production planning leads to risk reduction and performance improvement in the company's business. Due to the fluctuating and nonlinear trend of demand and the variables affecting it in the different periods, the linear models have low efficiency in achieving asymmetric changes. Therefore, in this study, for the first time, the Markov switching model is applied to predict demand in the problem of aggregate production planning. In this regard, after demand predicting, the decision variables and total costs are estimated, and the results are compared with the actual total costs. The obtained results showed that the Markov switching model has better performance compared to the autoregressive moving average (ARMA) and vector autoregressive (VAR) models based on the cumulative absolute forecast error (CAFE) criteria and the aggregate production planning costs.

As a relatively emerging topic in optimization, green routing could reduce the fixed and variable costs induced by different parts of a routing and transportation system and the costs imposed by pollution. This study proposes green transportation routing with a time window under confirmed stable conditions that consider transportation capacity restrictions, speed and time of delivery, and allocation of authorized drivers. This being the case, the goals of selecting the shortest route to goods transfer with the lowest costs caused by pollution, overdue fines, and maintenance of costs could be attained. Accordingly, the Lagrangian relaxation algorithm was employed, and the mathematical model of mixed-integer was developed to address the problem. Lagrange coefficient takes advantage of subgradient methods and closed methods. Lagrangian relaxation is applied separately to address two restrictions. The algorithm saves time by immediate problem solving than the Baron approach. These papers seek to simultaneously use complex restrictions close to actual incidents and could be more similar to natural conditions than other developed models.

This paper studies the flexible job shop scheduling problem with parallel machines by considering cleaner production criteria, dual human-machine resources, job release date, and machine speed-dependent processing time. The objective functions of this problem include minimizing the sum of earliness and tardiness and the speed increasing. Here it is assumed that the speed of the machines can be increased to reduce the completion time while the increasing the speed leads to increasing the noise pollution in the production environment, and due to the cleaner production approach which is a preventive approach, an attempt has been made to reduce the amount of noise pollution by minimizing the speed increasing. In this regard, first, a mixed integer-programming model was developed, and since the model is bi-objective and NP-hard, a NRGA is proposed and the obtained results are compared with the NSGAII considering some multi-objectives criteria. The results show that the proposed algorithm considering the MID criterion in instances with 10 and 25 jobs and considering the RAS criterion in instances with 25 and 100 jobs have better performance compare to the NSGAII. Furthermore, the TOPSIS method is also used for analysis and the results show the efficiency of the proposed algorithm.

This paper deals with production scheduling in a flexible flowshop with several factories trying to satisfy market demand by creating a network through a distributed network. Although these factories have their own flowshop system consisting of stages with a number of parallel machines, each job must pass through these stages to be completed; in some cases, due to the long queues in one factory, some jobs are sent to other factories to reduce the overall completion time. In other words, in this system, it is assumed that each factory, after satisfying the demand of its region, can cooperate with other factories in order to provide economic benefits and increase sales as a result of greater profitability of the production network, which means that if for any reason in the process, the production of products is disrupted or the amount of load of factories is too much, in order to improve the overall objective function, it is possible that some jobs be sent to other factories for processing. Multi-factory production takes place in several factories, which may be geographically distributed in different locations, in order to comply with and to take advantage from the trend of globalization. This allows them to be closer to their customers, to employ professionals, to comply with local laws, to focus on a few product types, to produce and market their products more effectively, and respond to market changes more quickly. Here, after introducing a flexible flowshop in the distributed network structure, a model is proposed for the problem considering the holding costs in the buffers and heterogeneity of factories in the production network. Finally, after solving the model using GAMS software, the Lagrange relaxationalgorithm has been developed for it. By comparing the GAMS and output results of the Lagrange relaxation algorithm, it is concluded that the proposed algorithm is of very high efficiency.

This study analyzes the portfolio optimization model, by considering the financial management and investment science in order to evaluate risks and return in regard with restrictions such as buyers’ assets for purchasing per share. Accordingly, a novel model is designed as linear programming in order to optimize the investment portfolio, considering the expected rate of return, the minimum risk, and the buyer’s assets. After the introduction of the model as linear programming and expressing the related limitations, different types of investments which an investor can consider in order to form an investment portfolio were studied. Finally, an approach is proposed to solve the model by using the genetic algorithm, and is implemented and analyzed in regard with a real example. According to the results of this study, the new model reduced downside risk in comparison with previously proposed models, in a manner that its stair descent continues as the number of shares under study increases.

