Scheduling Optimization of Sugarcane Harvest Using Simulated Annealing Algorithm

Abstract

Scheduling is one of the important part in production planning process. One of the factor that influence the smooth production process is raw material supply. Sugarcane supply as the main raw material in the making of sugar is the most important componen. The algorithm that used in this study was Simulated Annealing (SA) algorithm. SA apability to accept the bad or no better solution within certain time distinguist it from another local search algorithm. Aim of this study was to implement the SA algorithm in scheduling the sugarcane harvest process so that the amount of sugarcane harvest not so differ from mill capacity of the factory. Data used in this study were 60 data from sugarcane farms that ready to cut and mill capacity 1660 tons. Sugarcane harvest process in 19 days producing 33043,76 tons used SA algorithm and 27089,47 tons from factory actual result. Based on few experiments, obtained sugarcane harvest average by SA algorithm was 1651,63 tons per day and factory actual result was 1354,47 tons. Result of harvest scheduling used SA algorithm showed not so differ average from mill capacity of factory. Truck uses scheduling by SA algorithm showed average 119 trucks per day while from factory actual result was 156 trucks. With the same harvest time, SA algorithm result was greater and the amount of used truck less than actual result of factory. Thus, can be concluded SA algorithm can make the scheduling of sugarcane harvest become more optimall compared to other methods applied by the factory nowdays.

Penyusunan Jadwal Asisten Praktikum Menggunakan Algoritma Genetika

“…Data yang digunakan meliputi jumlah hari untuk panen dan jarak kebun tebu yang digunakan untuk menentukan banyaknya truk untuk proses pengangkutan tebu ke pabrik. Solusi jadwal terbaik yang dipilih adalah jadwal yang mempunyai perbedaan jumlah tebu terkecil dan panen tebu tidak lebih dari 1900 ton [6]. Sebagai salah satu algoritma meta-heuristik, algoritma genetika merupakan algoritma yang cukup kuat dan mampu menyelesaikan berbagai permasalahan multi-objektif [7].…”

Optimizing Cost of Sugarcane Logging and Transportation to Milling Using Iterative Fuzzy Inference System

“…Florentino, Irawan, Aliano, Jones, Cantane, and Nervis used genetic algorithms and goal programming to schedule sugarcane harvests as close to sugarcane maturity [1]. In addition, Afifah, Alamsyah, and Sugiharti also performed scheduling optimization by minimizing sugarcane transport trucks from the land to the mill using the simulated annealing algorithm [9]. Caixeta-Filho and Miyashita optimized the sugarcane harvest scheduling with processing capacity limits and the minimum proportion of land harvested using mixed-integer linear programming [10].…”