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In this research, a model of the possible dynamic cumulative size of a single product with random demand and capacity limitations in the production and transportation network has been developed to minimize the total costs of maintenance, start-up, and overtime. In the proposed model, there is a possibility of transferring the launch of goods to the next period, which plays a significant role in reducing the cost of restarting. Static uncertainty strategies and various service level performance indicators are considered to limit the amount of post-drop demand. Common commercial agents have been used. It is demonstrated by the sensitivity analysis of the proposed model and its analysis at the level of different services that it is performing and efficient, and it also provides a more realistic perspective for planning in the face of uncertain demand conditions.

Productivity of production systems depends on proper planning in various fields such as production, maintenance and repairs, inventory control, etc. Considering the major and direct impact of the production plan on other fields, it is necessary to prepare this plan with a suitable approach so that the production systems can be properly managed and costs can be reduced as much as possible. Limitations such as the need to supply a certain amount of demand in a certain period of time, the possibility of producing defective products, and the imposition of costs due to frequent system setup, make managers a challenge in providing an accurate plan. In this research, an attempt is made to plan the production process in a multi-period single product system by taking advantage of the Markov decision process and taking into account the above-mentioned constraints. In the following problem, using the stochastic dynamic programming technique, the best possible action will be selected and in other words, the best production volume will be determined for each period. The goal is to determine the volume of production in each period and for different states, in such a way that at the end of the permitted periods of production, the entire demand can be covered with the lowest cost. The effectiveness of the model is examined by solving a numerical example and analysis of the effects of changing parameters on the results of the problem has been presented. The results show that there is a direct relationship between the average cost rate and the costs related to setting up the system and producing each product unit, and the relationship between the said rate and the probability of producing healthy products and the production capacity is inverse. Increasing the production capacity after a certain threshold will not affect the average cost rate.

Production planning in a production system analyzes and calculates the amount of production of each of the products that can be produced in a production system in the future based on market conditions and conditions, customer needs, supply chain of raw materials, etc., and its purpose is to reduce production costs, maintaining or increasing customer satisfaction, competition in the market, diversification of manufactured products, etc. Several methods have different characteristics when preparing the production plan, and the appropriate method is chosen depending on the type of production system and the conditions governing it. This research deals with preparing the dairy product production plan of Pegah pasteurized milk company in East Azerbaijan to reduce production costs; using the system dynamics approach to prepare this plan, the influential factors in preparing the production plan and their mutual effects on each other were studied. Commented. For this purpose, the cause-effect model of these factors has been prepared, and its flow chart has been simulated in Vensim software with four different scenarios. The simulation results show that the best situation for the factory and society is to encourage the increase or continuation of the consumption of dairy products, which reduces total costs by 12%. Otherwise, with the increase in product prices and decrease in consumer demand, the factory should keep the variety of manufactured products constant and reduce the production volume of each product.

The complex conditions prevailing in the industries and the increasing costs of production equipment and machinery and competitiveness in gaining market share, show the role and importance of production planning and maintenance with other parts of the industry. Integrating such decisions can take fundamental steps to reduce costs and increase quality. Maintaining and creating the continuity of production activities depends on accurate and correct planning of production, maintenance activities and how to support these processes. The need for integration and coherence in the simultaneous planning of such activities causes a lack of rework and parallel work and obstacles and delays and inconsistencies at different levels of production.

In this research, a two-objective mathematical model of production planning and repairs with limited resources is presented in conditions of uncertainty.

The results of comparing accurate and meta-innovative solutions show the improvement in the company's products and the optimal use of material and human resources. Sensitivity analysis also shows that the failure rate of the machine before and after preventive maintenance has a great impact on the value of the objective function of the mathematical model. The results show that the average error of the ant algorithm is only 3%. This is while the average solving time in GAMZ is 45,000 seconds, while the average solving time of the ant algorithm is about 354 seconds.

This shows that the ant algorithm has a very small amount of error with much less time and therefore the efficiency of this solution method can be well explained.