In the pharmaceutical supply chain, pharmaceutical products must be distributed among consumers with good quality at the right time and in the right place. Medicineis a product which affects the health of society and its timely delivery to consumers is of great importance. Therefore, it requires proper planning for its production and distribution. In this paper, we developed a model that minimize the cost of production, inventory, delivery, earliness and tardiness. We also assumed the uncertainty of demand and solved the linear mathematical model using stochastic programming and we solved the problem with stochastic programming. Also, due to the fact that the model with the objective function of earliness and tardiness with different delivery times of NP-hard problem for this problem, a hybrid genetic and variable neighborhood search algorithm were presented. Here, five scenarios were considered, the expected value of perfect information (EVPI) was measured and the obtained results were compared with the two-stage random-scheduling model. The computational results showed the efficiency of the developed model. Also, the results of the proposed hybrid algorithm were compared with the genetic algorithm, and the results showed that in terms of objective function, the hybrid algorithm has a much better performance compared to the genetic algorithm.

The low productivity of Iranian government organizations is one of the issues that has been repeatedly emphasized by internal and sometimes external evaluation institutions. Given the vital importance of productivity and its major impact on the achievement of organizations to the desired goals, as well as its important role in the development of the country, it seems necessary to examine the various dimensions and, in other words, to find the reasons of this issue. Despite the efforts made so far, a comprehensive model that can be used to consider all matters related to employees and to act 100% fairly and only on the basis of the final and real results of performance to determine their salaries is not provided. In government organizations, due to the many problems and obstacles, the accurate implementation of academic methods is not possible and, in fact, requires a strong will to rebuild the inflexible structure of seemingly private organizations (originally governmental). The issue of conflict of interest of an organization and its employees, especially in government organizations, is always raised. What an organization wants from each of its employees is to perform all the assigned tasks with the highest level of productivity and with the lowest expectations regarding issues such as salaries, benefits and bonuses, leave, incentives and etc. On the other hand, employees try to perform their tasks in the shortest time and receive the highest level of salary and benefits (and of course, regardless of productivity). This contradiction depicts the environment of the organization as a playground with the senior management of the organization on one side and the employees on the other side, each side trying to get the most profit and points from the other side. In other words, this campaign consists of continuously playing n two-player games between senior management and n employees, and the final profit of the organization is optimized when each of the parties is at least satisfied with the results of the game. The aim of this study is to provide a mathematical model using a combination of data envelopment analysis (DEA) and game theory that can be helpful in improving the productivity of the organization while meeting the demands of employees and managers. In this study, using data envelopment analysis as one of the non-parametric methods of evaluating the performance of decision-making units (DMU), we have tried to simulate the organization environment as a game environment. Also, using game theory strategies and proposing a platform based on blockchain technology, while evaluating existing strategies, we tried to provide solutions to improve productivity and ensure the interests of players.

The subject of the present study is first to calculate the efficiency of DMUs and determine the efficient and inefficient DMUs by the senior management of the organization and using DEA, and then according to the objective observations and accurate rooting done by the authors of the research and based on the reward/punishment payment system of a state-owned company in the field of electricity (structurally similar to other government organizations), using the principles Game theory and determining new equilibrium points a practical model to improve the productivity of government organizations is proposed. The data used in this study have been prepared from the review of the annual rulings of official personnel and with the cooperation of experts working in the financial fields and job classification of the electricity distribution company. Also, the statistical population of this research is the official employees of the company.

Determining a strategy solely for the purpose of gaining more financial resources can't put organizations on the path to excellence, and therefore it seems that determining new strategies and establishing new equilibrium points between senior managers and employees in new places can be effective. It is recommended that organizations stop overtime for employees, and employees perform their duties solely in the form of office time. On the other hand, considering that the main emphasis of this research is on government organizations that are funded by the government budget and therefore their performance is usually evaluated annually by higher institutions, obtaining high scores and obtaining top rankings in the results evaluations are key importance, and therefore one of the main strategies of organizations should be to save money while providing appropriate incentives to employees who seek to improve their efficiency to level one and keep the level of efficiency in this They are level and support the organization in achieving high scores.

