A Construction Project Scheduling Approach for Petroleum Refinery Turnaround Maintenance Using Hybrid GA–LOB Optimization
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This study develops a hybrid Genetic Algorithm–Line of Balance (GA–LOB) scheduling framework for turnaround maintenance (TAM) in petroleum refineries, addressing the NP-hard challenge of optimizing task sequencing under realistic contractor availability and mandatory work/rest cycles. The TAM problem is formulated as an unrelated parallel machine scheduling problem with sequence-dependent setup times and SIMOP (simultaneous operations) safety constraints. A three-component chromosome encodes unit processing sequence, contractor allocation, and execution mode, enabling the GA to explore a large combinatorial solution space. At the same time, the LOB scheduler enforces crew continuity and the feasibility of work/rest. An exhaustive enumeration of contractor counts establishes the theoretical performance ceiling, and statistical validation using ANOVA and independent t-tests assesses the significance of optimization gains. The unconstrained exhaustive search identifies a global optimum of 5.93 weeks with a contractor allocation of (8, 10, 8, 10). With realistic resource constraints of up to three contractors per task, the GA achieves a project duration of 25.50 weeks, a statistically significant 10% improvement over the 28.32-week single-contractor baseline (ANOVA: F = 6.94, p = 0.009). Task-level t-tests reveal no significant change in individual task durations (all p > 0.05), demonstrating that efficiency gains arise exclusively from optimized concurrency and sequencing rather than task compression. This is the first study to apply a GA–LOB hybrid framework to petroleum TAM, formally integrating work/rest cycle constraints and SIMOP safety logic within the optimization chromosome. The resulting framework provides a scalable, data-driven diagnostic tool for industrial asset management with direct applicability to multi-site refinery operations.
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