# Multi-Objective Resource-Constrained Scheduling in Large and Repetitive Construction Projects
## 2 Related Work  
Several approaches and algorithms have been developed in the past to address the typical RCSP problem. These techniques can be divided into three categories: exact methods, heuristic methods, and meta-heuristic and evolutionary algorithms. In the first category, linear/integer programming (LP/IP) has been widely used. In this approach, mathematical relationships are built that linearly express the problem goals and limitations, and pertinent methods (e.g., the Simplex method) are used to develop a solution. Examples of works that solve scheduling problems using linear programming include those in [1; 2]. As the project's size and complexity increase, these methods eventually prove to be ineffective for setting up the computerized problem structure, even though they can offer exact solutions. Since the solution space is often large to fully explore, especially as the problem size increases, heuristic techniques are used to first generate lists of feasible solutions and then search among them by bounding and pruning until a (near) optimal solution is obtained (branch and bound algorithms). The main drawbacks of such approaches are that the best solution is not always guaranteed and there is no single universal bounding algorithm that can be used to solve all problems. The work in [3] might be referred to as representative of such type of methods. Metaheuristic techniques or evolutionary algorithms are used to provide near-optimal solutions when the precise solution to a problem is not known or is computationally demanding to determine. Evolutionary algorithms imitate the selection process in which, within the population set, members that least fit are eliminated while suitable members survive and multiply towards better solutions. In this direction, the studies [4; 5] have employed Genetic Algorithms to explore near-optimal solutions in resource-constrained scheduling problems. Genetic Algorithms have also been applied to level daily resource utilization and minimize project makespan [6; 7]. Other studies have created hybrid evolutionary algorithms or approaches by combining Genetic Algorithms with other evolutionary algorithms or techniques to produce better results [8]. The Ant Colony Optimization (ACO) algorithm [9], along with various hybrid ACO structures [10], has been employed to address the resource-constrained scheduling problem in both its classical formulation and its extended forms, such as those involving alternative execution modes. The employment of Harmony Search Algorithm (HS) to optimize the multi-mode resource-constrained project scheduling problem is reported in [11]. In a similar vein, the Particle Swarm Optimization (PSO) algorithm has also been utilized for optimizing resource-constrained scheduling problems, with an illustrative application detailed in [12]. The research in [13] uses the Artificial Bee Colony algorithm to solve problems from the PSPLIB library and compares it with several different algorithms from the literature such as GA, PSO, PSO +, ANGEL, and others. The research in [14] evaluates the performance of different types of bee algorithms (Bee Algorithm, Artificial Bee Colony, and Bee Swarm Optimization) for solving RCSP problems. The effectiveness of the Differential Evolution (DE) algorithm has been evaluated in [15]. The works in [16; 17] address complex scheduling problems by developing hyper-heuristic algorithms based on Particle Swarm Optimization and Simulated Annealing, respectively. A hyper-heuristic algorithm is defined as a top-level heuristic algorithm that controls the application of other lower-level algorithms applied to the problem solution space. Finally, the concept of entropy-maximization for project resource-leveling has been introduced in [18] based on a minimum moment parameter of the resource time series. The challenge of maintaining the continuity of various resources working on different tasks or sub-projects is a distinctive trait of repetitive projects.