Based on the results of this study, it was found that the main reason for the low productivity of Iranian government organizations is the lack of mutual understanding of senior managers and employees of each other's demands and, in fact, the lack of a model to simultaneously ensure the satisfaction of the interests of both parties when promoting productivity is the organization. In this study, it is suggested that the current equilibrium point of organizations is changed and in a new environment by adding a strategy to improve efficiency to level 1 instead of the strategy of doing overtime for employees and the strategy of increasing special encouragement instead of reducing The input for the new equilibrium point organization is determined by simultaneously selecting these two strategies. On the other hand, senior managers of organizations should avoid the view that generally views employees as a tool that has no claims and is completely obedient to the policies of managers and instead of exerting destructive discrimination in the organization, provide conditions for worthy employees to others set them as a valuable role model for themselves.

Carrying goods from points of supply to different customers is an important task in the supply chain. In this regard, the problem of vehicle routing is of particular importance. In this paper, the Vehicle Routing Problem with Time Windows (VRPTW) is presented using the concept of heterogeneities. The concept of heterogeneities is concerned with the ownership of fleet. Ownership heterogeneities occur when the private fleet is not sufficient and the company has to rent some vehicles from freight companies. Moreover, unlike prior attempts to minimize cost by minimizing overall traveling distance, the proposed model incorporates energy minimizing. In this paper, two different scenarios have been analyzed and for each of them a mathematical multi-objective model is proposed. The first scenario investigates VRPTW regardless of the concept of heterogeneity and in the second scenario, there are some rental vehicles provided by freight companies. In senario II, the number of these vehicles, the time of contract and generally their capacity must be specified. Therefore, the strategy of changing the capacity of the rental fleet is determined based on the proposed model. The proposed solution method of this paper is based on a hybrid artificial immune system, artificial fish swarm, and NSGAII. Metaheuristics finally, small and large-scale test problems are randomly generated, solved and compared by those algorithms. The computational results show that using rental fleet significantly saves costs and energy. Also, the proposed model can be used as a decision support system for carriers to investigate capacity strategies.

This study analyzes the portfolio optimization model, by considering the financial management and investment science in order to evaluate risks and return in regard with restrictions such as buyers’ assets for purchasing per share. Accordingly, a novel model is designed as linear programming in order to optimize the investment portfolio, considering the expected rate of return, the minimum risk, and the buyer’s assets. After the introduction of the model as linear programming and expressing the related limitations, different types of investments which an investor can consider in order to form an investment portfolio were studied. Finally, an approach is proposed to solve the model by using the genetic algorithm, and is implemented and analyzed in regard with a real example. According to the results of this study, the new model reduced downside risk in comparison with previously proposed models, in a manner that its stair descent continues as the number of shares under study increases.

The employee scheduling seeks to find an optimal schedule for employees according to the amount of demand (workload), employee availability, labor law, employment contracts, etc. The importance of this problem in improving the quality of service, health and satisfaction of employees and reducing costs, including in hospitals, military or service centers, has encouraged researchers to study. In this regard, nurse rostering problem is a scheduling that determines the number of nurses required with different skills and the time of their services on the planning horizon. In this research, by adding the nurses' shift preferences and number of consecutive working days constraints, an attempt has been made to make the problem more realistic. The objective function of the problem is to minimize the total cost of allocating work shifts to nurses, the cost of the number of nurses required to reserve, the cost of overtime from a particular shift, the cost of underemployment from a particular shift, the cost of overtime on the planning horizon, the cost of underemployment on the planning horizon and the cost of absence shift-working and non-working days preferred by nurses. To solve problem, after modeling the problem as a mixed-nteger program and due to the complexity of the problem, the differential evolutionary algorithm is used with innovation in its crossover operator. To validate the proposed algorithm, its output was compared with the genetic algorithm. The results show that the differential evolutionary algorithm has good performance in problem-solving. Keywords: Nurse Rostering Problem, Deferential Evolution Algorithm

Aiming at achieving performance goals and due to the need for satisfying sustainability regulations, appropriate supplier selection is put ahead of strategic and significant decisions. The functional relative-balance indices with the sustainability targets are dealt as the operations foundation in today's enterprises. Different from previous studies, this paper introduces a three-stage strategic approach based on the introduction, growth, and maturity of a product supposed to be delivered to the market. To this end, a sandcone methodology is developed in a way that the distinct and aggregate significances of the measures are taken into account. The least thresholds of reaching to both sustainability and functional goals are set, respectively, by a governmental entity and a manufacturer. In addition, in order to approach the aggregational improvement, sustainable balance and identification of suppliers' categories, a new multi-stage heuristic algorithm is utilized at each period. This methodology provides a mechanism whereby suppliers are evaluated according to their current status and the performance records as well. The results of the numerical study indicate that adopting the discussed algorithm can be very useful in developing long-term relationships with deserving and stable suppliers.

