Optimization of Total Production of Refined Sugar From Raw Sugar Raw Materials and Supporting Raw Materials Using the Generate-And-Test Method at PT. DSI Banten - Sigit Rahayu; Andri Budi Kusumah; Supriyadi Supriyadi; Wahyu Oktri Widyarto (Journal of Machine Learning and Soft Computing, 2019)
The search problem is a problem that is commonly applied to systems based on the concept of Artificial Intelligence. One of the well-known heuristic search methods in Artificial Intelligence terminology is Generate and Test. In general, there are no companies operating without raw materials, raw materials in PT. DSI is a type of main and supporting raw material. Refined sugar production at PT. DSI Banten has been experiencing fluctuations in the output of production every day, the data in April 2014 showed from 1-7 consecutively that is 726, 578, 592, 518, 692, 734, 473 tons (PT. DSI, April 2014 ). The purpose of this study is to implement the heuristic search concept with the Generate and Test Algorithm in the search for a combination of the two raw materials to obtain the highest amount of production / output in the form of refined sugar, from the results of this study obtained a system that is able to find the highest amount of sugar production per cuisine, namely in the form of types of supporting raw materials (Limestone CaO, HCL, NaOH) and types of main raw materials (Raw sugar). After conducting research through the heuristic search concept with the GnT method, from 3 types of supporting raw materials (type 1: supplier from PT. SAP, type 2: supplier from PT. MNA, type 3: supplier from PT. CKT) and 3 types of raw material main (raw sugar 1: import from Australia, raw sugar type 2: import from Vietnam, raw sugar type 3: import from Thailand) found an optimization of the two raw materials with the results of supporting material type "3" and main raw material type " 2 "with the amount of 123 tons per cuisine for refined sugar output, the results obtained are able to increase productivity in the refined sugar processing.