A Genetic Algorithm-Based Model for Intelligent Scheduling and Route Optimization of Construction Engineering Inspection Tasks

Abstract

Construction engineering inspection agencies may suggest that uneven task allocation, underused equipment, and inefficient routes create significant challenges for field inspection work. However, the findings from this study could indicate that intelligent scheduling and route optimization offer key solutions to these important problems. Moreover, the research establishes a multi-objective optimization model based on a genetic algorithm, using inspection tasks, inspectors, equipment, and project locations as its foundational elements. In light of these elements, the model may demonstrate that jointly considering task deadlines, staff competencies, equipment applicability, working time, scheduling cost, and route distance within one analytical framework could yield significant results. Model shows algorithm reduces complexity. Furthermore, the solution procedure could indicate that a hybrid chromosome, a fitness function, a penalty mechanism, and a set of selection, crossover, mutation, and feasibility-repair operations appear to provide critical support for the model. Given that the simulation case demonstrates these results, the findings may suggest that the genetic-algorithm solution reduces total route distance by 26.2%, cuts the estimated total completion time by 22.0%, and lowers comprehensive scheduling cost by 15.8%. Additionally, the significant results could demonstrate that the average utilization rates of personnel and equipment rise to 86.2% and 82.5%, respectively, while the on-time task completion rate increases to 100%. Nevertheless, the evidence may indicate that these key findings appear to confirm the genetic algorithm can handle the construction engineering inspection scheduling problem under multiple tasks, multiple personnel, multiple devices, and multiple locations. Algorithm handles multi-variable scheduling efficiently. Therefore, the study could suggest that the model may provide a quantitative decision-making basis for inspection agencies that appear to need more efficient field task organization. Notwithstanding the limitations of experience-based manual scheduling, the significant evidence may indicate that this approach could demonstrate important reductions in operating costs while raising resource utilization to key levels.