Authors: Yindong Shen; Jingpeng Li; Kunkun Peng
Addresses: Key Laboratory of Image Processing and Intelligent Control of Education Ministry, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China ' Division of Computer Science and Mathematics, University of Stirling, Stirling FK9 4LA, UK ' Key Laboratory of Image Processing and Intelligent Control of Education Ministry, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Abstract: Public transport driver scheduling is a process of selecting a set of duties for the drivers of vehicles to form a number of legal driver shifts. The problem usually has two objectives which are minimising both the total number of shifts and the total shift cost, while taking into account some constraints related to labour and company rules. A commonly used approach is firstly to generate a large set of feasible shifts by domain-specific heuristics, and then to select a subset to form the final schedule by an integer programming method. This paper presents an estimation of distribution algorithm (EDA) to deal with the subset selection problem which is NP-hard. To obtain a candidate schedules, the EDA applies a number of rules, with each rule corresponding to a particular way of selecting a shift. Computational results from some real-world instances of drive scheduling demonstrate the availability of this approach.
Keywords: metaheuristics; estimation of distribution algorithm; EDA; Bayesian networks; driver scheduling; public transport; legal driver shifts.
International Journal of Operational Research, 2017 Vol.28 No.2, pp.245 - 262
Received: 04 Aug 2014
Accepted: 28 Jun 2015
Published online: 02 Jan 2017 *