This paper presents a new mixed integer nonlinear mathematical model to design dynamic cellular manufacturing systems (DCMSs). In the dynamic environment, the entire planning horizon is divided into multiple smaller periods, and each period has dierent product mix and part demand. In reality, production quantity may not be equal to the demand as it may be satised from inventory or backorders. Thus production quantity should be determined through PP decisions in order to determine the number and type of machines to be installed in manufacturing cells. Consequently, the manufactured cells should be recongured in each period. Design and implementation of an eective CMS involves many issues such as cell formation (CF), production planning (PP), layout design, and scheduling. The proposed model is concurrently making the CF and PP decisions and incorporates several design features including multi-period production planning, alternate routings, system reconguration, duplicate machines, lot splitting, workload balance among machines and cells, cell size limits, and material ow between machines. Machine capacity is also considered. Capacity requirement and the number of required machines in each cell are dened as decision variables and for the rst time, calculated based on ow shop perspective. The model forms independent cells and determines all related decision variables during each period of the time horizon according to the availability of the machines at the beginning of the rst period. The objective is to minimize the total costs of intra-cell material handling, machine operating, maintenance and setup, cell reconguration and forming, inventory holding and backorder, and production. Linearization techniques are used to transform the suggested non-linear programming model into a linearized formulation. Using ow shop perspective to calculate capacity requirement is considered in this paper for the rst time and so to verify the performance of the proposed model, a numerical example with randomly generated data is solved by a branch-and-bound (B&B) method under the Lingo 12.0 software.

In this research, we develop an economic production quantity model in a three layers supply chain with two different structures. Aforementioned chain composed of a supplier, a manufacturer and multiple retailers. In this chain, the supplier transforms raw material to the semi-finished product and sends them to the manufacturer and then the manufacturer transmutes them to the finished product and delivers to the retailers in order to satisfy market demand. The retailers replenish their inventory at the same time and demand of each retailer is different due to essence of various demand customers and according to the decisions of the chain’s members, two structures of non-integrated and integrated supply chains are surveyed. In this research, we will use the Stackelberg approach to solve the presented models. The ordering cycle of retailers is the decision variable of the model. The main aim of this study is to develop an inventory and production model in three layers supply chains in order to minimize the total cost of chain by utilizing the optimal inventory control policy. At last, numerical examples are presented for each structure of supply chains.

Bi-level programming is a mathematical programming, which there is another optimization problem at its constraints. According to the current fierce competition between large production companies to obtain a greater share of the market, this study develops a bi-level robust optimization model as the leader and the follower using Stackelberg game in the field of production planning. The leader company with higher leverage has decided to produce some new products that can be replaced with the existing products. The follower companies as a competitor, similar to the leader company, are looking to sell more. The follower companies do not have any intent and ability to produce such new products. Prices of the new products are determined using the tensile relations, which presented between the uncertain demand and price, creating the game between two levels of the model. After the linearization, the bi-level robust model is transformed to standard single-level model using conditions of Karush–Kuhn–Tucker (KKT). Finally, the accuracy and efficiency of the developed model have been verified by using the real data of Sarvestan Sepahan Company in Isfahan as the leader in the competitive market.

Reducing production costs has become one of the most important concerns, due to the economic development and increasing competitiveness in industries. To achieve this goal, considering real conditions is important. In this presentation, we investigate the production scheduling of a Pickling Line in Esfahan's Mobarakeh Steel Company called Pickling Line Scheduling (PLS). The problem is to generate multiple production turns for the Pickling Line coils and at the same time determine the sequence of these turns and select coils then the sequence coils of these turn so that the productivity and product quality both maximized while the production cost minimized. We formulate this problem as a mixed integer nonlinear program and propose a heuristic algorithm to obtain satisfactory solutions. Results on real production instances show heuristic algorithm is more effective and efficient with comparison to manual scheduling in Esfahan's Mobarakeh Steel Company.

This paper studies multi-stage, multi-product, multi-period production planning problem with sequence dependent setups in closed-loop supply chain. Manufacturing and remanufacturing processes of each product are regarded consequently, and both of them could be performed if machine is ready for processing corresponding product. To formulate the problem, a mixed-integer programming (MIP) model is presented and four heuristic algorithms using rolling horizon and a genetic algorithm are developed to solve the model. First two heuristic algorithms are developed based on the original model, but to solve the large instances the other two heuristics and the genetic algorithm are based on the simplified model, which is obtained by elimination of non-permutation sequences of original model solution space. To calibrate the parameters of the proposed genetic algorithm, Taguchi method is applied. The numerical results indicate the efficiency of the proposed meta-heuristic algorithm against MIP-based heuristic algorithms.

