Geoff Kent

Hybrid metaheuristic techniques for optimising sugarcane rail operations

Mixed integer programming and parallel-machine job shop scheduling are used to solve the sugarcane rail transport scheduling problem. Constructive heuristics and metaheuristics were developed to produce a more efficient scheduling system and so reduce operating costs. The solutions were tested on small and large size problems. High-quality solutions and improved CPU time are the result of developing new hybrid techniques which consist of different ways of integrating simulated annealing and Tabu search techniques.

The Effect of Added Water Temperature on Milling Train Operation and Performance

Four recent factory experiments have been conducted to provide information to determine an optimal added water temperature. The results were not completely conclusive but did provide some further insight into the effect of added water temperature. The effect of added water temperature on extraction has proven particularly difficult to measure. There is little doubt that bagasse is more compressible at higher temperature. Two experiments have shown that bagasse moisture content is lower at higher temperature. These effects are most likely related. Two experiments produced indirect evidence of an increase in extraction with higher added water temperature. With bagasse being more compressible, mill capacity is increased at higher temperature. The use of a heat exchanger to cool added water with mixed juice reduces heat loss and consequently reduces steam usage for primary heating of juice. Some of the lost heat may serve a useful purpose in increasing boiler efficiency, resulting in more efficient use of bagasse. Hotter added water does not appear to be a complete solution for mill hygiene since even the hottest added water investigated only provides sufficient benefit for the last one or two mills. There remains considerably more work to identify the optimal added water temperature. The main outstanding issues are the effect of added water temperature on extraction, the effect of hotter bagasse on boiler efficiency through moisture content or temperature and the impact of bagasse temperature on hygiene.

An integrated approach to optimise sugarcane rail operations

An innovative mathematical model is presented to optimise a cane transport system.An integrated depth-first-search algorithm with constraint programming is presented.A hybrid metaheuristic technique is developed for finding more accurate solutions with less CPU time. In Australia, the railway system plays a vital role in transporting the sugarcane crop from farms to mills. The sugarcane transport system is complex as it routines a daily schedule, which consists of a set of train runs to satisfy the requirements of the mills and harvesters. A constrain programming approach is used to formulate this complicated system. Metaheuristic techniques and constraint programming are hybridised as an efficient solution approach. Thus, a better sugarcane transport scheduling system is achieved to maximise the throughput of sugarcane transport. A numerical investigation is presented and demonstrates that high-quality solutions are obtainable for industry-scale applications in a reasonable time.

A new constraint programming approach for optimising a coal rail system

Because of the bottlenecking operations in a complex coal rail system, millions of dollars are costed by mining companies. To handle this issue, this paper investigates a real-world coal rail system and aims to optimise the coal railing operations under constraints of limited resources (e.g., limited number of locomotives and wagons). In the literature, most studies considered the train scheduling problem on a single-track railway network to be strongly NP-hard and thus developed metaheuristics as the main solution methods. In this paper, a new mathematical programming model is formulated and coded by optimization programming language based on a constraint programming (CP) approach. A new depth-first-search technique is developed and embedded inside the CP model to obtain the optimised coal railing timetable efficiently. Computational experiments demonstrate that high-quality solutions are obtainable in industry-scale applications. To provide insightful decisions, sensitivity analysis is conducted in terms of different scenarios and specific criteria.

Experimental dataset for optimising the freight rail operations

The freight rail systems have an essential role to play in transporting the commodities between the delivery and collection points at different locations such as farms, factories and mills. The fright transport system uses a daily schedule of train runs to meet the needs of both the harvesters and the mills (An Integrated Approach to Optimise Cane Rail Operations (M. Masoud, E. Kozan, G. Kent, Liu, Shi Qiang, 2016b) [1]). Producing an efficient daily schedule to optimise the rail operations requires integration of the main elements of harvesting, transporting and milling in the value chain of the Australian agriculture industry. The data utilised in this research involve four main tables: sidings, harvesters, sectional rail network and trains. The utilised data were collected from Australian sugar mills as a real application. Operations Research techniques such as metaheuristic and constraint programming are used to produce the optimised solutions in an analytical way.