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Incorporating risk in field services operational planning process

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conference contribution
posted on 01.04.2019, 13:10 by Chenlu Ji, Rupal Mandania, Jiyin LiuJiyin Liu, Anne Liret, Mathias Kern
© Springer Nature Switzerland AG 2018. This paper presents a model for the risk minimisation objective in the Stochastic Vehicle Routing Problem (SVRP). In the studied variant of SVRP, service times and travel times are subject to stochastic events, and a time window is constraining the start time for service task. Required skill levels and task priorities increase the complexity of this problem. Most previous research uses a chance-constrained approach to the problem and their objectives are related to traditional routing costs whilst a different approach was taken in this paper. The risk of missing a task is defined as the probability that the technician assigned to the task arrives at the customer site later than the time window. The problem studied in this paper is to generate a schedule that minimises the maximum of risks and sum of risks over all the tasks considering the effect of skill levels and task priorities. The stochastic duration of each task is supposed to follow a known normal distribution. However, the distribution of the start time of the service at a customer site will not be normally distributed due to time window constraints. A method is proposed and tested to approximate the start time distribution as normal. Moreover, a linear model can be obtained assuming identical variance of task durations. Additionally Simulated Annealing method was applied to solve the problem. Results of this work have been applied to an industrial case of SVRP where field engineering individuals drive to customer sites to provide time-constrained services. This original approach gives a robust schedule and allows organisations to pay more attention to increasing customer satisfaction and become more competitive in the market.



  • Business and Economics


  • Business

Published in

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)


11311 LNAI


293 - 307


JI, C. ... et al., 2018. Incorporating risk in field services operational planning process. In: Bramer M. and Petridis M. (eds) Artificial Intelligence XXXV. (SGAI 2018). Cham, Switzerland: Springer, pp. 293 - 307.




AM (Accepted Manuscript)

Publisher statement

The final authenticated version is available online at https://doi.org/10.1007/978-3-030-04191-5_26.

Publication date



This paper was presented at the 38th SGAI International Conference on Artificial Intelligence, AI 2018, Cambridge, UK, December 11–13, 2018.







Book series

Lecture Notes in Computer Science;11311