# An Optimal Cane Delivery Scheduling Approach

## An Optimal Cane Delivery Scheduling Approach

### Authors

Surya Prakash, University of the South Pacific, Anjeela Jokhan, University of the South Pacific

### Description

This paper investigates the scheduling of cane delivery problem and presents a Monte Carlo scheduler that minimizes mill idle time as a first criterion with lorry queue time as the next.

### 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.
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. Research was undertaken to address this issue 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 use of random sampling to acquire a solution to a given problem. This approach is often used when traditional heuristics methods fail usually because it is sometimes hard to derive admissible evaluation functions to determine which of the candidate successors is to be selected next. Monte Carlo techniques are nowadays widely used for many applications including all types of optimization algorithms.
In the sugarcane industry an optimal cane delivery schedule would minimize the mill idle time and at the same time minimize the queue/wait time at the mill. Since cost arising from idle mill time is significantly more compared to the cost associated with the queue time of the lorries, priority is given to minimizing the idle mill time.
This paper investigates the scheduling of cane delivery problem and presents a Monte Carlo scheduler that minimizes mill idle time as a first criterion with 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 presented.

#### Publisher

Association for European Transport