Background: A reasonable management and monitoring of construction projects requires accurate construction schedules. Accuracy depends highly on availability of reliable actual logistics data.
This paper considers the integration of uncertain real-time logistics data for reactive construction scheduling. In order to manage a construction project efficiently, an accurate schedule representing the current project progress is inevitable. The quality and up-to-dateness of such a schedule depends on the availability of real-time data. Typically, real-time logistics data contain information about the availability of material, equipment and personnel as well as delivery dates and site conditions. The accuracy and inherent uncertainty depends on the location where the real-time data was acquired. Currently, the integration of such data into a construction schedule is a very time-consuming, manual and, thus, errorprone process. Therefore, this paper proposes a methodology that enables an automatic integration of such uncertain data into construction schedules. By integrating uncertainties into the existing schedule their impacts on the construction work can be evaluated. For this, discrete event simulation is applied. In order to model uncertain input parameters for simulation models this methodology applies the fuzzy sets theory. In combination with alpha-cut sampling technique, discrete model input parameters are obtained. By applying reactive scheduling with several discrete event simulation experiments, the results can be used to modify construction schedules according to agreed timeframes and costs. In order to demonstrate and validate the presented approach an example is conducted.
-This paper deals with the adaption of construction schedule due to real-time data. This real-time data needs to be evaluated regarding the effects on the schedule. If significant delays or other problems are identified, the schedule should be adapted. That means that under the existing condition a new schedule needs to be generated. Thereby, different constraints of the target schedule like contracted delivery dates, milestones or resource allocation should be considered. In the presented approach these additional constraints are modeled and integrated into a simulation model which represents the planned target schedule. By applying simulationbased optimization a new efficient schedule is generated considering existing and additional so-called target constraints. If the identified delays are that large, such that an adaption considering all significant constraints is not possible, some constraints, like resource capacity or shifts, can be relaxed. The proposed concept for reactive adaptation of construction schedules by applying simulation-based optimization is verified by a standard construction schedule example.
An efficient execution of complex construction projects requires a comprehensive scheduling of all construction activities. For this, it is necessary to consider various restrictions and the availability of required resources. The generation of efficient schedules is a very challenging task, in fact an NP-hard optimization problem. An appropriate approach is the application of discrete-event simulation for the generation of valid schedules. Additionally, simulation is combined with optimization methods to determine efficient schedules regarding given objectives. The applied optimization techniques are evolutionary algorithms. Thus, operators have to be implemented that define the way of generating new schedules in the recombination step of the algorithm. In this paper an improved operator is presented that outperforms common operators for scheduling problems by considering technological dependencies between activities, so that it will be possible to determine efficient schedules for complex construction scheduling problems. An example of implementation is presented to validate the developed operator. INTRODUCTIONThe efficient execution of construction activities is absolutely essential for today's construction industry. A multitude of construction tasks has to be scheduled efficiently regarding different conflicting objectives like time, cost and quality. However, various constraints like resource restrictions or precedence relations have to be taken into consideration. For that reason, scheduling problems belong to the class of combinatorial optimization problems and the generation of efficient schedules for complex construction projects results in an NP-hard optimization problem. Thus, an analytical calculation of optimal schedules with exact mathematical methods is impractical.In the paper at hand, an approach is presented where discrete-event simulation is used for the generation of valid schedules for construction projects. By implementing the constraint satisfaction approach within a discrete-event simulation framework, it is possible to generate schedules for highly complex projects under the consideration of all existing constraints in a relatively short amount of time. To enable the determination of efficient schedules, an existing optimization framework is adapted for the use in combinatorial optimization. The implemented optimization strategy is an evolutionary algorithm. Thus, several steps of the algorithm have to be defined, e.g. the recombination of selected solutions. We present a rankbased crossover operator, which has advantages over existing operators concerning the properties of generated solutions. In the paper at hand, this rank-based crossover operator is described in detail, the optimization framework is presented, and the efficiency of the developed operator is shown by a comprehensive case study.978-1-4673-4781-5/12/$31.00 ©2012 IEEE
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