Using Group Role Assignment to Solve Dynamic Vehicle Routing Problem

Lei, Bo; Zhu, Haibin; Gningue, Youssou

doi:10.1109/icnsc.2019.8743322

Cited by 3 publications

(2 citation statements)

References 81 publications

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“…Similarly, dynamic vehicle routing aims to minimize the cost of serving customers dispersed across various locations by updating vehicle routes as new information is received [52][53][54][55]. To address these issues, the technique of re-optimization can be employed [49,56,57], which breaks down the dynamic issue into a set of static problems and generates an initial schedule or route, then re-optimizes it at two scenarios: fixed time intervals [58][59][60][61] or real-time response to new events happens, known as event-driven re-optimization [50,52,57,62]. The time-slice method's limitation in effectively managing urgent dynamic events is due to its inability to promptly assimilate new information; thus, its utility is restricted to less timesensitive issues, including customer requests that do not have a narrow time frame [56].…”

Section: Dynamic Scheduling Problems and Dynamic Vehicle-routing Prob...mentioning

confidence: 99%

Enhancing Buildings’ Energy Resilience by Dynamic Seismic Emergency Inspection and Restoration Scheduling in Multiple Systems

Zhang,

Li,

Chen

et al. 2023

Buildings

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Buildings’ energy resilience in natural disasters is reliant on the support of the functionalities of critical infrastructure that the buildings connect to, such as highway-bridge and electric power systems. Meanwhile, as critical infrastructure systems have increasingly become interconnected and interdependent, they are more susceptible to natural hazards and less able to withstand their effects. Insufficient research has been conducted regarding computational models of effectively representing the interdependencies and interactions involved in the restoration scheduling of post-disaster critical infrastructure systems. To address this research gap, this study proposes integer programs, integrating hybrid genetic algorithms, to explicitly investigate the impact of interactions and interdependencies between electric power systems (EPSs) and highway-bridge systems (HBSs) on the energy-recovery processes of buildings. The objective is to dynamically prioritize the restoration scheduling for EPSs and HBSs while considering inspection and restoration activities. A case study based on the 2008 Wenchuan Earthquake in Sichuan province, China, is employed to validate the efficacy of the proposed method. The results of the analysis reveal that the dynamic model exhibits a substantial 6.4% improvement in building energy resilience at the seven-day mark, compared to the static model. Moreover, the proposed coupled EPS–HBS inspection–restoration joint model outperforms a disjoint EPS inspection–restoration scheduling model, yielding a remarkable 11.4% enhancement in system resilience at the seven-day mark. These findings underscore the significance of considering interdependencies and interactions within critical infrastructure systems to enhance the energy resilience of buildings in earthquake-affected areas.

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Section: Dynamic Scheduling Problems and Dynamic Vehicle-routing Prob...mentioning

confidence: 99%

Enhancing Buildings’ Energy Resilience by Dynamic Seismic Emergency Inspection and Restoration Scheduling in Multiple Systems

Zhang,

Li,

Chen

et al. 2023

Buildings

View full text Add to dashboard Cite

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“…Here data of travel is required at least 1 day before to design the routes and system cannot handle passenger who decides to travel at immediate. On the other hand [3] stating the complexity of Dynamic Route planning introduces new challenges while judging the merit of given dynamic route. The paper proposes O(m 3 ) to O(m 4 ) algorithm which is acceptable only for smaller net-work and fewer number of requests.…”

Section: Motivation and Related Workmentioning

confidence: 99%

Parivahan: Passenger Demand Triggered Bus (Vahan) Routing in Intelligent Public Transport System

Ladhai¹,

Chaubey²

2022

INDJCSE

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Daily commuters using the Public Transport System (PTS) have a different experience in ComfortPerception (CP) depending on the time of day. The pick-time CP value is not good, compared to rest time. This problem raised in PTS is due to the fixed-route strategy. The issue here is addressed by implementing demand based dynamic routing approach using the SUMO tool noting service time, request allocated per vehicle, average waiting time, and average travel time over variously categorized network topology. It was observed that dynamic routing reduces the travel time of passengers from 5.3mins to under 5mins. This reduction here is achieved with a little overhead of computational time as low as 14.37ms/request. The result shows that the ratio of average waiting time to average travel time is up to 0.5 for smaller network and decreases with an increase in network size and well connected-network.

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