2 research outputs found
Optimisation heuristics for solving technician and task scheduling problems
Motivated by an underlying industrial demand, solving intractable technician and task
scheduling problems through the use of heuristic and metaheuristic approaches have
long been an active research area within the academic community. Many solution
methodologies, proposed in the literature, have either been developed to solve a particular
variant of the technician and task scheduling problem or are only appropriate for a
specific scale of the problem. The motivation of this research is to find general-purpose
heuristic approaches that can solve variants of technician and task scheduling problems,
at scale, balancing time efficiency and solution quality. The unique challenges include
finding heuristics that are robust, easily adapted to deal with extra constraints, and
scalable, to solve problems that are indicative of the real world.
The research presented in this thesis describes three heuristic methodologies that
have been designed and implemented: (1) the intelligent decision heuristic (which
considers multiple team configuration scenarios and job allocations simultaneously),
(2) the look ahead heuristic (characterised by its ability to consider the impact of
allocation decisions on subsequent stages of the scheduling process), and (3) the greedy
randomized heuristic (which has a flexible allocation approach and is computationally
efficient).
Datasets used to test the three heuristic methodologies include real world problem
instances, instances from the literature, problem instances extended from the literature
to include extra constraints, and, finally, instances created using a data generator. The
datasets used include a broad array of real world constraints (skill requirements, teaming,
priority, precedence, unavailable days, outsourcing, time windows, and location) on a range of problem sizes (5-2500 jobs) to thoroughly investigate the scalability and
robustness of the heuristics.
The key findings presented are that the constraints a problem features and the size
of the problem heavily influence the design and behaviour of the solution approach
used. The contributions of this research are; benchmark datasets indicative of the
real world in terms of both constraints included and problem size, the data generators
developed which enable the creation of data to investigate certain problem aspects,
mathematical formulation of the multi period technician routing and scheduling problem,
and, finally, the heuristics developed which have proved to be robust and scalable
solution methodologies