Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/31313
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: A fuzzy genetic algorithm for driver scheduling
Author(s): Li, Jingpeng
Kwan, Raymond S K
Contact Email: jli@cs.stir.ac.uk
Keywords: Fuzzy sets
Genetic algorithms
Driver scheduling
Issue Date: 1-Jun-2003
Citation: Li J & Kwan RSK (2003) A fuzzy genetic algorithm for driver scheduling. European Journal of Operational Research, 147 (2), pp. 334-344. https://doi.org/10.1016/s0377-2217%2802%2900564-7
Abstract: This paper presents a hybrid genetic algorithm (GA) for the bi-objective public transport driver scheduling problem. A greedy heuristic is used, which constructs a schedule by sequentially selecting shifts, from a very large set of pre-generated legal potential shifts, to cover the remaining work. Individual shifts and the schedule as a whole have to be evaluated in the process. Fuzzy set theory is applied on such evaluations. For individual shifts, their structural efficiency is assessed by fuzzified criteria identified from practical knowledge of the problem domain. A GA is used to derive a near-optimal weight distribution amongst the fuzzified criteria, so that a single-valued weighted evaluation can be computed for each shift. The corresponding schedule constructed utilising the weight distribution is evaluated by the GA’s fitness function, in which the two objectives of minimising the number of shifts and minimising the total cost are formulated as a fuzzy goal. Comparative results on real-world problems are presented.
DOI Link: 10.1016/s0377-2217(02)00564-7
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