Abstract:
Generator scheduling is a
combinatorial optimization problem and this
paper presents a new version of annealing (SA)
method to model and solve the scheduling
problem. Firstly, solution is decomposed into
hourly schedules and each hourly schedule is
modified by decomposed-SA using bits flipping. If
the generated new hourly schedule is better, by
convention it is accepted deterministically. A
worse hourly schedule is accepted with
temperature dependent SA probability. A new
solution consists of these hourly schedules of
entire scheduling period after repair as unit-wise
constraints may not be fulfilled at the time of
individual hourly schedule modification. This
helps to direct and modify schedules of
appropriate hours. Secondly, this new solution is
accepted for the next iteration if its cost is less
than that of current solution. A higher cost new
solution is accepted with temperature dependent
SA probability again. Besides, problem dependent
other features are incorporated to save the
execution time. The proposed method is tested
using the reported problem data sets. Simulation
results are compared to previous reported results.
Numerical results show an improvement in
solution cost and time compared to the results
obtained from powerful algorithms.
