Browsing by Author "Nigrini, Leanne"

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    Investigating hyperheuristics for solving bi-objective simulation optimisation problems
    (Stellenbosch : Stellenbosch University, 2023-02-10) Nigrini, Leanne; Bekker, JF; Nel, GS; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
    ENGLISH ABSTRACT: The investigation and exploration of search and optimisation methodologies are crucial research areas. Take for example the potential impact of an effective and computationally efficient decision support methodology, it could be the difference between life and death in healthcare scheduling. Schedule too few doctors and patients could die; schedule long work-hours and doctors could make fatal mistakes due to fatigue. Simulation optimisation is typically used to approximately solve large and complex problems that cannot be solved by exact methods. In addition, the need for better simulation optimisation approaches are further motivated by the combinatorial relationship that results in significant search spaces. One of the biggest problems that researchers face with metaheuristic approaches is the lack of general applicability and the high number of hyperparameter combinations that algorithms have and a lack of insight on how to choose them. This is due to the fact that the performance of metaheuristics greatly depends on the type of problem being optimised, as supported by the no free lunch (NFL) theorem. Accordingly, each optimisation algorithm has its strengths and weaknesses when it comes to exploring the search space. Hyperheuristics propose to compensate, to some extent, for the weaknesses of the individual low-level heuristics (LLHs) by method of algorithmic cooperation, creating ensemble algorithms that are more generally applicable, i.e. can solve a larger range of problems than the individual LLHs are capable of solving. In this study, two hyperheuristic approaches are developed, one for population-based search and the second for single-solution based search, and are assessed using five discete-event dynamic stochastic bi-objective simulation optimisation problems. The hyperheuristics as well as their individual LLHs are implemented and assessed in Tecnomatix Plant Simulation. In addition, an algorithmic parameter study is presented for the respective LLHs to determine good hyperparameter combinations and possibly infer insights from the complex interaction. Furthermore, due to the dynamic and stochastic nature of simulation models, there exists a sufficient number of observations per solution that need to be evaluated to be able to construct suitable narrow confidence intervals. This renders simulation optimisation computationally expensive and for that reason a pilot study is conducted to determine the feasibility of an ANN as metamodel to screen out solutions that are predicted to be of low-quality, thereby reducing the number of computationally expensive evaluations that need to be made by the simulation model. The statement that hyperheuristics perform better (or at least similar) to its individual LLHs does not hold true for the population-based hyperheuristic. The statement, however, holds true for the single-solution based hyperheuristic. It can be concluded that both hyperheuristics failed to exhibit superior performance and did not indicate favourable performance improvements relative to all the individual applications of the LLHs. Furthermore, the novel pilot study provided valuable insights pertaining to the complex interaction within an ANN, however, the study could not conclude whether or not an ANN metamodel is a feasible solution to enhance the simulation optimisation process. The study does provide valuable insights which could inspire further research.