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Digital Library

of the European Council for Modelling and Simulation

 

Title:

Aggregate Planning Using Mixed Integer Programing: A Fruit Juice Concentrated Factory Case Study

Authors:

Issara Ruangngam, Thananya Wasusri

Published in:

 

 

(2019). ECMS 2019 Proceedings Edited by: Mauro Iacono, Francesco Palmieri, Marco Gribaudo, Massimo Ficco, European Council for Modeling and Simulation.

 

DOI: http://doi.org/10.7148/2019

 

ISSN: 2522-2422 (ONLINE)

ISSN: 2522-2414 (PRINT)

ISSN: 2522-2430 (CD-ROM)

 

33rd International ECMS Conference on Modelling and Simulation, Caserta, Italy, June 11th – June 14th, 2019

 

 

Citation format:

Issara Ruangngam, Thananya Wasusri (2019). Aggregate Planning Using Mixed Integer Programing: A Fruit Juice Concentrated Factory Case Study, ECMS 2019 Proceedings Edited by: Mauro Iacono, Francesco Palmieri, Marco Gribaudo, Massimo Ficco European Council for Modeling and Simulation. doi: 10.7148/2019-0249

DOI:

https://doi.org/10.7148/2019-0249

Abstract:

The case study is a newly opened fruit juice concentrated factory. Three different products have been launched to serve both domestic and international markets. For its inbound logistics, it receives fresh fruits from local growers. As the fresh fruits are seasonal, its prices and quantities are higly fluctuated. A cold storage is then used to freeze and store the fruits during their peak periods in order to reduce risks of fruit shortages and high prices in off peak periods. Storaging costs of fresh fruits are, however, substantial. To produce the finished products, Clean-in-Place (CIP) is being managed before changing the product lines and it takes 3 days for each cleaning or setup. After the production process, the finished products are stocked in its warehouse no more than 2 months as its clients require the products to be 80% viable of its shelf life. Aggregate planning is then needed to match demand with supply of the case study while minimising total operating costs including purchasing cost, raw material inventory cost, semi-product cost, finished inventory cost, setup cost and labour cost. Mixed integer programing is used to find the optimal decision variables that are purchasing volumes, semi-product volumes, production volumes, and setup decisions.

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