### Construction Planning and Scheduling Optimization  
Construction planning and scheduling are two of the most important activities in all phases of construction. A well-planned and optimized construction schedule contributes significantly to a successful project. Several researchers adopted optimization techniques to solve construction planning and scheduling problems [21; 22; 23]. In the past, linear programming was one of the techniques most widely used to solve this issue. Multi-objective linear programming is suggested for optimization when faced with constrained resource problems, including construction costs, project duration, resource idle time, and project delivery time [24]. A particle swarm optimization is also suggested for construction scheduling problems in order to solve resource constraint issues [25]. An ant colony optimization, which focuses on minimizing project duration while varying activity sequences, is another approach for resource-constrained projects [26]. Genetic algorithms (GA) have been used in several construction scheduling research problems, such as resource allocation and leveling optimization [27], concrete precast production optimization [28], nonproductive resource determination [29], and building performance assessment for long time usages [8]. The basic concept and overview of the applications of optimization algorithm in construction processes can be further found in References [9; 30; 31], where advantages and limitations were also discussed. In real construction projects, there are always multiple objectives forming a set of solutions as optimization data for project managers to make decisions. Consequently, the field of multi-objective optimization has been studied intensively [32; 33]. Multi-objective genetic algorithms (MOGA) were first introduced by [PERSON] in 1995 to provide decision-makers with Pareto optimization instead of constant weights [34]. It has been discussed in several construction optimization studies. For example, it is used for construction time-cost trade-off optimization [35] by defining optimal time and cost with variable crane locations and crews by minimizing crane stoppage time [36]. It is also used to improve safety by optimizing safety costs on different project layouts [37]. Even construction quality is used as an objective function for MOGA along with construction project time and cost, with the results showing their weights and Pareto solutions for the repetitive scheduling method [38]. The optimization results can be used for the decision-maker in other applications, such as planning, scheduling, or even unpredictable situations such as construction logistics network during a hazard [39]. A combination of BIM and optimization techniques has been discussed in the past [30], such as genetic algorithms (GA), particle swarm optimization (PSO), and fuzzy theory working with Autodesk Revit as a BIM tool [9] used to improve information flow and visualization. An optimization technique such as a genetic algorithm was used with BIM for possibilities usage of information from BIM directly and parsing the data to GA directly [40]. More than the fuzzy-logic dataset was possible usages in GA for construction scheduling [41]. Other applications of BIM-based optimization were used for the construction assembly line [42], building energy performance evaluation [43] and planning of construction site layout [44]. Regarding such characteristics of renovation projects, more flexible planning models unique to the extra-large-scale renovation projects are needed in order to improve the planning efficiency in terms of actual construction time, total cost, and worker utilization fluctuation [27; 45; 46; 47], especially in real construction projects where there are always multiple objectives forming a set of solutions for project managers to make decisions. Therefore, with the performance of BIM, this paper explored opportunities to develop a systematic BIM-based multi-objective approach for construction planning and scheduling of renovation projects.