| dc.description.abstract | ABSTRACT
Project scheduling plays a vital role in project management, and constitutes
one of the most important directions in both research and practice in the
Operational Research (OR) field. During the last decades, the Resource-
Constrained Project Scheduling Problem (RCPSP) has become the most
challenging standard problem of project scheduling in the OR literature. The
RCPSP involves the construction of a precedence and resource feasible time
schedule which identifies the starting and completion times of activities, under
a specific objective. Several variations of the RCPSP exist that represent
different practical problems with different objectives, resource types, more
than one way (mode) to execute an activity, generalised precedence relations
for activities, etc. The RCPSP and its variants belong to the class of strongly
NP-hard problems and a number of solution methods, both exact and
approximate have been proposed in the literature.
Scheduling is also a critical issue in process operations. The process
scheduling problem consists of determining the most efficient way to produce
a set of products in a time horizon given a set of processing recipes and
limited resources. The activities to be scheduled usually take place in
multiproduct and multipurpose plants, in which a wide variety of different
products can be manufactured via the same recipe or different recipes by
sharing limited resources, such as equipment, material, time, and utilities.
The common problem features, such as required resource types, precedence
relations and initial/target inventories, suggest that exchanging solution
techniques between the two research fields is both possible and useful.
The process scheduling industry is driven by the substantial advances of
related modelling and solution techniques, as well as the rapidly growing
computational power. On the other hand, project scheduling research effort
has mostly focused on developing approximate solution techniques. However,
recent project scheduling research papers show a renewed interest for
mathematical programming-based solution strategies. Moreover, the best
lower bounds ever found on broadly-studied RCPSP test instances, were
obtained by a hybrid method involving constraint propagation and a MILP
formulation. Additionally, mathematical programming solvers are often the
only software available to industrial practitioners. Therefore, the study of
exact methods, and especially mathematical programming techniques for
solving the RCPSP is of particular theoretical and practical interest. The main
objective of this work is to develop new optimal project scheduling techniques
inspired by the process scheduling literature.
This thesis consists of a literature review and state-of-the-art, three chapters
with novel mathematical programming solution methods for the RCPSP and its
variants under the objective of minimising the makespan and finally some
concluding remarks. The first part presents new mixed-integer linear
programming models for the deterministic single- and multi-mode RCPSP with
renewable and non-renewable resources. The modelling approach relies on
the Resource-Task Network (RTN) representation, a network representation
technique used in process scheduling problems, based on continuous time
models. Next, two new binary integer programming discrete-time models and
two novel precedence-based mixed integer continuous-time formulations are
developed. These four novel mathematical formulations are compared with
four state-of-the-art models from the open literature using a total number of
2760 well-known open-accessed benchmark problem instances. The
computational comparison demonstrates that the proposed mathematical
formulations feature the best overall performance. Finally, a new precedencebased
continuous-time formulation is proposed for a challenging extension of
the standard single-mode resource-constrained project scheduling problem
that also considers minimum and maximum time lags (RCPSP/max). The new
formulation is then used to conduct an extensive computational study on a
total of 2,250 benchmark problems, which illustrates its efficient performance. | en_US |
