In this paper, a multi-objective project scheduling problem is addressed. This problem considers two conflicting, priority optimization objectives for project managers. One of these objectives is to minimize the proje...
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In this paper, a multi-objective project scheduling problem is addressed. This problem considers two conflicting, priority optimization objectives for project managers. One of these objectives is to minimize the project makespan. The other objective is to assign the most effective set of human resources to each project activity. To solve the problem, a multi-objective hybrid search and optimization algorithm is proposed. This algorithm is composed by a multi-objective simulated annealing algorithm and a multi-objective evolutionary algorithm. The multi-objective simulated annealing algorithm is integrated into the multi-objective evolutionary algorithm to improve the performance of the evolutionary-based search. To achieve this, the behavior of the multi-objective simulated annealing algorithm is self-adaptive to either an exploitation process or an exploration process depending on the state of the evolutionary-based search. The multi-objective hybrid algorithm generates a number of near non-dominated solutions so as to provide solutions with different trade-offs between the optimization objectives to project managers. The performance of the multi-objective hybrid algorithm is evaluated on nine different instance sets, and is compared with that of the only multi-objective algorithm previously proposed in the literature for solving the addressed problem. The performance comparison shows that the multi-objective hybrid algorithm significantly outperforms the previous multi-objective algorithm. (c) 2012 Elsevier Ltd. All rights reserved.
The aim of this paper is to present a multi-objective project scheduling approach to help project managers when deciding on a baseline schedule. The concepts of satisfaction functions and goal programming are incorpor...
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The aim of this paper is to present a multi-objective project scheduling approach to help project managers when deciding on a baseline schedule. The concepts of satisfaction functions and goal programming are incorporated to generate this baseline schedule that represents the best compromise among a set of conflicting projectobjectives. An efficient computerized procedure based on the tabu search algorithm is proposed and enables the handling of large planning and schedulingprojects.
Traditionally, projectscheduling and environmental impact assessment are carried out separately which is illsuited for modern organizations as creating a project plan that optimizes for environmental goals can have a...
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Traditionally, projectscheduling and environmental impact assessment are carried out separately which is illsuited for modern organizations as creating a project plan that optimizes for environmental goals can have an adverse effect on project time and resource utilization efficiency. This study aims to address this issue by considering project time, greenhouse gas emissions, and resource-leveling objectives simultaneously. multiobjective Grey Wolf Optimization algorithm has been modified by incorporating genetic operators and a local search heuristic and its performance was compared with the elitist non-dominated sorting genetic algorithm II on six assessment metrics. The computational experiments exhibited that the modified multi-objective Grey Wolf Optimization algorithm outdid the non-dominated sorting genetic algorithm on five out of six assessment metrics. Two case studies have been used to demonstrate the applicability and validity of the presented model. In the obtained Pareto front solutions for the real-life case study, the range of time, greenhouse gas emissions, and resource deviation have been 594-835 (days), 19,870,953.1-20,938,469.6 (kgCO2-e) and 67,204.93-141,915.55 (unitless) respectively. The examination of tradeoffs between objectives showed that greenhouse gas emissions can be decreased if the makespan and resource deviation are allowed a slight increase at various decision points. The presented model can help practitioners and researchers make practical tradeoffs between the conflicting goals of duration, greenhouse gas emissions, and resource optimization.
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