A genetic algorithm for public transport driver scheduling
Computers and Operations Research - Special issue on genetic algorithms
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Algorithms for railway crew management
Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
Evolutionary Divide and Conquer for the Set-Covering Problem
Selected Papers from AISB Workshop on Evolutionary Computing
A flexible system for scheduling drivers
Journal of Scheduling
Evolutionary Driver Scheduling with Relief Chains
Evolutionary Computation
A fuzzy evolutionary approach with Taguchi parameter setting for the set covering problem
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
An evolutionary squeaky wheel optimization approach to personnel scheduling
IEEE Transactions on Evolutionary Computation
Improved squeaky wheel optimisation for driver scheduling
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
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Public transport driver scheduling is a world wide problem, which is NP-hard. Although some mathematically based methods are being used in the transport industry, there is still much scope for improvements. This paper presents a novel evolutionary approach that simulates the self-adjusting process on a single schedule. Five factors characterized by fuzzy membership functions are first aggregated to evaluate the shift structure. This evaluating function is incorporated into a constructing heuristic to make shift selection. A self-adjusting algorithm is then designed to guide the constructing heuristic to improve a given initial schedule iteratively. In each generation an unfit portion of the working schedule is removed. Broken schedules are repaired by the constructing heuristic until stopping condition is met. Experimental results on real-world driver scheduling problems has demonstrated the success of the proposed approach.