Particle Swarm Optimization for Resource Constrained-project Scheduling Problem with Varying Resource Levels

Joy, Jiby; Rajeev, Srijith; Narayanan, Vishnu

doi:10.1016/j.protcy.2016.08.185

Cited by 28 publications

(9 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Benefits of PSO over other metaheuristic approaches are computational feasibility and effectiveness. PSO shows its uniqueness such as easy implementation and consistency in performance [97].…”

Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

Short-Term Load Forecasting Using Neural Network and Particle Swarm Optimization (PSO) Algorithm

Chafi

Afrakhte

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Electrical load forecasting plays a key role in power system planning and operation procedures. So far, a variety of techniques have been employed for electrical load forecasting. Meanwhile, neural-network-based methods led to fewer prediction errors due to their ability to adapt properly to the consuming load's hidden characteristic. Therefore, these methods were widely accepted by the researchers. As the parameters of the neural network have a significant impact on its performance, in this paper, a short-term electrical load forecasting method using neural network and particle swarm optimization (PSO) algorithm is proposed, in which some neural network parameters including learning rate and number of hidden layers are determined in order to forecast electrical load using the PSO algorithm precisely. Then, the neural network with these optimized parameters is used to predict the short-term electrical load. In this method, a three-layer feedforward neural network trained by backpropagation algorithm is used beside an improved gbest PSO algorithm. Also, the neural network prediction error is defined as the PSO algorithm cost function. The proposed approach has been tested on the Iranian power grid using MATLAB software. The average of three indices beside graphical results has been considered to evaluate the performance of the proposed method. The simulation results reflect the capabilities of the proposed method in accurately predicting the electrical load.

show abstract

Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

Short-Term Load Forecasting Using Neural Network and Particle Swarm Optimization (PSO) Algorithm

Chafi

Afrakhte

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Multi-objective linear programming is suggested for optimization when faced with constrained resource problems, including construction costs, project duration, resource idle time, and project delivery time [24]. A particle swarm optimization is also suggested for construction scheduling problems in order to solve resource constraint issues [25]. An ant colony optimization, which focuses on minimizing project duration while varying activity sequences, is another approach for resource-constrained projects [26].…”

Section: Construction Planning and Scheduling Optimizationmentioning

confidence: 99%

Construction Planning and Scheduling of a Renovation Project Using BIM-Based Multi-Objective Genetic Algorithm

et al. 2021

View full text Add to dashboard Cite

Renovation is known to be a complicated type of construction project and prone to errors compared to new constructions. The need to carry out renovation work while keeping normal business activities running, coupled with strict governmental building renovation regulations, presents an important challenge affecting construction performance. Given the current availability of robust hardware and software, building information modeling (BIM) and optimization tools have become essential tools in improving construction planning, scheduling, and resource management. This study explored opportunities to develop a multi-objective genetic algorithm (MOGA) on existing BIM. The data were retrieved from a renovation project over the 2018–2020 period. Direct and indirect project costs, actual schedule, and resource usage were tracked and retrieved to create a BIM-based MOGA model. After 500 generations, optimal results were provided as a Pareto front with 70 combinations among total cost, time usage, and resource allocation. The BIM-MOGA can be used as an efficient tool for construction planning and scheduling using a combination of existing BIM along with MOGA into professional practices. This approach would help improve decision-making during the construction process based on the Pareto front data provided.

show abstract

“…Using PSO algorithm, Joy et al solved resource-constrained project scheduling problem with varying resource levels. The available resources were limited in these problems but the different levels of resources were determined beforehand and the resource levels were not fixed during the whole project (Joy et al , 2016; Elbeltagi et al , 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

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

Kasravi

Mahmoudi

Feylizadeh

2019

JAMR

View full text Add to dashboard Cite

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.

show abstract

Particle Swarm Optimization for Resource Constrained-project Scheduling Problem with Varying Resource Levels

Cited by 28 publications

References 6 publications

Short-Term Load Forecasting Using Neural Network and Particle Swarm Optimization (PSO) Algorithm

Short-Term Load Forecasting Using Neural Network and Particle Swarm Optimization (PSO) Algorithm

Construction Planning and Scheduling of a Renovation Project Using BIM-Based Multi-Objective Genetic Algorithm

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

Contact Info

Product

Resources

About