Aggregate production planning considering performance evolution : a case study (original) (raw)
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INTERNATIONAL SYMPOSIUM FOR PRODUCTION RESEARCH 2017 (ISPR 2017), 2017
Linear programming is a mathematical programming technique that provides the most convenient choice or the distribution among the various alternatives and the most efficient usage of limited resources to achieve a specific goal. Production planning is the process of decision-making to be able to produce in the required quality products by using existing resources rationally. In this study, emphasizing the importance of production and capacity planning to companies, the linear programming technique, one of the most used techniques to solve the problems in these areas is examined and the application of this technique is practiced in an industrial enterprise. In this study, using the demand forecast in line with the company's data, linear programming model of annual production plan of products is formulated to maximize profit. Lindo software has been used to solve and analyze the model. Finally, some suggestions are examined according to results.
Optimization of Production Planning Using Linear Programming
The production process carried out by an industry aims to meet customer demands accurately and quickly through a production process. If the production results are not following customer demand, whether it is a shortage or excess product, it can result in increased costs incurred by the industry. Therefore, it is necessary to carry out a comprehensive production planning (Aggregate Production Planning) by considering the number of customer requests and the resources or capacity owned by the industry. The purpose of this research is to find a comprehensive optimization system model of the production planning, to optimize the production costs incurred and the level of profit. The methods used are the forecasting method, the Aggregate Production Planning method, and linear programming. The variables and parameters used are the appropriate production factors to obtain an optimization model for aggregate production planning. The result of this research is an optimization model of aggregate production planning using linear programming, which is obtained through the integration of linear programming model and aggregate production planning model, with decision variables and parameters covering various production factors to attain a minimum total cost.
Aggregate Production Planning (APP) is a medium-term planning which is concerned with the lowest-cost method of production planning to meet customers' requirements and to satisfy fluctuating demand over a planning time horizon. APP problem has been studied widely since it was introduced and formulated in 1950s. However, in several conducted studies in the APP area, most of the researchers have concentrated on some common objectives such as minimization of cost, fluctuation in the number of workers, and inventory level. Specifically, maintaining quality at the desirable level as an objective while minimizing cost has not been considered in previous studies. In this study, an attempt has been made to develop a multi-objective mixed integer linear programming model that serves those companies aiming to incur the minimum level of operational cost while maintaining quality at an acceptable level. In order to obtain the solution to the multi-objective model, the Fuzzy Goal Programming approach and max-min operator of Bellman-Zadeh were applied to the model. At the final step, IBM ILOG CPLEX Optimization Studio software was used to obtain the experimental results based on the data collected from an automotive parts manufacturing company. The results show that incorporating quality in the model imposes some costs, however a trade-off should be done between the cost resulting from producing products with higher quality and the cost that the firm may incur due to customer dissatisfaction and sale losses.
Development of a Dynamic Programming Model for Optimizing Production Planning
ABSTRACT:- Production planning is the backbone of any manufacturing operation, and its main objective is to determine the quantity of products to be produced and inventory level to be carried from one period to the other, with the objective of minimizing the total costs of production and the annual inventory, while at the same time meeting the customers’ demand. A mathematical model was developed for a multi-product problem using Dynamic Programming approach and the solution procedure proposed by Wagner and Whitin was adopted. The model is very useful in solving a problem with multi-stage problem, a particular situation in which there is appreciable variation in average periodic demand and availability of raw materials among the different periods. It also stipulates the minimum quantities of the product to produce per period and the corresponding inventory levels such that total production cost is minimized over the planning periods. Keywords: Cost, Dynamic, Inventory, Minimum, Model, Production.
