Resumen
Earthwork scheduling (during the planning phase of road construction) is an important task that directly affects the cost and time of a project. However, the current scheduling methods are not performed at a detailed level and carry forward gaps from the actual schedule in the construction stage, causing problems, including time delays and additional costs, during the construction stage and thereby leading to the modification and supplementation of existing plans. Many studies related to scheduling have focused on process optimization or automation; therefore, the gaps between the planning and construction stages have not been studied well. These gaps must be determined in advance to solve the fundamental problem of earthwork scheduling in road construction. Therefore, this study proposes a detailed activity-based scheduling model for earthwork at the planning stage to minimize the gap between the planned schedule and the actual process at the construction stage. The proposed model comprises a detailed activity-based database of earthwork in road construction (DADER), which enables the subdivision of the existing earthwork schedule plan, and a dynamic programming (DP) procedure, which enables the combination of activities. The earthwork schedule at the case site, planned for only three activities, is subdivided using DADER into 36 activities. Additionally, the DP procedure is used to derive a combination of activity alternatives that minimizes the cost among the conditions that satisfied the input target duration. The model is verified through an expert survey using a 7-point Likert scale. Results show that the model has cost efficiency (4.19), onsite applicability (4.70), and task efficiency (4.48). The findings indicate that performing scheduling during the planning stage of earthwork projects can help reduce additional work caused by the gap between the site and the plan. This study will help improve the productivity of road construction projects by providing correct process optimization and automation research data.