Modern heuristic techniques for combinatorial problems
A genetic algorithm for public transport driver scheduling
Computers and Operations Research - Special issue on genetic algorithms
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
Solving Crew Scheduling Problems bu Constraint Programming
CP '95 Proceedings of the First International Conference on Principles and Practice of Constraint Programming
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
A Self-Adjusting Algorithm for Driver Scheduling
Journal of Heuristics
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 problems are well known to be NP-hard. Although some mathematically based methods are being used in the transport industry, there is room for improvement. A hybrid approach incorporating a genetic algorithm (GA) is presented. The role of the GA is to derive a small selection of good shifts to seed a greedy schedule construction heuristic. A group of shifts called a relief chain is identified and recorded. The relief chain is then inherited by the offspring and used by the GA for schedule construction. The new approach has been tested using real-life data sets, some of which represent very large problem instances. The results are generally better than those compiled by experienced schedulers and are comparable to solutions found by integer linear programming (ILP). In some cases, solutions were obtained when the ILP failed within practical computational limits.