Improved Squeaky Wheel Optimisation for Driver Scheduling

TL;DR

This paper introduces an improved version of Squeaky Wheel Optimisation for driver scheduling that enhances effectiveness and speed through evolutionary steps, iterative repair, and experimental validation.

Contribution

The paper proposes a novel Improved Squeaky Wheel Optimisation method incorporating Selection and Mutation steps for better driver scheduling solutions.

Findings

Enhanced optimization speed and effectiveness.

Successful application to driver scheduling problems.

Encouraging experimental results.

Abstract

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.