جستجوی مقالات مرتبط با کلیدواژه "برنامه ریزی تولید ادغامی" در نشریات گروه "مدیریت"

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.

Aggregate production planning is a multi-objective problem which is influenced by managerial preferences which is rarely considered with these preferences in many researches. In this paper, a multi-product multi-objective aggregate-production-planning model has been proposed and implemented in an industrial ball-valves manufacturing company. In the first phase, preferences of various product groups have been determined via a multiple-attribute-decision-making method which is used as an input for the second phase. To do this, one of the outranking methods has been used because of the variety in the dimension and the nature of different attributes. In the second phase, a deterministic multi-objective mixed-integer mathematical model has been designed considering the needs of the company. This model not only concentrates on the benefits, but also considers the preferences of the products. The third objective function is decreasing work in process. To solve this model, ϵ-constraint method has been used leading to a set of Pareto-optimal solutions, enabling the decision-maker to choose the best solution by trading off between the three objective functions. So top managers are able to decide how to provide product preferences and how to decrease WIP products while the benefits remain reasonable. The results show that using the proposed approach in the case study has improved 35%, 28%, and 56% total benefit, total utility, and WIP products, respectively.

Realization of plans and on time demands providing, is nowadays one of the most strategic goals in all production industries. This importance is caused by importance of customer position and also exploitation of resources. In this paper, the aggregate production planning problem is studied. Two objective function is considered for this problem. First, minimizing total cost elements and second, minimizing dissatisfaction of customer that comes about delay in demands delivery. Due to the importance of the customer place in nowadays competitive environment, the last objective function is added to problem to getting closer to the real world. Therefore, at first problem definition via objective functions, parameters, and decision variables are presented. Then mathematical model in multi objective is developed. Since, this problem has been proved as NP-Hard, an approximation method is also developed based on Non-dominated Sorting Genetic Algorithm II (NSGA-II). Finally this problem is solved applying two algorithm ε_Constraint and NSGA-II using standard data that is obtained from references. The result show good performance of the proposed method based on NSGA-II both in solution quality and running time.

In this paper, multi-site aggregate production planning for the production of perishable products such as gifts of New Year, calendars and maturities is determined by postponement policy in uncertainty conditions. The production process for these products is proposed to be divided into two phases including the production of final and semi-finished products with applying the concept of postponement. So, there are three production activities including direct production, the production of semi-finished products and final assembly. Also, a robust optimization model to solve aggregate production planning problem for these products is developed. Finally, a set of real data from a calendar producing company in Tehran called “NIK Calendar” is used to validate and show the efficiency of the proposed model. Results show that the proposed model of this paper can be used for similar factories which are active in the field of aggregate production planning with considering uncertainty in the parameters.

Nowadays, the organizations’ success depends on planning. The purpose of medium term production planning is optimum usage of human resource, equipment, and capacity to meet demand during a given period. Always, in the organizations, there are multiple and sometimes conflicting goals in planning. On the other hand, production planning models should be designed so that to be able to deal with uncertainty and lack of information. Grey goal programming technique is the way that not only consider multiple and conflicting goals but also it is able to deal with uncertainty and lack of information. In this research, in order to develop the L.M.I Company’s medium - term production planning is used grey goal programming model.