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An optimal cane delivery scheduling using the Monte Carlo method

Prakash, Surya and Jokhan, Anjeela D. (2016) An optimal cane delivery scheduling using the Monte Carlo method. [Conference Proceedings]

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    Abstract

    Transporting sugarcane from farms to sugar mills plays a vital role in most raw sugar producing industries including those in Australia, Brazil, Thailand and Fiji. In Fiji, this issue needs to be urgently investigated and made cost effective due Fiji Sugar Corporation’s (FSC’s) plan to take over the responsibility of the delivery of sugar cane from farms to mills. The transportation of sugarcane is a complicated process and includes many variables and criteria that have to be fulfilled hence making it difficult for mill traffic planners/officers to produce optimal schedules manually. A non-optimal schedule of cane delivery leads to increased costs associated with either idle mill time or wait/queue time of the lorries or both. This issue is addressed in this paper and the Monte Carlo approach has been used to develop a scheduler for the cane delivery to the mills via cane lorries. The Monte Carlo approach relies on the use of random sampling to acquire a solution to a given problem. This approach is often used when traditional heuristics methods fail. Monte Carlo techniques are nowadays widely used for many applications including all types of optimization algorithms. This paper investigates the scheduling of cane delivery problem and presents a Monte Carlo scheduler that minimizes mill idle time as a first criterion and lorry queue time as the next, while incorporating different travel times to the mills from various locations, processing time of mills and time taken to cut cane and load lorries at the farms. Examples and scenarios are provided where the Monte Carlo scheduler is utilized and the generated optimal schedule is presented for real data related to Fiji Sugar Industry.

    Item Type: Conference Proceedings
    Subjects: H Social Sciences > HE Transportation and Communications
    Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
    Faculty of Science, Technology and Environment (FSTE)
    Depositing User: Surya Prakash
    Date Deposited: 26 Jan 2017 16:14
    Last Modified: 23 Jun 2017 09:55
    URI: http://repository.usp.ac.fj/id/eprint/9024
    UNSPECIFIED

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