One of the main problems of the firms is the inefficiency of the production; hence, one of the primary objectives of them is to improve the production by the increased utilization of the scarce recourses the most economically in accordance with the goals of the firms. Therefore, a variety of operational research tools have been evolved in the literature. Among those techniques, linear programming is of great importance with its power of tractability as well as the sensitivity analysis it enables the planner to investigate the robustness of the solution it generates. At present, firms have been lost their freedoms of changing the prices to a large extent because of the increasing competition, hence, they choose the way of minimizing the costs to be able to improve their profitability. As a result, the importance of the linear programming which is the effective production planning method has been growing day by day. This study models and demonstrates the benefits of the mixed integer linear programming in aggregate production planning for a lubricant factory.
AGGREGATE PRODUCTION PLANNING: MIXED STRATEGY
This article is a mathematical model to make decisions in the aggregate production planning of a pump manufacturing company. The mathematical formulation proposed is based on process selection and lot-sizing models. The aim is to help the planners in selecting the industrial processes used to produce pumps and the inventory strategy. The planning period is one year and decisions are taken on a discrete time. A case study was developed in a pump manufacturing company. Under mixed strategy, both inventory and workforce levels are allowed to change during the planning horizon. Thus, it is a combination of the " chase " and " level " strategies. This will be a good strategy if the costs of maintaining inventory and changing workforce level are relatively high. Optimization models are generally used to determine an optimum mixed strategy. In this paper, we use Python program to optimize the problem. Index terms: aggregate production planning, mixed integer programming, mixed strategy, python.
A linear optimization approach to the combined production planning model
Journal of the Franklin Institute, 2011
Two fundamental processes usually arise in the production planning of many industries. The first one consists of deciding how many final products of each type have to be produced in each period of a planning horizon, the well-known lot sizing problem. The other process consists of cutting raw materials in stock in order to produce smaller parts used in the assembly of final products, the wellstudied cutting stock problem. In this paper the decision variables of these two problems are dependent of each other in order to obtain a global optimum solution. Setups that are typically present in lot sizing problems are relaxed together with integer frequencies of cutting patterns in the cutting problem. Therefore, a large scale linear optimizations problem arises, which is exactly solved by a column generated technique. It is worth noting that this new combined problem still takes the trade-off between storage costs (for final products and the parts) and trim losses (in the cutting process). We present some sets of computational tests, analyzed over three different scenarios. These www.elsevier.com/locate/jfranklin .br (M.C. Gramani), paulo.morelato@fct.unesp.br (P.M. Franca), arenales@icmc.usp.br (M.N. Arenales). 1 Tel.: þ55 18 3229 5385; fax: þ55 18 3229 5353. 2 Tel.: þ55 16 3373 9655; fax: þ55 16 3373 9751.
2007
In this paper, a multi-objective model for aggregate production planning is presented which includes two objectives: (1) minimized cost and (2) minimized effect on the workforce motivation level caused by hire/layoff decisions. Then, six strategies are considered and the most appropriate one is determined to structure the plan. These strategies are set the regular time production quantities in a certain value which is unique for each. A preference based optimization method called Linear Physical Programming (LPP) is used to solve the model. A forecasting phase which chooses the convenient method to forecast the demand for planning horizon is embedded to study in addition to application of LPP to an APP model as another key contribution of this paper. Significance: This paper presents the application of a relatively new method –Linear Physical Programming- to the Aggregate Production Planning strategy selection process. This method provides a flexibility to decision makers in terms o...
Extended model for a hybrid production planning approach
International Journal of Production Economics, 2001
The traditional production planning model based upon the famous linear programming formulation has been well known in the literature. However, the capacity constraints in such a model may not correctly represent the actual situations of the shop #oor, as pointed out by Byrne and Bakir (International Journal of Production Economics 59 (1999) 305}311) . A hybrid approach was proposed by them, applying simulation and a linear programming model iteratively, to "nd the capacity-feasible production plan. This paper proposes an extended linear programming model for a similar hybrid approach. At each simulation run, the actual workload of the jobs and the utilization of the resources are identi"ed. The information is then passed to the linear programming model for calculating the optimal production plan with minimum total costs. Through the case study, it is shown that the proposed approach "nds the better solution in a less number of iterations compared to the approach by Byrne and Bakir .