In recent years, increased use of internet to conduct business electronically and globalization of business have forced producers of goods to re-examine the production and distribution of their products. Cellular manufacturing (CM) is an innovative manufacturing strategy, which is derived from a group technology (GT) concept. This approach can be used to improve both flexibility and efficiency in today's modern competitive manufacturing environments, such as flexible manufacturing systems (FMS). Some benefits of CM performance are reduction of setup time, reduction of work-in-process inventory, reduction of material handling cost, machine utilization improvement, and quality improvement. In a dynamic environment, we require the development of organizations and facilities to be more flexible. This work directed us to combine the dynamic model of cellular manufacturing system and supply chain, taking into account different issues such as the existence of multi plants, multi markets, several warehouse, different suppliers, multi periods, and reconfigurations. In this paper, an integrated dynamic cellular manufacturing system model is proposed, which takes into account both production planning and supply chain design. The objective of this model is to reduce costs such as inter-cell movement cost, intra-cell movement cost; to keep parts in stock, outsourced parts, establishment of warehouse, replacement of parts are shipped from the plant to the warehouse, from warehouse to markets, from plant to market, and so on. In addition, considering the candidate sites for the construction of warehouse, locating the warehouses outside the factory is discussed, which has not been considered in previous papers. Furthermore, with adding the phrase of machine splitting in the objective function and constraints, it is attempted to improve the dynamic cellular manufacturing system. This term prevents the excessive split of one type of machine in the several cells and optimizes the placement of existing machines. Finally, computational results are investigated and solved through GAMS software to show the validity and importance of the presented model.

Production-distribution (P-D) planning in supply chain leads to a coordination between production and distribution systems, and provides an integrated production and distribution plan in order to create a balance between the costs of production-distribution in supply chain and the level of customer satisfaction. P-D planning and optimization in the context of supply chain management have raised signicant interest for both researchers and practitioners over the past few years. However, demand variances of the retailers may generate the potential challenges for P-D problems, such as increasing supply chain inventory levels and increasing the bullwhip eect. The vendor managed inventory (VMI) can improve supply chain performance by decreasing supply chain inventory levels. It is an important business mode in supply chains where the vendor is responsible to manage the inventory held at the retailer site. In addition, the bullwhip eect can be reduced using VMI. Although many researchers were committed to solve P-D problems in the supply chain environment, they did not pay attention to integrate P-D planning with VMI. In this paper, a multi-objective non-linear mathematical model for production-distribution planning based on vendor-managed inventory (P-D-VMI) is presented for a three-level supply chain, including multiple external suppliers, a single manufacturer, and multiple retailers. The aim of this paper is to minimize the total cost of the manufacture, total cost of the retailers, and total ow times. The inventory of the retailers is managed by the manufacturer, and the common replenishment cycle policy is established between the manufacturer and all the retailers. Then, min-max method and an improved genetic algorithm are utilized to solve the proposed model, and several problems are designed to demonstrate their validation and eciency. Results show that decreasing the retail price elasticity leads to an increase in demand and distribution time along with a reduction in the total cost of the manufacture and retailers, which is more tangible for the retailers. Finally, results conrm the applicability of the proposed model and solution methods.

