A novel algorithm for solving resource-constrained project scheduling problems: a case study

Abstract

Purpose

Construction projects managers try their best for the project to go according to the plans. They always attempt to complete the projects on time and consistent with the predetermined budgets. Amid so many problems in project planning, the most critical and well-known problem is the Resource-Constrained Project Scheduling Problem (RCPSP). The purpose of this paper is to solve RCPSP using hybrid algorithm ICA/PSO.

Design/methodology/approach

Due to the existence of various forms for scheduling the problem and also the diversity of constraints and objective functions, myriad of research studies have been conducted in this realm of study. Since most of these problems are NP-hard ones, heuristic and meta-heuristic methods are used for solving these problems. In this research, a novel hybrid method which is composed of meta-heuristic methods of particle swarm optimization (PSO) and imperialist competitive algorithm (ICA) has been used to solve RCPSP. Finally, a railway project has been examined for RCPS Problem in a real-world situation.

Findings

According to the results of the case study, ICA/PSO algorithm has better results than ICAs and PSO individually.

Practical implications

ICA/PSO algorithm could be used for solving problems in a multi-mode situation of activities or considering more constraints on the resources, such as the existence of non-renewable resources and renewable. Based on the case study in construction project, ICA/PSO algorithm has a better solution than PSO and ICA.

Originality/value

In this study, by combining PSO and ICA algorithms and creating a new hybrid algorithm, better solutions have been achieved in RCPSP. In order to validate the method, standard problems available in PSPLib library were used.

Citation

Kasravi, M., Mahmoudi, A. and Feylizadeh, M.R. (2019), "A novel algorithm for solving resource-constrained project scheduling problems: a case study", Journal of Advances in Management Research, Vol. 16 No. 2, pp. 194-215. https://doi.org/10.1108/JAMR-03-2018-0033