## 2 Literature Review
[48] developed a CP-based adaptive neighborhood search model to solve vehicle routing problems dealing with the logistical issues of large items for retailer companies, where the solution focuses on the synchronization of arrival times of delivering items and the crew for installation in customer sites. [PERSON] and et al. [49] proposed CP models to solve he integrated port container terminal problem (IPCTP) that considers the optimization of the intersectional operations quay and yard crane assignments and scheduling, yard location selection, and yard truck assignment and scheduling. For maintenance issues, [PERSON] and et al. [50] proposed an optimization algorithm for locomotive maintenance scheduling. Different objectives were discussed in this research. Such objectives include the minimization of several important time-related variables, such as total and average waiting times of locomotives, and the start and finish time of maintenance on all locomotives. Compared with the heuristic algorithm, CP with the ILOG CPLEX Optimizer provides better results in less time. On the other hand, resource limitation is another issue to consider and has great influence on optimization problems in various fields. In construction sites, resource allocation issues are crucial to a project's success, since the available resources are always less than the required resources. In the proposed two-stage optimization model, the bridge maintenance problem is treated as the special case in the resource-constrained project scheduling problem (RCPSP). RCPSP is a problem domain used to search for the optimal solution in terms of project schedules, in order to minimize the project's duration based on resource limitations and precedence constraints [9]. RCPSP became a challenging problem for project managers based on project characteristics, such as different types of activities, massive resource usage, and complex precedence relationships [10; 11]. On the other hand, the main reason for project delays is a lack of proper resource-constrained scheduling [12]. In a resource-constrained environment, typical time-cost trade-off analyses are discussed, and moreover, the overall evaluations of project performance in regard to quality and environmental impacts should be further considered [51; 52]. The RCPSP framework was adopted to solve scheduling problems in project management, manufacturing planning, and construction management. Several RCPSP applications in the construction management field have been conducted in the last decade. [PERSON] and [PERSON] [53] proposed a CP-based optimization model which aims to integrate RCPSP and cash flow problems, where typical calculations of cash flows usually ignore resource usage. This model was developed from a contractor's point of view and attempted to tackle the management goal of profit maximization. [PERSON] et al. [54] used a genetic algorithm (GA) to solve the resource constrained multi-project scheduling problem (RCMPSP). The goal is to find a schedule of the activities (i.e., to determine the start and completion times of the detailed activities). It takes into consideration multi-resource and inter-project precedence constraints, and the objective function is overall project duration minimization. Several alternative solutions representing different decoding methods were tested with different sizes of projects from 10 to 50 projects, where activity number varies from 1200 to 6000 activities. [PERSON] and [PERSON] [55] also adopted a GA to solve RCPSPs with the consideration of two objectives: minimization of the project's overall duration and total cost. The strength Pareto evolutionary approach II (SPEA II) was assessed with a proposed innovative chromosome design. In the past decade, research of RCPSP has utilized many metaheuristics methods, such as particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO), and simulated annealing (SA). However, none of these methods can well perform in all situations [56].