A decision support system for the single-depot vehicle rescheduling problem

Disruption management in vehicle routing and scheduling for road freight transport: a review

“…Computational results are reported to show the effectiveness of the hybrid algorithm. Li et al (2008) provide a further investigation of the case study involving waste collection in Porto Alegre, Brazil that was included in Li et al (2007a). This study concentrates on the rescheduling problem where a trip that has been scheduled is cut due to the breakdown of one of the vehicles.…”

“…The third column lists the main objectives considered in the paper and the final column indicates the solution approach. Li et al (2007a) introduce the Vehicle Rescheduling Problem (VRSP) which seeks to serve the passengers/cargo on the affected trip and complete all remaining trips, while minimising the operation and delay costs. They present a prototype decision support system which recommends solutions for the single-depot vehicle rescheduling problem (SDVRSP) and for the single-depot vehicle scheduling problem (SDVSP).…”

The vehicle rescheduling problem: Model and algorithms

“…Note that these considerations are coherent with the main objective of the experiments, to compare the performance of the implemented algorithms. The procedure to find the backup vehicle candidates, described in Section 4.1, has already been tested in a real life application described in [35]. The new starting time, ST , of the cut trip is the arrival time of a particular backup vehicle at the breakdown point plus a 3− min start-up service time.…”

“…However, in our experiments, the backup trip candidates are generated based on distances and travel times. In order to simplify the experiments and to better compare the algorithms, we assumed that K, the number of backup trips, is such that K ∈ {2, 3,5,10,15,20,25,30,35, 40} for test cases with more than 300 remaining trips. For test cases with 100 and 300 remaining trips, K ∈ {2, 3, 5, 10} and K ∈ {2, 3, 5, 10, 15, 20}, respectively, since smaller-size problems have fewer backup vehicles.…”

A Study on Transportation Algorithm of Bi-Level Logistics Nodes Based on Genetic Algorithm

“…Taking the longest online time of vehicle as the target, it optimized the vehicle path [2]. Li et al (2007) proposed rescheduling the reserve vehicle against the dynamic customer demand and established corresponding mathematical model of scheduling [3]. Wang Liang, Li Shiqiao (2007) have studied the stochastic distribution strategy of demand in the two-stage distribution system, proposed optimized integration of inventory control and transportation decision and established corresponding mathematics, but the driving circuit planning problem is not mentioned [4].…”