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An Integrated Sugarcane Phenology and an Optimization Approach to Set Plant and Harvest Schedules Within a Mill Region

An Integrated Sugarcane Phenology and an Optimization Approach to Set Plant and Harvest Schedules Within a Mill Region
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Author(s): Kullapapruk Piewthongngam (Khon Kaen University, Thailand), Kanchana Setthanan (Khon Kaen University, Thailand) and Jakrapun Suksawat (Khon Kaen University, Thailand)
Copyright: 2007
Pages: 4
Source title: Managing Worldwide Operations and Communications with Information Technology
Source Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-59904-929-8.ch056
ISBN13: 9781599049298
EISBN13: 9781466665378

Abstract

As a result of separated planning between rain-fed sugarcane growers and sugar mills in Northeastern of Thailand, the supply of sugarcane to the mills are fluctuated. This situation leads both sugarcane farmers and sugar plants to produce below their profit maximization level. During harvest season, quantity of sugar being harvested is not matched with capacity level of the mills. Hence, supply of sugar is under capacity in early and late harvest season and is over capacity in the middle of season. This problem portrays a loss to the mills when operate under capacity and a loss to farmers when cut-to-crush time exceeds 15 hours as a result of over supply of sugar on that particular day. This problem can be resolved through supply chain management using database management, sugar phenology modeling such as DSSAT-CANEGRO and heuristic method. The database is used to record land information, climate zone, soil structure, size of land, and farm management. This information, then, is fed into CANEGRO, which combined weather data, genetic characters, management strategies, and soil data in order to simulate expected outputs. The results of these simulations are used in heuristic programming to identify variety, time, and harvesting date for a particular sugar plot such that capacity of a sugar processing plant is optimized as well as farmer’s revenue. The simulated maximum quantities of sugar can be produced is 810 million kilograms which is obtained from starting harvesting season on January 6 and ending on November 9. The simulated result using purposed algorithm is greater than an average of simulated randomly grown sugarcane by 243 millions kilograms. Moreover the simulated randomly grown results depicts that supply of sugar exceeds capacity by 4 times and under capacity by 20 times on the average.

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