Developing a Hybrid Optimisation Methodology for Sustainable Facility Layout Design in Resource-Variable Manufacturing Environments

This dissertation develops an original hybrid optimisation methodology for sustainable facility layout design applicable to manufacturing environments characterised by resource variability, a condition prevalent in Nigerian manufacturing where energy supply, water availability, and labour attendance fluctuate substantially from planned levels. Standard facility layout optimisation models in the industrial engineering literature optimise fixed objectives, primarily material handling cost and flow distance, under deterministic resource assumptions that systematically misrepresent the operating conditions facing Nigerian manufacturers, producing layout solutions whose real-world performance degrades severely under actual resource variability conditions. The hybrid methodology integrates three original components: a stochastic facility layout problem formulation incorporating probabilistic resource availability parameters derived from empirical measurement data, a multi-objective evolutionary algorithm combining non-dominated sorting genetic algorithm II with a novel variable neighbourhood local search operator, and a sustainability impact assessment module quantifying environmental and social performance implications of layout alternatives alongside economic cost objectives. The methodology is developed and computationally validated on benchmark facility layout problem instances before application to layout redesign problems at three manufacturing facilities in Ogun, Anambra, and Sokoto states. Computational results demonstrate the superiority of stochastic over deterministic formulations under Nigerian operating conditions, with solution quality degradation under resource variability scenarios reduced by forty-four percent compared to deterministic optimisation benchmarks. Keywords: facility layout optimisation, stochastic programming, multi-objective evolutionary algorithm, sustainable manufacturing, Nigeria