Production optimization in sausage industry based on the demand of the products

Abstract

The sausage production industry has recently had an increase in consumption. This led to the search for the most efficient use of available resources in order to achieve increased productivity, reduced costs, reliability in service, and reduce delivery times. In the sausage industry a wide variety of products is obtained, most of these products do not has an exclusive line, and therefore there is a competition for equipment and intermediate states. The manufactured products are usually perishable and it is recommended that production should not exceed the demand under the disadvantage of stocks generating and exceed the shelf life of the product. The objective of this work was the adaptation and use of a mathematical model of production scheduling to optimize the production of a sausage industry, from demand values for each product. The proposed approach treats the problem as mixed integer linear programming to represent the decisions involved and was solved with the GAMS software (Generic Algebraic Modeling System). The developed model was applied to the problem of production scheduling with actual operational data from a regional industry. The results of the application of the model with data collected in the industry were an increase of the total production of the plant in the range of 20%. and has shown its capacity to generate better results than the production results presented by industry. Therefore, the result of the model presented here can assist in making the production scheduling decisions in the sausage industry. Practical applications The objective of the work was to maximize the production of the products taking into account the demand of each of them avoiding the formation of stocks due to the useful life of the products. For this, we use a mathematic model adapted to the sausage industry that operates in batch. The production scheduling was done in a single campaign mode for weekly production in the industry and short‐term production planning was done to produce the stocks of intermediate products needed at the beginning of the campaign. The results presented in the Gantt tables can be used on the factory floor. The results of the application of the model with data collected in the industry were an increase of the total production of the plant in the range of 20%.

Model to reduce the cost of production in a bottling company using the EOQ, Linear Programming and Aggregate Planning

“…In addition, it is possible to analyze whether the demand can be satisfied or not and, if there is excess capacity, what resources can be re-allocated [13]. In cases where demand or price is notably changing, linear programming is also a tool that allows decisions to be made based on changes in model parameters [14]. In the literature it is common to find case studies in which linear programming is performed to develop aggregate planning.…”

“…In the literature it is common to find case studies in which linear programming is performed to develop aggregate planning. An aggregate planning model can be obtained from demand predictions [14]. As well, it can also be obtained based on linear programming optimization model results [15] [16].…”

“…On the other hand, it is necessary to know if the use of this tool is compatible with the model. Therefore, in the research presented by [14], the simultaneous use of aggregate planning together with linear programming is evident. The use of linear programming allowed obtaining more precise data in this case.…”

Optimization-Based Scheduling for the Process Industries: From Theory to Real-Life Industrial Applications

“…Solutions with approximately 2% integrality gap can be generated in 20 CPU s without allowing the backlog generation and 1075 CPU s by allowing the possibility of backlogs. Polon et al [100] studied a sausage production industry aiming to the profit maximization by solving an MILP scheduling model for batch processes. The packaging stage, which often constitutes the main production bottleneck has not been considered.…”

Optimal Production Scheduling in the Dairy Industries

“…In particular, multiple batch and/or continuous stages are required to prepare the foodstuff, which is finally packed in a form appropriate for the needs of the market. Polon 4 studied the scheduling of a sausage industry without considering the packaging stage, which often constitutes the main production bottleneck. A novel MILP was proposed by Baldo 5 for a brewery industry, and heuristics were integrated for the investigation of real-life study cases.…”