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Due to the COVID-19 pandemic, many universities across the globe are unexpectedly accelerated to face another major financial crisis. An effective university course timetabling has a direct effect on the utilisation of the university resources and its operating costs. The university course timetabling is classified to be a Non-deterministic Polynomial (NP)-hard problem. Constructing the optimal timetables without an intelligence timetabling tool is extremely difficult task and very time-consuming. A Hybrid Particle Swarm Optimisation-based Timetabling (HPSOT) tool has been developed for optimising the academic operating costs. In the present study, two variants of Particle Swarm Optimisation (PSO) including Standard PSO (SPSO) and Maurice Clerc PSO (MCPSO) were embedded in the HPSOT program. Five combinations of Insertion Operator (IO) and Exchange Operator (EO) were also proposed and integrated within the HPSOT program aimed at improving the performance of the proposed PSO variants. The statistical design and analysis indicated that five combination results of IO and EO for hybrid SPSO and MCPSO were significantly better than those obtained from the original PSO variants for all eleven problem instances. The average computational times taken by the proposed hybrid methods were also faster than the conventional SPSO and MCPSO for all cases.

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An effective layout can reduce material flow distances and manufacturing lead-times, whilst increasing productivity, throughput and cost effectiveness. The facilities layout problem (FLP) is a non-deterministic polynomialtime hard problem, which means that the computational time taken to produce solutions increases exponentially with problem size. Metaheuristics are particularly suitable for solving such problems in reasonable time. Biogeographybased optimisation (BBO) is a well-known nature-inspired computing metaheuristic. Its mechanisms mimic an analogy with biogeography which relates to the migration, mutation and geographical distribution of biological organisms. This paper presents a novel BBO optimisation tool that solves the unequal area facilities layout problem to generate multirow solutions that minimise the total material flow distance. Two novel modifications were made to the conventional BBO: the use of a Genetic Algorithm crossover operator in the migration process; and a changed method for selecting candidate solutions. The local search approaches used data on flow intensities and machine adjacencies. Experiments were conducted using five benchmark datasets obtained from the literature. The statistical analysis of the computational results indicated that the proposed mBBOs produced statistically better solutions than the conventional BBO and other metaheuristics for all datasets and converged more rapidly with comparable execution times.