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A project buffer and resource management model in energy sector: A case study in construction of a wind farm projectPurpose This study aims to introduce an efficient project buffer and resource management (PBRM) model for project resource leveling and project buffer sizing and controlling of project buffer consumption of a wind power plant project to achieve a more realistic project duration. Design/methodology/approach The methodology of this research consists of three main phases. In the first phase of the research methodology, resource leveling is done in the project and resource conflicts of activities are identified. In the second phase, the project critical chain is determined, and the appropriate size of the project buffer is specified. In the third phase of the methodology, buffer consumption is controlled and monitored during the project implementation. After using the PBRM method, the results of this project were compared with those of the previous projects. Findings According to the obtained results, it can be concluded that using PBRM model in this wind turbine project construction, the project duration became 25 per cent shorter than the scheduled duration and also 29 per cent shorter than average duration of previous similar projects. Research limitations/implications One of the major problems with projects is that they are not completed according to schedule, and this creates time delays and losses in the implementation of projects. Today, as projects in the energy sector, especially renewable projects, are on the increase and also we are facing resource constraint in the implementation of projects, using scheduling techniques to minimize delays and obtain more realistic project duration is necessary. Practical implications This research was carried out in a wind farm project. In spite of the initial plan duration of 142 days and average duration of previous similar projects of 146 days, the project was completed in 113 days. Originality/value This paper introduces a practical project buffer and resource management model for project resource leveling, project buffer sizing and buffer consumption monitoring to reach a more realistic schedule in energy sector. This study adds to the literature by proposing the PBRM model in renewable energy sector.
Multimode time-cost-robustness trade-off project scheduling problem under uncertaintyThe time/cost trade-off problem is a well-known project scheduling problem that has been extensively studied. In recent years, many researchers have begun to focus on project scheduling problems under uncertainty to cope with uncertain factors, such as resource idleness, high inventory, and missing deadlines. To reduce the disturbance from uncertain factors, the aim of robust scheduling is to generate schedules with time buffers or resource buffers, which are capped by project makespan and project cost. This paper addresses a time-cost-robustness trade-off project scheduling problem with multiple activity execution modes under uncertainty. A multiobjective optimization model with three objectives (makespan minimization, cost minimization, and robustness maximization) is constructed and three propositions are proposed. An epsilon-constraint method-based genetic algorithm along with three improvement measures is designed to solve this NP-hard problem and to develop Pareto schedule sets, and a large-scale computational experiment on a randomly generated dataset is performed to validate the effectiveness of the proposed algorithm and the improvement measures. The final sensitivity analysis of three key parameters shows their distinctive influences on the three objectives, according to which several suggestions are given to project managers on the effective measures to improve the three objectives.
Automatic detection of the best performing priority rule for the resource-constrained project scheduling problemPriority rules are applied in many commercial software tools for scheduling projects under limited resources because of their known advantages such as the ease of implementation, their intuitive working, and their fast speed. Moreover, while numerous research papers present comparison studies between different priority rules, managers often do not know which rules should be used for their specific project, and therefore have no other choice than selecting a priority rule at random and hope for the best. This paper introduces a decision tree approach to classify and detect the best performing priority rule for the resource-constrained project scheduling problem (RCPSP). The research relies on two classification models to map project indicators onto the performance of the priority rule. Using such models, the performance of each priority rule can be predicted, and these predictions are then used to automatically select the best performing priority rule for a specific project with known network and resource indicator values. A set of computational experiment is set up to evaluate the performance of the newly proposed classification models using the most well-known priority rules from the literature. The experiments compare the performance of multi-label classification models with multi-class classification models, and show that these models can outperform the average performance of using any single priority rule. It will be argued that this approach can be easily extended to any extension of the RCPSP without changing the methodology used in this study.
Pertinent insights from Europe on executive compensationThroughout this article, it became clear that there is no “European way” of executive compensation as a number of geographical differences were found to be present. However, the research shows that the variance is explained by determinants such as business size, the industry in which the company operates and the share ownership structure. The article also looked at the underlying key performance indicators used for incentive systems. Not surprisingly, financial indicators were found to be most prevalent, determining on average 70% of the bonus while company size was the main driver of CEO compensation. We found that CEO compensation policies in the best performing companies, over a longer period of time, are characterized by modesty. This applies to compensation levels, the weight of incentives in the total package, and the spread between target and maximum bonus.
Least-cost distribution network tariff design in theory and practiceIn this paper a game-theoretical model with self-interest pursuing consumers is introduced in order to assess how to design a least-cost distribution tariff under two constraints that regulators typically face. The first constraint is related to difficulties regarding the implementation of cost-reflective tariffs. In practice, so-called cost-reflective tariffs are only a proxy for the actual cost driver(s) in distribution grids. The second constraint has to do with fairness. There is a fear that active consumers investing in distributed energy resources (DER) might benefit at the expense of passive consumers. We find that both constraints have a significant impact on the least-cost network tariff design, and the results depend on the state of the grid. If most of the grid investments still have to be made, passive and active consumers can both benefit from cost-reflective tariffs, while this is not the case for passive consumers if the costs are mostly sunk.