G‌r‌e‌e‌n s‌u‌p‌p‌l‌y c‌h‌a‌i‌n m‌a‌n‌a‌g‌e‌m‌e‌n‌t (G‌S‌C‌M) h‌a‌s b‌e‌e‌n g‌r‌o‌w‌i‌n‌g i‌n r‌e‌c‌e‌n‌t y‌e‌a‌r‌s w‌i‌t‌h i‌n‌t‌e‌r‌e‌s‌t f‌r‌o‌m b‌o‌t‌h a‌c‌a‌d‌e‌m‌i‌a a‌n‌d i‌n‌d‌u‌s‌t‌r‌y. A p‌r‌e‌p‌o‌n‌d‌e‌r‌a‌n‌c‌e o‌f s‌p‌e‌c‌i‌a‌l i‌s‌s‌u‌e‌s d‌e‌v‌o‌t‌e‌d t‌o t‌h‌i‌s t‌o‌p‌i‌c i‌n l‌e‌a‌d‌i‌n‌g o‌p‌e‌r‌a‌t‌i‌o‌n‌s a‌n‌d s‌u‌p‌p‌l‌y c‌h‌a‌i‌n m‌a‌n‌a‌g‌e‌m‌e‌n‌t (S‌C‌M) j‌o‌u‌r‌n‌a‌l‌s a‌t‌t‌e‌s‌t‌s t‌o t‌h‌i‌s t‌r‌e‌n‌d. T‌h‌e c‌l‌o‌s‌e‌d l‌o‌o‌p s‌u‌p‌p‌l‌y c‌h‌a‌i‌n (C‌L‌S‌C) i‌s a b‌r‌a‌n‌c‌h o‌f G‌S‌C‌M w‌h‌i‌c‌h i‌n‌c‌l‌u‌d‌e‌s r‌e‌c‌y‌c‌l‌i‌n‌g a‌n‌d r‌e‌m‌a‌n‌u‌f‌a‌c‌t‌u‌r‌i‌n‌g a‌c‌t‌i‌v‌i‌t‌i‌e‌s f‌o‌r r‌e‌a‌s‌o‌n‌s o‌f e‌n‌v‌i‌r‌o‌n‌m‌e‌n‌t p‌r‌o‌t‌e‌c‌t‌i‌o‌n. C‌L‌S‌C h‌a‌s a‌t‌t‌r‌a‌c‌t‌e‌d g‌r‌o‌w‌i‌n‌g a‌t‌t‌e‌n‌t‌i‌o‌n i‌n r‌e‌c‌e‌n‌t y‌e‌a‌r‌s, d‌u‌e t‌o t‌h‌e s‌c‌a‌r‌c‌i‌t‌y o‌f n‌a‌t‌u‌r‌a‌l r‌e‌s‌o‌u‌r‌c‌e‌s a‌n‌d i‌n‌c‌r‌e‌a‌s‌e‌d e‌n‌v‌i‌r‌o‌n‌m‌e‌n‌t‌a‌l c‌o‌n‌c‌e‌r‌n‌s. I‌n t‌h‌i‌s p‌a‌p‌e‌r, a m‌u‌l‌t‌i p‌e‌r‌i‌o‌d m‌o‌d‌e‌l i‌s p‌r‌o‌p‌o‌s‌e‌d w‌h‌i‌c‌h c‌o‌n‌t‌a‌i‌n‌s p‌r‌o‌d‌u‌c‌t‌i‌o‌n p‌l‌a‌n‌n‌i‌n‌g a‌n‌d i‌n‌v‌e‌n‌t‌o‌r‌y c‌o‌n‌t‌r‌o‌l i‌n a c‌l‌o‌s‌e‌d l‌o‌o‌p s‌y‌s‌t‌e‌m. T‌h‌i‌s m‌o‌d‌e‌l i‌s d‌e‌v‌e‌l‌o‌p‌e‌d f‌r‌o‌m t‌h‌e w‌o‌r‌k o‌f S‌h‌i e‌t a‌l. (2011), w‌h‌i‌c‌h c‌o‌n‌s‌i‌d‌e‌r‌s r‌e‌t‌u‌r‌n f‌l‌o‌w a‌n‌d u‌n‌c‌e‌r‌t‌a‌i‌n‌t‌y f‌o‌r p‌r‌o‌d‌u‌c‌t‌i‌o‌n p‌l‌a‌n‌n‌i‌n‌g i‌n a c‌l‌o‌s‌e‌d l‌o‌o‌p s‌y‌s‌t‌e‌m. I‌n t‌h‌e p‌r‌o‌p‌o‌s‌e‌d m‌o‌d‌e‌l, w‌e h‌a‌v‌e a‌d‌d‌e‌d i‌n‌v‌e‌n‌t‌o‌r‌y c‌o‌n‌t‌r‌o‌l, p‌r‌i‌c‌i‌n‌g, l‌a‌n‌d f‌i‌l‌l‌i‌n‌g, r‌e‌c‌y‌c‌l‌i‌n‌g a‌n‌d o‌u‌t s‌o‌u‌r‌c‌i‌n‌g (m‌a‌n‌u‌f‌a‌c‌t‌u‌r‌i‌n‌g, r‌e‌m‌a‌n‌u‌f‌a‌c‌t‌u‌r‌i‌n‌g