In this paper, a multi objective multi stage stochastic model is proposed to portfolio selection. This model takes into account both the investment goal and risk control at each stage. A scenario generation method is proposed that acts as the basis of the portfolio management model. Scenarios for multistage portfolio management are proposed that use by consumption that rate of returns are not correlated during stages. One of the most important aspects of this model is using transaction cost in model and providing this ability that investors could add or withdrawal cash during time. In the end some numerical example are illustrated and model effectiveness proved. As is presented using stochastic programming with recourse and combination of this model with scenario generation model provides this possibility for investors to plan their medium and short term investing. As can be seen result of the model proved effectiveness of the model in financial markets. As result presented having such tool that investor could adjust his or her portfolio during time according to targets such as maximizing rate of return and minimizing risk of his or her decisions could bring powerful superiority in competitive financial markets.

Since the problem of optimizing the stock portfolio in nonlinear conditions with multi-objective functions cannot be effectively solved using traditional and traditional approaches, in this research, a metaheuristic approach is proposed to optimize the stock portfolio using the particle swarm optimization technique. This method has been implemented on a variety of investment portfolios with selection constraint and unconstraint conditions for the investor. By implementing the algorithm and in order to validate it, the results were compared with the genetic optimization algorithm. The computational results indicate that particle swarm optimization algorithm has a good performance, so that the results seem very promising and satisfactory for use in real conditions.

The purpose of solving the problem of vehicle routing is to find a suitable route taking into account the existing conditions in the transportation problem. In this case, considering the routing conditions with several depots along with imposing traffic restrictions on some vehicles on some routes, will create quite real and complex conditions. Furthermore, in some cases, it is necessary to deliver the customer demand by visiting several times. For this purpose, in this research, by simultaneous considering of multiple depots, split delivery and traffic restrictions, it has been tried to bring the conditions of the routing problem very close to real-world problems. In this paper, after presenting a mathematical model, the problem is solved in small-size instances using CPLEX solver. Then, due to NP-Hardness of considered problem, to solve it on a larger size instance, a variable neighborhood search algorithm is proposed. Finally, the simulated annealing algorithm is used to validate and evaluate the quality of the proposed algorithm. The computational results show that the proposed algorithm has good performance in terms of runtime and solution quality.

Recently, university courses time tabling problem has been attended as a main issue by most universities and educational departments. In this paper, a bi-objective model is proposed to formulate a university courses time tabling problem which aims to minimize the cost of assigning courses to professors and classes during time horizon and maximize professorschr('39') satisfaction through minimizing their total idle time, simultaneously. Furthermore, despite of previous similar works in this area in which predefined values are considered as the assignmentchr('39') cost coefficients, in this paper, for the first time, an analytical hierarchy process (AHP) approach is used to calculate these coefficients which enables educational departments to attend various criteria in considering assignment cost coefficients. Finally, to evaluate the efficiency of the proposed model, a real case study in industrial engineering faculty of Bu-Ali Sina University is done.

In general, numerous studies have paid a special attention to machine planning,job allocating andjob sequencing in scheduling problems to optimize makespan. Due to the relation among economy, energy and environmental concerns, energy use is one of the most important issues in different systems planning. In this paper, a scheduling of heterogeneous parallel machines is studied, in which the job process speed on every machine is settable. Since there is a direct link between used energy of machines and process speed, the purpose of the paper is to minimize total used energy and tardiness-related costs in delivering customers' demand. In order to optimizing the problem, two meta-heuristic algorithms, Memetic algorithm and Genetic algorithm, are developed, finally the results of both algorithms are analyzed and then compared to each other as well as to the results of the GAMS optimization software. Keyword: heterogeneous parallel machines scheduling, total lateness costs, Energy costs. Memetic algorithm

Organizations with integrated production and routing programs sometimes encounter traffic when they use vehicles for distribution. So there are risks such as an accident that could result in damage, loss of product quality, unavoidable delivery delays, or even irreversible impacts on costs and service time. Therefore, by taking account the risk of accident into the production routing problem, the model becomes closer to reality. In this study, a production routing problem with the purpose of reducing costs and the risk of accident in the transportation with outsourcing, multi-product and multi-period, is considered in which the production routing problem combines with the lot sizing and vehicle routing problem according the supplier's inventory management system. Since this is an NP-hard problem, after modeling the problem, to solve it, a Non-dominated Sorting Genetic Algorithm II (NSGA II) has been used. To examine the efficiency of the algorithm, the solutions of ε-constraint method in GAMS obtained in small-size instances have been compared with NSGA II. Finally, to validate the proposed algorithm and evaluate its performance in large-size instances, the results of NSGA II have been compared with multi-objective genetic algorithm using several indices. The obtained results showed that the NSGA II algorithm had better performance.

