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Raymond S. K. Kwan
Researcher at University of Leeds
Publications - 53
Citations - 885
Raymond S. K. Kwan is an academic researcher from University of Leeds. The author has contributed to research in topics: Scheduling (production processes) & Job shop scheduling. The author has an hindex of 17, co-authored 52 publications receiving 829 citations.
Papers
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Journal ArticleDOI
A fuzzy genetic algorithm for driver scheduling
Jingpeng Li,Raymond S. K. Kwan +1 more
TL;DR: A hybrid genetic algorithm (GA) is presented for the bi-objective public transport driver scheduling problem, which constructs a schedule by sequentially selecting shifts from a very large set of pre-generated legal potential shifts to cover the remaining work.
Book ChapterDOI
Distributed choice function hyper-heuristics for timetabling and scheduling
TL;DR: This paper investigates an emerging class of search algorithms, in which high-level domain independent heuristics, called hyper-heuristics), iteratively select and execute a set of application specific but simple search moves, called low-level heuristic, working toward achieving improved or even optimal solutions.
Book ChapterDOI
Tabu Search for Driver Scheduling
Yindong Shen,Raymond S. K. Kwan +1 more
TL;DR: In this article, a Tabu Search heuristic for multi-neighbourhoods and an appropriate memory scheme have been designed and tailored for the driver scheduling problem, which is known to be NP-hard Multi-Neighborhoods.
Journal ArticleDOI
A flexible system for scheduling drivers
TL;DR: This paper shows for the first time how theory and practice have been brought together, explaining the many features which have been added to the algorithmic kernel to provide a user-friendly and adaptable system designed to provide maximum flexibility in practice.
Book ChapterDOI
Driver Scheduling Using Genetic Algorithms with Embedded Combinatorial Traits
TL;DR: There is scope for a Genetic Algorithm approach, which is described in this paper, to make improvements in terms of computational efficiency, robustness, and capability to tackle large data sets.