a‌n‌d r‌e‌t‌u‌r‌n) t‌o t‌h‌e f‌o‌r‌m‌e‌r m‌o‌d‌e‌l. S‌i‌n‌c‌e t‌h‌i‌s m‌o‌d‌e‌l c‌o‌n‌s‌i‌d‌e‌r‌s i‌n‌v‌e‌n‌t‌o‌r‌y c‌o‌n‌t‌r‌o‌l, i‌t s‌h‌o‌u‌l‌d b‌e a m‌u‌l‌t‌i p‌e‌r‌i‌o‌d m‌o‌d‌e‌l. F‌o‌r d‌e‌c‌r‌e‌a‌s‌i‌n‌g t‌h‌e c‌o‌m‌p‌l‌e‌x‌i‌t‌y o‌f t‌h‌e m‌o‌d‌e‌l, i‌n t‌h‌i‌s p‌a‌p‌e‌r, i‌t i‌s a‌s‌s‌u‌m‌e‌d t‌h‌a‌t o‌n‌l‌y o‌n‌e p‌r‌o‌d‌u‌c‌t c‌a‌n b‌e p‌r‌o‌d‌u‌c‌e‌d. B‌u‌t, i‌t c‌a‌n b‌e u‌s‌e‌d f‌o‌r m‌u‌l‌t‌i p‌r‌o‌d‌u‌c‌t‌s t‌o‌o. I‌t i‌s a‌s‌s‌u‌m‌e‌d t‌h‌a‌t a p‌e‌r‌c‌e‌n‌t‌a‌g‌e o‌f u‌s‌e‌d p‌r‌o‌d‌u‌c‌t‌s c‌a‌n b‌e r‌e‌m‌a‌n‌u‌f‌a‌c‌t‌u‌r‌e‌d b‌u‌t a‌l‌l c‌a‌n b‌e r‌e‌c‌y‌c‌l‌e‌d. T‌h‌e g‌e‌n‌e‌t‌i‌c a‌l‌g‌o‌r‌i‌t‌h‌m (G‌A) i‌s s‌u‌g‌g‌e‌s‌t‌e‌d f‌o‌r s‌o‌l‌v‌i‌n‌g t‌h‌e p‌r‌o‌p‌o‌s‌e‌d m‌o‌d‌e‌l. S‌o‌m‌e r‌a‌n‌d‌o‌m e‌x‌a‌m‌p‌l‌e‌s a‌r‌e s‌o‌l‌v‌e‌d b‌y G‌A f‌o‌r i‌l‌l‌u‌s‌t‌r‌a‌t‌i‌n‌g t‌h‌e p‌r‌o‌p‌o‌s‌e‌d m‌o‌d‌e‌l a‌n‌d t‌h‌e s‌o‌l‌v‌i‌n‌g a‌p‌p‌r‌o‌a‌c‌h. F‌o‌r e‌v‌a‌l‌u‌a‌t‌i‌n‌g t‌h‌e s‌o‌l‌v‌i‌n‌g a‌p‌p‌r‌o‌a‌c‌h, o‌n‌e o‌f t‌h‌e m‌o‌d‌e‌l's c‌o‌n‌s‌t‌r‌a‌i‌n‌t‌s i‌s e‌l‌i‌m‌i‌n‌a‌t‌e‌d a‌n‌d t‌h‌e e‌x‌a‌m‌p‌l‌e s‌o‌l‌v‌e‌d w‌i‌t‌h‌o‌u‌t t‌h‌a‌t c‌o‌n‌s‌t‌r‌a‌i‌n‌t. H‌e‌n‌c‌e, t‌h‌e u‌p‌p‌e‌r l‌i‌m‌i‌t o‌f t‌h‌e o‌b‌j‌e‌c‌t‌i‌v‌e f‌u‌n‌c‌t‌i‌o‌n i‌s d‌e‌f‌i‌n‌e‌d. T‌h‌e r‌e‌s‌u‌l‌t‌s s‌h‌o‌w t‌h‌a‌t t‌h‌e s‌o‌l‌v‌i‌n‌g a‌p‌p‌r‌o‌a‌c‌h h‌a‌s a m‌a‌x‌i‌m‌u‌m a‌v‌e‌r‌a‌g‌e e‌r‌r‌o‌r o‌f 3.59 p‌e‌r‌c‌e‌n‌t. N‌o‌t‌e t‌h‌a‌t t‌h‌e u‌p‌p‌e‌r l‌i‌m‌i‌t o‌f t‌h‌e o‌b‌j‌e‌c‌t‌i‌v‌e f‌u‌n‌c‌t‌i‌o‌n i‌s i‌n‌f‌e‌a‌s‌i‌b‌l‌e f‌o‌r t‌h‌e o‌r‌i‌g‌i‌n‌a‌l e‌x‌a‌m‌p‌l‌e. H‌e‌n‌c‌e, G‌A i‌s a‌n a‌p‌p‌r‌o‌p‌r‌i‌a‌t‌e a‌p‌p‌r‌o‌a‌c‌h f‌o‌r s‌o‌l‌v‌i‌n‌g t‌h‌e p‌r‌o‌p‌o‌s‌e‌d m‌o‌d‌e‌l.

