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The use of resource management techniques is crucial to resolve conflicts and achieve the efficient utilization of resources. Particularly, resource-leveling techniques schedule activities in unconstrained-resource conditions to minimize fluctuations in resource profiles. According to the literature, resource leveling has typically been performed by considering that the original project duration remains fixed. Virtually, some extension in the project duration might be acceptable should a considerable enhancement in resource leveling be achieved. This paper enhances resource leveling through devising schedules of extended duration that exhibit resource profiles of lower fluctuation. Critical path method (CPM) networks of extended total floats are utilized to provide expanded yet definite spaces to search for schedules of lower resource fluctuation. The modified CPM networks accommodate for employing optimization models and searching optimal or near-optimal solutions. For demonstration, a genetic algorithm model was formulated to solve two case-study networks of 30 and 120 activities. The results indicate that schedules of lower fluctuation in resource profiles were obtained beyond the original networks' duration. [ABSTRACT FROM AUTHOR]

Compressing project schedule using activity accelerating and overlapping requires that an intensive time-cost trade-off analysis be carried out, to determine costs and benefits for each day of compression. However, the cost elements and implications of compression techniques differ significantly, since activity accelerating imposes extra direct cost whereas activity overlapping adds a risk of changes and rework. Such a trade-off becomes even more complicated in capital projects comprised of a large number of schedule activities and relationships. The variety of combinations of accelerating and overlapping of different activities in these complex networks can offer numerous possibilities for compression with various costs and potential risks. The lack of a reliable analytical tool for performing a precise cost-benefit analysis causes this critical task to be performed in a subjective manner during the planning stage of projects. The purpose of this paper is to present an advanced method using a multi-objective evolutionary optimization tool seeking the optimum degree of accelerating and overlapping during the schedule compression process. This optimization technique would be beneficial in maximizing project benefits while meeting the intended target dates. [ABSTRACT FROM AUTHOR]

The generalized model of the resource-constrained project scheduling problem (RCPSP) is valuable because it can be incorporated into the advanced computational methods of commercial project management software for practical applications. A construction schedule generated by most commercial project management programs does not guarantee its optimality when the resources are limited. This paper presents an improved elitist genetic algorithm (GA) for resource-constrained scheduling of large projects. The proposed algorithm allocates multiple renewable resources to activities of a single large-sized project to achieve the objective of minimizing the project duration. A permutation-based decoding procedure is developed using the improved parallel schedule generation scheme. A new parameter, named transformation power, is created in the transformation method of the improved algorithm to ensure that the individual selection process performs correctly. Extensive computational results using a standard set of large-sized multiple resource-constrained project scheduling problems are presented to demonstrate the performance and accuracy of the algorithm. [ABSTRACT FROM AUTHOR]

The resource-constrained project scheduling problem (RCPSP) is one of the most challenging problems in construction scheduling applications, in which optimal solutions are of great value to project planners. This paper presents a new adaptive hybrid genetic algorithm search simulator (AHGASS) for finding an optimal solution to the problem, and provides the strategies and practical procedures to develop the algorithm. Elitist genetic algorithm (EGA) developed is used for the global search, while random walk algorithm for the local search is incorporated into the EGA to overcome the drawbacks of general genetic algorithms, which are computationally intensive and premature convergence to a local solution. Computational experiments are presented to demonstrate the performance and accuracy of AHGASS. The proposed algorithm provides a comparable and competitive performance compared with the existing genetic algorithm (GA) hybrid heuristic methods. The findings demonstrate that AHGASS has significant promise for solving a large-sized RCPSP. [ABSTRACT FROM AUTHOR]