Solution to the Time-Cost-Quality Trade-off Problem in Construction Projects Based on Immune Genetic Particle Swarm Optimization

Multiobjective optimization (MOO) dispatch for microgrids (MGs) can achieve many benefits, such as minimized operation cost, greenhouse gas emission reduction, and enhanced reliability of service. In this paper, a MG with the PV-battery-diesel system is introduced to establish its characteristic and economic models. Based on the models and three objectives, the constrained MOO problem is formulated. Then, an advanced multiobjective particle swarm optimization (MOPSO) algorithm is proposed to obtain Pareto optimization dispatch for MGs. The combination of archive maintenance and Pareto selection enables the MOPSO algorithm to maintain enough nondominated solutions and seek Pareto frontiers. The final trade-off solutions are decided based on the fuzzy set. The benchmark function tests and simulation results demonstrate that the proposed MOPSO algorithm has better searching ability than nondominated sorting genetic algorithm-II (NSGA-II), which is widely used in generation dispatch for MGs. The proposed method can efficiently offer more Pareto solutions and find a trade-off one to simultaneously achieve three benefits: minimized operation cost, reduced environmental cost, and maximized reliability of service.

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Overall multiobjective optimization of construction projects scheduling using particle swarm

Engineering Construction & Architectural Management ◽

10.1108/ecam-11-2014-0135 ◽

2016 ◽

Vol 23 (3) ◽

pp. 265-282 ◽

Cited By ~ 21

Author(s):

Emad Elbeltagi ◽

Mohammed Ammar ◽

Haytham Sanad ◽

Moustafa Kassab

Keyword(s):

Cash Flow ◽

Construction Projects ◽

Real Life ◽

Particle Swarm ◽

Project Managers ◽

Time Cost ◽

Swarm Optimization ◽

Content Type ◽

Proposed Model ◽

Optimized Schedule

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.

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Stochastic Weight Trade-Off Particle Swarm Optimization for Optimal Power Flow

Journal of Automation and Control Engineering ◽

10.12720/joace.2.1.31-37 ◽

2014 ◽

Vol 2 (1) ◽

pp. 31-37 ◽

Cited By ~ 1

Author(s):

Luong Dinh Le ◽

Jirawadee Polprasert ◽

Weerakorn Ongsakul ◽

Dieu Ngoc Vo ◽

Dung Anh Le

Keyword(s):

Particle Swarm Optimization ◽

Power Flow ◽

Optimal Power Flow ◽

Particle Swarm ◽

Swarm Optimization ◽

Trade Off ◽

Optimal Power

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Particle Swarm Optimization Based Approach for Estimation of Costs and Duration of Construction Projects

Civil Engineering Journal ◽

10.28991/cej-2020-03091478 ◽

2020 ◽

Vol 6 (2) ◽

pp. 384-401 ◽

Cited By ~ 5

Author(s):

Tarq Zaed Khalaf ◽

Hakan Çağlar ◽

Arzu Çağlar ◽

Ammar Nasiri Hanoon

Keyword(s):

Particle Swarm Optimization ◽

Construction Projects ◽

Particle Swarm ◽

Building Design ◽

Construction Costs ◽

General Applicability ◽

Swarm Optimization ◽

Soft Computing Techniques ◽

Concrete Footing ◽

Duration Estimation

Cost and duration estimation is essential for the success of construction projects. The importance of decision making in cost and duration estimation for building design processes points to a need for an estimation tool for both designers and project managers. Particle swarm optimization (PSO), as the tools of soft computing techniques, offer significant potential in this field. This study presents the proposal of an approach to the estimation of construction costs and duration of construction projects, which is based on PSO approach. The general applicability of PSO in the formulated problem with cost and duration estimation is examined. A series of 60 projects collected from constructed government projects were utilized to build the proposed models. Eight input parameters, such as volume of bricks, the volume of concrete, footing type, elevators number, total floors area, area of the ground floor, floors number, and security status are used in building the proposed model. The results displayed that the PSO models can be an alternative approach to evaluate the cost and-or duration of construction projects. The developed model provides high prediction accuracy, with a low mean (0.97 and 0.99) and CoV (10.87% and 4.94%) values. A comparison of the models’ results indicated that predicting with PSO was importantly more precise.

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