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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/31882

Title: Decision support system for green real-life field scheduling problems
Authors: Zhou, Yizi
Liret, Anne
Liu, Jiyin
Ferreyra, Emmanuel
Rana, Rupal
Kern, Mathias
Keywords: Green logistic scheduling system
Speed profile
Metaheuristics comparison
Heat map
Issue Date: 2017
Publisher: © Springer
Citation: ZHOU, Y. ...et al., 2017. Decision support system for green real-life field scheduling problems. IN: Bramer, M. and Petridis, M. (eds.) Artificial Intelligence XXXIV: 37th SGAI International Conference on Artificial Intelligence (AI 2017), Cambridge, UK, December 12-14, 2017, Proceedings. Chaim: Springer, pp. 355-369.
Series/Report no.: Lecture Notes in Computer Science;10630
Abstract: © Springer International Publishing AG 2017. A decision support system is designed in this paper for supporting the adoption of green logistics within scheduling problems, and applied to real-life services cases. In comparison to other green logistics models, this system deploys time-varying travel speeds instead of a constant speed, which is important for calculating the CO 2 emission accurately. This system adopts widely used instantaneous emission models in literature which can predict second-by-second emissions. The factors influencing emissions in these models are vehicle types, vehicle load and traffic conditions. As vehicle types play an important role in computing the amount of emissions, engineers’ vehicles’ number plates are mapped to specified emission formulas. This feature currently is not offered by any commercial software. To visualise the emissions of a planned route, a Heat Map view is proposed. Furthermore, the differences between minimising CO 2 emission compared to minimising travel time are discussed under different scenarios. The field scheduling problem is formulated as a vehicle routing and scheduling problem, which considers CO 2 emissions in the objective function, heterogeneous fleet, time window constraints and skill matching constraints, different from the traditional time-dependent VSRP formulation. In the scheduler, this problem is solved by metaheuristic methods. Three different metaheuristics are compared. They are Tabu search algorithms with random neighbourhood generators and two variants of Variable Neighbourhood search algorithms: variable neighbourhood descent (VND) and reduced variable neighbourhood search (RVNS). Results suggest that RVNS is a good trade-off between solution qualities and computational time for industrial application.
Description: The final authenticated version is available online at https://doi.org/10.1007/978-3-319-71078-5_30.
Version: Accepted for publication
DOI: 10.1007/978-3-319-71078-5_30
URI: https://dspace.lboro.ac.uk/2134/31882
Publisher Link: https://doi.org/10.1007/978-3-319-71078-5_30
ISBN: 9783319710778
ISSN: 0302-9743
Appears in Collections:Conference Papers and Presentations (Business)

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