Algorithm For Resource-constrained Project Scheduling Problem Research Articles

A late-mover genetic algorithm for resource-constrained project-scheduling problems

The Resource-Constrained Project Scheduling Problem (RCPSP) plays a critical role in various management applications. Despite its importance, research efforts are still ongoing to improve lower bounds and reduce deviation values. This study aims to develop an innovative and straightforward algorithm for RCPSPs by integrating the “1+1” evolution strategy into a genetic algorithm framework. Unlike most existing studies, the proposed algorithm eliminates the need for parameter tuning and utilizes real-valued numbers and path representation as chromosomes. Consequently, it does not require priority rules to construct a feasible schedule. The algorithm's performance is evaluated using the RCPSP benchmark and compared to alternative algorithms, such as cWSA, Hybrid PSO, and EESHHO. The experimental results demonstrate that the proposed algorithm is competitive, while the exploration capability remains a challenge for further investigation.

An effective teaching–learning-based optimisation algorithm for RCPSP with ordinal interval numbers

To solve the resource-constrained project-scheduling problem (RCPSP) with ordinal interval numbers, this paper presents an effective teaching–learning-based optimisation (TLBO) algorithm. Ordinal interval number is introduced as a novel tool for handling vague information to describe the RCPSP under uncertain environment. An ordinal interval-based parallel schedule generation scheme is used to generate feasible schedules. Two new phases including the self-study phase and the exam phase are incorporated into the TLBO to enhance the teaching–learning process. In the self-study phase, the population is updated by a mutation operator to prevent premature convergence and to enhance exploration search. In the exam phase, elite students are selected to enhance exploitation search. Moreover, a novel ordinal interval resource-based crossover operator (OIRBCO) is well designed for both the teacher phase and the student phase of the TLBO. Computational comparisons between the OIRBCO and the existing two-point crossover show that OIRBCO is more effective due to the utilisation of the resource information. In addition, statistical comparisons with particle swarm optimisation and simulated annealing show that the proposed TLBO is more effective in solving the RCPSP with ordinal interval numbers.