In the steel industry that involves with large amount of wealth, labor and energy flow a small improvement in planning has significant consequences such as reducing costs, energy consumption, production time, delivery time and also increasing customer satisfaction. This research aim to present a efficient model in order to schedule the orders in the product line of cold rolled in the Mobarakeh Steel Company. The model is solved by applying real data of Mobarakeh Steel cold rolling in two stages. At first the third model without considering zero-one constraint is solved. Then the zero-one and some other constraints obtained from heuristic algorithm are added to the model and the model is solved by GAMS. Each product Production quantity on each machine was obtained in a three month horizon. Actual amount production of cold-rolled machines has been compared with the outputs of the model. The results show that by implementing this model, the number of delivered orders and also usage of the machines capacity are increased.

In any organization, there are many stakeholders, whosepoints of view should be taken into account when planning.The involvement of dierent stakeholders in thedecision making process is an important feature to beconsidered, not only for interpretation and making decisionsbased on their judgment, but also for their participationin research and the decision making process.A decision is evaluated as a suitable decision when allstakeholders are satised with the nal decision, whichmeans that the stakeholders should reach a consensuson the decision. Stakeholder participation in the decisionmaking process enhances respect for their opinions,as well as improving the learning process in the organizationand better understanding of the studied system.Involvement of dierent stakeholders can improve theperception of a problem because of their diverse information,which may be ignored in the presence of justone stakeholder. Therefore, in any planning, it is necessaryto consider all stakeholder objectives to reacha compromise. Stakeholder objectives may be in con-ict with each other, and a production plan based on just one stakeholder, however important he/she may be, may create problems in the organization. To overcome these problems, this paper intends to provide a set of linear programming models for each individual stakeholder, with their objectives, in order to discover whether or not stakeholder viewpoints are identical. If the solutions of the models are the same, then, we can claim there are no major con icts between the stakeholders. Otherwise, it is necessary to aggregate the individual models to obtain a unique model and, therefore, a single solution. To do this, a multi-objective programming model is established, which is an aggregation of individual linear programming models, in order to consider dierent stakeholder objectives. Solution of the aggregated model, using the LP metric method, can provide the nal solution for an organization that satises stakeholder viewpoints as much as possible. The aim of the paper is to provide a methodology to consider dierent stakeholder viewpoints, with their objectives, in discontinuous production planning systems. Aggregation of individual stakeholder models to obtain a unique solution has been studied via multi-objective programming. The methodology has been applied to an electrical manufacturing company to show the abilities of the methodology. The results of the study show that company stakeholders are relatively satised with the solutions. However, there is some small dissatisfaction, which may always exist.

Cellular manufacturing system is one of the most important applications of group technology. Design of this system involves many structural and operational issues, in which the cell formation and production planning are two important steps. In this paper, a new mathematical model is proposed for integration of cell formation and production planning problems with the aim of minimizing the overall costs such as machine, inter-cell and intra-cell movements, reconfiguration, tool consumption inventory holding, backorders and partial subcontracting based on tooling available in dynamic condition. Since the cell formation problem is NP-hard, an extended particle swarm optimization is presented. In the proposed algorithm, we use the local best for updating the particle position and re-initialize the worst particles positions to increase diversity and prevent premature convergence. Comparison of the proposed algorithm with LINGO 8.0 software in small size problem and with the standard particle swarm optimization in large size problem shows the efficiency of the presented approach.

In this paper, a hybrid method for limited resource allocation and leveling in complex multi-stage, multi-product and multi-period production planning problems with aim of lot-size determination and total cost minimization has been proposed. This problem consists of multiple products with sequential production processes that are produced in different periods to meet the customers demand. By determining decision variables, production capacity of machines and customers demand, an integer linear program is developed to minimize the total set-up, inventory holding and production cost. A three-stage approach has been developed to solve the problem. In the first stage, the primary problem is divided into several sub-problems using a heuristic algorithm based on the limited resource Lagrangean multipliers. In this case, each sub-problem could be solved using more simple methods. In the second stage a new approach is proposed to solve these sub-problems combining the genetic algorithm with a neighborhood search technique. In the third stage resource leveling is performed among sub-problems to obtain a better solution. In this case, lot-size for each product is determined during the planning periods. This paper's objectives have been evaluated and verified through several empirical experiments.