6 research outputs found
Improved Squeaky Wheel Optimisation for Driver Scheduling
This paper presents a technique called Improved Squeaky Wheel Optimisation
for driver scheduling problems. It improves the original Squeaky Wheel
Optimisations effectiveness and execution speed by incorporating two additional
steps of Selection and Mutation which implement evolution within a single
solution. In the ISWO, a cycle of
Analysis-Selection-Mutation-Prioritization-Construction continues until
stopping conditions are reached. The Analysis step first computes the fitness
of a current solution to identify troublesome components. The Selection step
then discards these troublesome components probabilistically by using the
fitness measure, and the Mutation step follows to further discard a small
number of components at random. After the above steps, an input solution
becomes partial and thus the resulting partial solution needs to be repaired.
The repair is carried out by using the Prioritization step to first produce
priorities that determine an order by which the following Construction step
then schedules the remaining components. Therefore, the optimisation in the
ISWO is achieved by solution disruption, iterative improvement and an iterative
constructive repair process performed. Encouraging experimental results are
reported
The General Yard Allocation Problem
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)27241986-199