When scheduling projects under resource constraints, assumptions are typically made with respect to the resource availability. In resource scheduling problems important assumptions are made with respect to the resource requirements. As projects are typically labour intensive, the underlying (personnel) resource scheduling problems tend to be complex due to different rules and regulations. In this paper, we aim to integrate these two interrelated scheduling problems to minimise the overall cost. For that purpose, we propose an exact algorithm for the project staffing with resource scheduling constraints. Detailed computational experiments are presented to evaluate different branching rules and pruning strategies and to compare the proposed procedure with other optimisation techniques.
This research introduces novel control metrics for projects that use cost and/or time incentives. The proposed technique extends the traditional earned value management (EV M) methodology for project control. This is done by measuring the deviation in the accrual of incentives, rather than the time and cost performance relative to the planned schedule. The proposed dedicated approach avoids two key issues when controlling incentivized projects using traditional earned value management. Firstly, the impact of variations in the cost and time dimensions are adequately weighted in the control signals. Secondly, the technique is capable of monitoring the potential non-linear accrual of incentive amounts throughout the project. The performance of the proposed technique is tested by means of a computational experiment on 4200 projects of varying size, structure and type of incentive contract. The results show that the proposed technique improves signal quality when compared to traditional EV M metrics.
Batselier, Jordy; Vanhoucke, Mario (Elsevier, 2017)
In this paper, the earned value management (EVM) project control methodology is integrated with the exponential smoothing forecasting approach. This results in an extension of the known EVM and earned schedule (ES) cost and time forecasting formulas. A clear correspondence between the established approaches and the newly introduced method - called the XSM - is identified, which could facilitate future implementation. More specifically, only one smoothing parameter is needed to calculate the enhanced EVM performance factor. Moreover, this parameter can be dynamically adjusted during project progress based on information of past performance and/or anticipated management actions. Additionally, the reference class forecasting (RCF) technique can be incorporated into the XSM. Results from 23 real-life projects show that, for both time and cost forecasting, the XSM exhibits a considerable overall performance improvement with respect to the most accurate project forecasting methods identified by previous research, especially when incorporating the RCF concept.
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