Operational scheduling with business modelling and genetic algorithms

[img]
Preview
PETRUKOV_Boris-thesis.pdf - Submitted Version (3MB) | Preview

Petukhov, Boris (2020) Operational scheduling with business modelling and genetic algorithms. PhD thesis, Victoria University.

Abstract

Development and maintenance of effective schedules is paramount to the overall success of project management. Scheduling in complex and large problem domains is resource consuming and challenging, and becomes especially difficult when project conditions often change within relatively short periods. This research contributes to knowledge in the program management area by putting forward a new approach that entails automation and optimisation of operational scheduling to enable organisations to run their workstreams in a controlled and predictable fashion to achieve the desired outcomes within expected timeframes and resource constraints. The approach put forward in this research combines theoretical knowledge, technology-based scheduling implementation and genetic algorithm optimisations in a single framework to generate optimised schedules. The approach entailed the development of a new planning and scheduling method based on business modelling and genetic algorithms. This new method, called Operational Scheduling with Business Modelling and Genetic Algorithms has been recognised with the award of an Australian Standard Patent, and offers an integrated operational scheduling approach that allows its users to follow a clear path and address their day-to-day problems at the level of complexity required. This method allows for artificial intelligence implementations based on genetic algorithms, which develop the initially proposed scheduling solutions to the optimal schedules that could be generated for given problem scenarios. The method starts from essential planning and scheduling where relatively simple scheduling is performed and then moves into domain- specific scheduling, which requires unrestricted, customised and complex implementations. In doing so, it constructs business models of the problem domain, identifies hard and soft constraints, implements automatic scheduling procedures to generate initial schedule samples, and performs genetic algorithms’ crossover, mutation, fitness valuation to produce optimal scheduling solutions. The method was applied in a number of case studies where it was found the optimisation delivered efficiency gains of between 8 per cent and 20 per cent of the total operational costs, which some cases resulted in significant monetary savings.

Item type Thesis (PhD thesis)
URI https://vuir.vu.edu.au/id/eprint/42038
Subjects Historical > FOR Classification > 0801 Artificial Intelligence and Image Processing
Historical > FOR Classification > 1503 Business and Management
Current > Division/Research > Institute for Sustainable Industries and Liveable Cities
Keywords operation management; artificial intelligence; genetic algorithms; optimisation
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login