Abstract: Concurrent planning of the resource constraint project scheduling and material ordering problems have received significant attention within the last decades. Hence, the issue has been investigated here with the aim to minimize total project costs. Furthermore, the presented model considers different discount options in order to approach the real world conditions. The incorporated alternatives consist of all-unit and incremental discount strategies. On the other hand, a modified version of the genetic algorithm is applied in order to solve the model for larger sizes, in particular. Finally, the applicability and efficiency of the given model is tested by different numerical instances.
Abstract: Concurrent planning of project scheduling and
material ordering has been increasingly addressed within last decades
as an approach to improve the project execution costs. Therefore, we
have taken the problem into consideration in this paper, aiming to
maximize schedules quality robustness, in addition to minimize the
relevant costs. In this regard, a bi-objective mathematical model is
developed to formulate the problem. Moreover, it is possible to
utilize the all-unit discount for materials purchasing. The problem is
then solved by the E-constraint method, and the Pareto front is
obtained for a variety of robustness values. The applicability and
efficiency of the proposed model is tested by different numerical
instances, finally.
Abstract: Concurrent planning of project scheduling and
material ordering can provide more flexibility to the project
scheduling problem, as the project execution costs can be enhanced.
Hence, the issue has been taken into account in this paper. To do so, a
mixed-integer mathematical model is developed which considers the
aforementioned flexibility, in addition to the materials quantity
discount and space availability restrictions. Moreover, the activities
duration has been treated as decision variables. Finally, the efficiency
of the proposed model is tested by different instances. Additionally,
the influence of the aforementioned parameters is investigated on the
model performance.