Decision support system for green real-life field scheduling problems.pdf (795.97 kB)
Decision support system for green real-life field scheduling problems
conference contribution
posted on 2018-02-19, 11:23 authored by Yizi Zhou, Anne Liret, Jiyin LiuJiyin Liu, Emmanuel Ferreyra, Rupal MandaniaRupal Mandania, Mathias Kern© 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.
History
School
- Business and Economics
Department
- Business
Published in
Artificial Intelligence XXXIV. 37th SGAI International Conference on Artificial Intelligence Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Volume
10630 LNAIPages
355 - 369Citation
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.Publisher
© SpringerVersion
- AM (Accepted Manuscript)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Publication date
2017Notes
The final authenticated version is available online at https://doi.org/10.1007/978-3-319-71078-5_30.ISBN
9783319710778;9783319710785ISSN
0302-9743eISSN
1611-3349Publisher version
Book series
Lecture Notes in Computer Science;10630Language
- en