Genetic Algorithm for Singular Resource Constrained Project Scheduling Problems

Abstract

The Resource-Constrained Project Scheduling Problem (RCPSP) is a challenging optimization problem. In RCPSPs, it is very common to consider homogeneous activities, which means all activities require all types of resources. In practice, the activities are often singular because they usually require one single resource to execute an activity. The existing algorithms may be used for solving this variant of RCPSPs with a simple modification. However, they are computationally expensive due to unnecessary resource constraints. In this paper, we propose a customised evolutionary algorithm integrated with three heuristics for the singular activities. The first heuristic is based on the earliest start time with an aim to rectify an infeasible schedule. The second heuristic is based on neighbourhood swapping which is used to find the best possible alternatives. The third heuristic is used to further enhance the quality of the schedule. The performance of the proposed framework has been tested by solving a wide range of benchmark problems and the obtained results revealed that the proposed approach outperformed the existing algorithms. In addition, statistical and parametric testing show the value and characteristics of the proposed approach.

A Practical Approach for Resource-Constrained Project Scheduling

“…This is the case, since the problem is difficult to solve (proven to be NP-hard [31]), thus heuristic algorithms are required to solve real-world instances that are required by the organizations. Metaheuristic algorithms such as ant-colony optimization (ACO), particle swarm optimization (PSO), and genetic algorithms (GAs) have also been used to address the complexity of the problem [14], [29], [30], [32]- [36]. For instance, the authors in [30], [32] solve the software project scheduling problem using an ACO metaheuristic, obtaining better results in terms of running time or problem objective for some instances compared to other algorithms using other methods such as genetic algorithms.…”

“…For example in [37], authors use a combination of tabu search and simulated annealing to solve the RCPS problem. In addition, the authors in [35], [36] solve variations of the RCPS problem using a combination of several heuristic and metaheuristic algorithms to address large scale instances. Finally, machine learning techniques (e.g., such as reinforcement learning) can be employed to solve scheduling problems in dynamic environments where agents cooperate to achieve group missions [38].…”

Metaheuristics Portfolio Optimization for Resource-Constrained Project Scheduling Problems with Milestones Payments

“…Notable metaheuristics hybrid designs for RCPSPDCF include Vanhoucke's fusion of bi-directional FBI with scatter search [47], Gu et al's hybridization of FBI with Lagrangian relaxation and constraint programming [48], the combination of immune genetic algorithm (IGA) and variable insertion neighborhood search with FBI in [49] and [44], the adoption of multi-operator IGA [49], which draws inspiration from efficient schemes proposed in works [50], [51], [52]. These hybridizations have improved the solutions for RCPSPDCF.…”

Project scheduling based on resource leveling using fuzzy ranking and genetic algorithm

“…5. Suitable efficiency of this method for project planning problems with limited resources in terms of solution time and quality of solutions [52][53][54].…”

“…On the other hand, one of the factors that casts doubt on the use of definite methods in solving the project scheduling problem is the uncertain nature of many activities, especially in terms of the duration they require to be completed. Researchers have recently turned to meta-heuristics to solve project scheduling problems with limited resources [52,54,55] and in fuzzy conditions. Table 1.…”