A flowshop problem with objective functions of minimizing makespan and energy cost has been investigated. Reducing production costs is one of the goals that industries always have in mind. Increasing public awareness about the energy issues creates a new attitude toward minimizing energy costs. In order to make the problem more compatible with the real-world conditions, the problem is considered under uncertainty. An existing research gap inspired this study. It is assumed that machines can use the three slow, normal and fast speeds to process jobs. At high speeds, consumption rate increases and completion time decreases, and vice versa. The difference in machine processing speeds yields different and contradictory values in the objective functions. Therefore, a method should be proposed in which, in addition to the order of jobs, the speed of machines could be determined. A mathematical model is presented, and then a simulation-based genetic algorithm is used to solve the problem on a large scale. Simulation is used for each evaluation of the objective function in the genetic algorithm to consider the uncertainty of processing times. Due to the stochastic processing time, the expected value model is used to deal with uncertainty. The computational results indicate that the algorithm and approach show a good performance.

Emphasizing the importance of competition and cooperation in supply chains promote the revitalization of game theory as a convenient tool for analyzing interactions, as well as how it affects the different levels of cooperation and coordination in a supply chain. Therefore, using the game theory concept, and based on a side-contract of partnership in profits and marketing costs, in which profit is shared between all the chain levels’ members and marketing expenses is shared between producer and retailer, a cooperation model has been designed with the aim of maximizing the supply chain profit.In this research for the first time in fuzzy enviormet and with considering discount costs, a mathematical modeling is proposed in which to get closer to real conditions, marketing cost is considered as a triangular fuzzy number. The proposed model has been solved using Genetic algorithm (GA), simulated annealing algorithm (SA) and a hybrid algorithm based on GA-SA, for some random examples, and the model has been validated using GAMS software.

In this paper, a hybrid model of fuzzy multi-criteria decision making is presented for the cases a large number of criteria, relationships or affiliation exists are between them. In this model, the DEMATEL method is used to determine the relationships between criteria and analytic network process (ANP) method to identify the weight of each criteria and the VIKOR method for optimizing the multi-criteria of ranking the best supplier. In this study, in order to cover the cases of decisions with ambiguous scenarios, rather than using absolute numbers, the linguistic variables are used. The proposed hybrid model can direct managers and experts organizations In order to improve their choices, especially when there are numerous criteria and there is dependence between them under conditions of uncertainty.The proposed method, reduces number of pairwise comparison matrix and volume of calculations and also increases calculation speed and the complexity of the problem is reduced. In order to showing the solving process a numerical example is presented. The computational results show that proposed hybrid method, in addition to ranking alternatives calculates the final weight of each criterion.

The conditions of the business environment of the manufacturing units and the multitude of factors affecting their performance have always emphasized the need for planning as one of the main tasks of management. In this regard, production planning in manufacturing companies is one of the most important tools for success. This paper attempts to present a comprehensive model for production planning in the industry using appropriate operations research -based techniques so that a suitable model of rational communication between operations can be formulated and so that decision makers can better plan and increases productivity. In this paper, after a brief introduction about one of the products of Ekbatan Gas Control Company, the production planning of this company will be carried out with the objective of cost reduction, demand satisfy minimizing surplus inventory in discrete production systems. In this study we intend to apply all parameters and variables of production in a comprehensive model, considering the production method of Ekbatan Gas Control Company. In the proposed goal programming model, simultaneously different and contradictory objective functions of the company have been considered. The approach proposed in this study increases the company's profit 3%. Also production of first product increases18% and the second increase 3%. It has also increased production capacity, and it satisfies 100% of the demand. The results of the model show a significant reduction in overtime, and only in the fourth period need to overtime for produce the first product. As consequences it eliminates the need for hiring and layoffs, and reduces their cost to 0. In fact, the Company will have a significant increase in number of production and profit with the samee number of manpower. The proposed model is expected to serve as a reference for future planning of the firm's corporate policies.