A genetic algorithm for public transport driver scheduling
Computers and Operations Research - Special issue on genetic algorithms
Computer-Aided Scheduling of Public Transport
Computer-Aided Scheduling of Public Transport
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Case-Bases Incorporating Scheduling Constraint Dimensions - Experiences in Nurse Rostering
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
A Tabu-Search Hyperheuristic for Timetabling and Rostering
Journal of Heuristics
An indirect genetic algorithm for a nurse-scheduling problem
Computers and Operations Research
The State of the Art of Nurse Rostering
Journal of Scheduling
A Self-Adjusting Algorithm for Driver Scheduling
Journal of Heuristics
Evolutionary Driver Scheduling with Relief Chains
Evolutionary Computation
Journal of Artificial Intelligence Research
An evolutionary squeaky wheel optimization approach to personnel scheduling
IEEE Transactions on Evolutionary Computation
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
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This paper presents a technique called Improved Squeaky Wheel Optimisation (ISWO) for driver scheduling problems. It improves the original Squeaky Wheel Optimisation's (SWO) 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.