Service scheduling to minimise the risk of missing appointments

© 2017 IEEE. This paper introduces the risk minimisation objective in the Stochastic Vehicle Routing Problem (SVRP). In the studied variant of SVRP, technicians drive to customer sites to provide service. The service times and travel times are stochastic, and a time window is required for the start of the service for each customer. Most previous research uses a chance-constrained approach to the problem. Some consider the probability of journey duration exceeding the threshold of the driver's workload while others set restrictions on the probability of individual time window constraints being violated. 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 risk and sum of risks of the tasks. The duration of each task may be considered as following 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. Therefore a multiple integral expression of the risk was derived, and this expression works whether task distribution is normal or not. Additionally a deterministic heuristic searching method was applied to solve the problem. Experiments are carried out to test the method. 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 allows organisations to pay more attention to increasing customer satisfaction and become more competitive in the market

Scheduling on-site service deliveries to minimise the risk of missing appointment times

This paper studies the stochastic service task scheduling and vehicle routing problem for a telecommunication provider where each vehicle is driven by an engineer who performs service tasks at customer premises. There is an agreed time window for starting each service task. The service times and travel times are subject to uncertainties, e.g., task taking longer or shorter than expected, traffic situation causing delays. The problem is to schedule the tasks and route the vehicles to minimise the risks of missing appointment times. Models are presented to express the risks and describe the problem. Simulated annealing and tabu search are applied for generating an initial schedule of the day and for re-optimisation during the day based on real-time information updates. The study reported is based on the work in an industrial case. The stochastic nature of the travel times and durations of different task types as well as their distribution parameters have been obtained by applying data analytics on large sets of operations data. These are used in calculating the risks and in making scheduling and routing decisions. Real-time data updates sent back from the engineers are used for re-optimisation to adjust the schedule and routes so that the risks are kept at a lower level. Simulation results show that using risk minimisation as objective and re-optimisation during the day help enhance the on-time start of tasks. With this approach organizations can achieve robust task scheduling and improved customer satisfaction, and so become more competitive in the market