Chili sauce production planning model considering raw material availability: An application of Mixed Integer Linear Programming Method

Conference on Innovation in Technology and Engineering Science

工业技术（总论）

Jonrinaldi^1 ; Adi, A.H.B.^1 ; Novira, R.^1

Department of Industrial Engineering, Faculty of Engineering, Universitas Andalas, Padang, West Sumatra

25163, Indonesia^1

关键词: Harvest season;    Input parameter;    Material availability;    Mixed integer linear programming;    Optimal solutions;    Production capacity;    Production Planning;    Service levels;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/602/1/012046/pdf
DOI  :  10.1088/1757-899X/602/1/012046

学科分类：工业工程学

来源: IOP

【 摘 要 】

PT Bina Usaha Keluarga Sedep Roso is one of agroindustry located in Padang producing chili and soy sauces. Chili and soy sauces main ingredients are sweet potato and black soybean, respectively. Both chili and soy sauces have a broad market in West Sumatera, Indonesia. The problem often faced by companies is that the demand for chili sauce product cannot be fulfilled because of seasonal and perishable factors of the sweet potato. It became scarce and expensive outside the harvest season otherwise the excessive supply and low price in the harvest season. This paper proposes an application of mixed integer linear programming to manage production planning of chili sauce to minimize total operational cost and increase the service level considering raw material availability and production capacity of chili sauce companies. The mixed integer linear programming method is applied because there are some constraints on the problem. The mathematical model is developed to obtain the optimal solution in allocating the available resources with given constraints regarding raw material availability and production capacity. The numerical example is given to illustrate the model and show results. Sensitivity analysis is performed to see the changes in the optimal solution caused by the changes in the input parameters of the model.