Overall multiobjective optimization of construction projects scheduling using particle swarm

Abstract

Purpose – Developing an optimized project schedule that considers all decision criteria represents a challenge for project managers. The purpose of this paper is to provide a multi-objectives overall optimization model for project scheduling considering time, cost, resources, and cash flow. This development aims to overcome the limitations of optimizing each objective at once resulting of non-overall optimized schedule. Design/methodology/approach – In this paper, a multi-objectives overall optimization model for project scheduling is developed using particle swarm optimization with a new evolutionary strategy based on the compromise solution of the Pareto-front. This model optimizes the most important decisions that affect a given project including: time, cost, resources, and cash flow. The study assumes each activity has different execution methods accompanied by different time, cost, cost distribution pattern, and multiple resource utilization schemes. Findings – Applying the developed model to schedule a real-life case study project proves that the proposed model is valid in modeling real-life construction projects and gives important results for schedulers and project managers. The proposed model is expected to help construction managers and decision makers in successfully completing the project on time and reduced budget by utilizing the available information and resources. Originality/value – The paper presented a novel model that has four main characteristics: it produces an optimized schedule considering time, cost, resources, and cash flow simultaneously; it incorporates a powerful particle swarm optimization technique to search for the optimum schedule; it applies multi-objectives optimization rather than single-objective and it uses a unique Pareto-compromise solution to drive the fitness calculations of the evolutionary process.

Scientometric Review of Construction Project Schedule Studies: Trends, Gaps and Potential Research Areas

“…Developing countries including China and India also conducted research related to time-cost-trade off problems (Zou et al, 2015), ant colony schedule-cost optimization and resource allocation improvement strategy (Zou et al, 2014). However, countries from Africa have less contribution in CPS research except Egypt which published articles on schedule optimization using swarm particle (Elbeltagi et al, 2016). This result is consistent as most of the construction projects are delayed in African countries.…”

Multi-objective Comprehensive Optimal Management of Construction Projects Based on Particle Algorithm

“…Elbeltagi et al [4] developed a multi-objective overall optimization model for project scheduling by using the new evolutionary strategy of particle swarm optimization and compromise solution based on Pareto optimal boundary. The experimental results showed that the model will help construction managers and decision makers to complete projects on time and reduce budgets through using existing information and resources.…”

“…In the construction project management, the network plan chart is usually used to plan the construction period. In this study, the double code network chart [7] is used to represent the construction period plan of the construction project, the nodes in the double code network represent events, and the line arrows between nodes represent the working process. In actual construction projects, the network plan can not only have one line, which must have several branches, meaning that part of the construction process can be carried out simultaneously and finally converge at the end node.…”

Developing a framework to revolutionise the 4D BIM process: IPD-based solution

“…Regarding the multi-objective scheduling optimisation, Elbeltagi et al (2016) proposed a model to optimise the schedule based on multi-criteria such as time, cost, resources, and cash flow. The method was particle swarm optimisation in order to reach the optimal schedule.…”

“…Furthermore, Elbeltagi et al (2016) developed a model to optimise the schedule based on multi-criteria, these criteria are the time, cost, resources, and cash flow. The used method was the particle swarm optimisation in order to determine the optimal path of activities based on several possible solutions to execute each activity(Elbeltagi et al, 2016). However, the proposed model relies on collecting data manually in order to enable implementing the optimisation This paper is accepted to be published in Construction Innovation Information, Process, Management journal.…”