Benefits Of Construction Projects Research Articles

Optimizing multi-mode time-cost-quality trade-off of construction project using opposition multiple objective difference evolution

The time, cost and quality are crucial, conflicting aspects to construction project management. Tradeoff optimization among project duration (time), project cost, and the project quality within the project scope is necessary to enhance overall construction project benefit. A novel optimization algorithm, Opposition-based Multiple Objective Differential Evolution (OMODE), is presented to solve the time-cost-quality tradeoff (TCQT) problem. This novel algorithm employs an opposition-based learning technique for population initialization and for generation jumping. Opposition numbers are used to improve the exploration and convergence performance of the optimization process. A numerical high-way construction project case study is analyzed to illustrate the use of the algorithm and demonstrate its capabilities in generating non-dominated solutions. Comparisons with non-dominated sorting genetic algorithm (NSGA-II), multiple objective particle swarm optimization (MOPSO), multiple objective differential evolution (MODE) and previous results verify the efficiency and effectiveness of the proposed algorithm. This study is expected to provide an alternative solving methodology for the TCQT problem and help project manager plan construction methods with optimal time-cost-quality tradeoff.

Time-Cost Trade-off Optimization of Construction Projects using Teaching Learning Based Optimization

Accelerating the project schedule raises the total cost of the project and shall be efficient only up to a certain limit. A Time-Cost Trade-off Problem (TCTP) is utilized to detect the optimal set of time-cost alternatives to enhance the overall construction project benefit. In this study, to find a set of Pareto front solutions, a multi-objective optimization model which is based on the Teaching- Learning Based Optimization (TLBO) incorporated with the Modified Adaptive Weight Approach (MAWA), is proposed. Four examples of construction projects taken from the technical literature ranging from 7 to 63 activities are investigated to show the performance of the MAWA-TLBO. The results are compared with those obtained using previously proposed models considering the optimal or near optimal solutions. It was found that the MAWA-TLBO algorithm works effectively for the TCTP in construction engineering and management field.

Opposition-based Multiple Objective Differential Evolution (OMODE) for optimizing work shift schedules

Abstract Work shift schedules are often utilized in construction projects to meet project deadlines. Nevertheless, evening and night shifts raise the risk of adverse events and thus must be used to the minimum extent feasible. Tradeoff optimization among project duration (time), project cost, and the utilization of evening and night work shifts while maintaining with all job logic and resource availability constraints is necessary to enhance overall construction project benefit. This paper develops a novel optimization algorithm, the Opposition-based Multiple Objective Differential Evolution (OMODE), to solve the time–cost-utilization work shift tradeoff (TCUT) problem. This novel algorithm employs an opposition-based learning technique for population initialization and for generation jumping. Opposition numbers are used to improve the exploration and convergence performance of the optimization process. Two numerical case studies of construction projects demonstrate the ability of OMODE generated non-dominated solutions to assist project managers to select an appropriate plan to optimize TCUT, an operation that is otherwise difficult and time-consuming. Comparisons with the non-dominated sorting genetic algorithm (NSGA-II), multiple objective particle swarm optimization (MOPSO), and multiple objective differential evolution (MODE) verify the efficiency and effectiveness of the proposed algorithm.

Two-Phase Differential Evolution for the Multiobjective Optimization of Time–Cost Tradeoffs in Resource-Constrained Construction Projects

Concurrent minimization of project time and project cost is an important issue in construction planning and management. Tradeoff optimization between these two variables is necessary to maximize overall construction project benefit. This paper presents a two-phase differential evolution (DE) model to resolve these problems. This model is able to effectively consider both time-cost effects and resource constraints. First, we introduce a novel multiple-objective algorithm, the chaotic initialized multiple objective differential evolution with adaptive-mutation strategy-based time-cost tradeoff, to determine the execution mode that best optimizes the time-cost balance. Subsequently, we introduce a DE-based resource-constrained method to generate a feasible schedule. A real construction case study is then used to illustrate the application of the proposed algorithm. Performance comparisons done with the nondominated sorting genetic algorithm, multiple objective particle swarm optimization, and multiple objective differential evolution further verify the efficiency and effectiveness of the proposed algorithm.

Opposition-Based Multiple-Objective Differential Evolution to Solve the Time–Cost–Environment Impact Trade-Off Problem in Construction Projects

AbstractCurrent competitive conditions in the construction market require that construction companies satisfy customer needs using increasingly tight project budgets. The key indicators of success currently used on most construction projects include meeting project duration, cost, and quality targets. Project decision makers seldom consider customer expectations related to project-related environmental impact. Quantitative assessments of project-related emissions should be conducted during the project-planning phase. Trade-off optimization among project duration (time), project cost, and project environmental impact is necessary to enhance the overall construction project benefit. This paper develops a novel optimization algorithm, the opposition-based multiple-objective differential evolution (OMODE), to solve the time–cost–environmental impact tradeoff (TCET) problem. This novel algorithm uses an opposition-based learning technique for population initialization and for generation jumping. Opposition num...