Coordinating assignment and routing decisions in transit vehicle schedules: A variable-splitting Lagrangian decomposition approach for solution symmetry breaking

To comprehensively understand the research progress of the fleet scheduling for hazardous materials, the study has summarized the corresponding research results from three aspects (a) hazardous materials transportation risk, (b) route optimization, and (c) fleet scheduling, and then pointed out potential problems from six aspects: (a) the coupling risk of the transport fleet; (b) the screening of time and space for the transport of hazardous materials; (c) the scheduling optimization for transport fleets; (d) taking insufficient account of transport risks fairness; (e) insufficient robustness of scheduling schemes; and (f) lacking of research results on fleet scheduling of transport in the context of antiterrorism. After that, by considering the existing shortcomings of the current research, five research directions are presented that should be further explored in the future. Subsequently, both rough set and association rule theory is applied to explore the cause chain of transportation accidents for hazardous materials, and analyze the mechanism of transport accident for hazardous materials. Next, the Bayesian network is presented to predict the accident rate of hazardous materials transportation under different temporal and spatial conditions, and the dynamic rolling scheduling method of hazardous materials transport fleet is constructed under normal and antiterrorism background.

Section: Problems In Current Researchmentioning

confidence: 99%

Fleet Scheduling Optimization of Hazardous Materials Transportation: A Literature Review

Zhou

et al. 2020

“…Compared with occasional congestion, the proportion of recurrent congestion is greater but the processes of its formation, propagation, and dissipation have certain rules. In the time dimension, the formation, and dispersion times of congestion o en have the same time window, while in the spatial dimension, the location of congestion, direction of propagation, and range of dissipation have a high degree of similarity [4][5][6][7]. e temporal and spatial evolution of recurrent congestion should be continually evaluated, and this rule should be applied to actively control the tra c ow in a congested area.…”

Section: Introductionmentioning

confidence: 99%

Evolution Regularity Mining and Gating Control Method of Urban Recurrent Traffic Congestion: A Literature Review

Zhou

et al. 2020

To understand the status quo of urban recurrent traffic congestion, the current results of recurrent traffic congestion, and gating control are reviewed from three aspects: traffic congestion identification, evolution trend prediction, and urban road network gating control. Three aspects of current research are highlighted: (a) The majority of current studies are based on statistical analyses of historical data, while congestion identification is performed by acquiring small-scale traffic parameters. Thus, congestion studies on the urban global roadway network are lacking. Situation identification and the failure to effectively warn or even avoid traffic congestion before congestion forms are not addressed; (b) correlation studies on urban roadway network congestion are inadequate, especially regarding deep learning, and considering the space-time correlation for congestion evolution trend prediction; and (c) quantitative research methods, dynamic determination of gating control areas, and effective countermeasures to eliminate traffic congestion are lacking. Regarding the shortcomings of current studies, six research directions that can be further explored in the future are presented.

“…Previous studies typically researched into vehicle scheduling based on a fixed timetable. The usual modeling framework of the VSP is built on a connection-based network, where trips and depots are expressed as vertices, and compatible connections between any two vertices are connected using arcs [6][7][8]. Considering that the connection-based method could bring an immense number of deadhead connection arcs when the number of trips grows, a space-time network modeling concept has been commonly used [8,9].…”

Section: Introductionmentioning

confidence: 99%

“…The usual modeling framework of the VSP is built on a connection-based network, where trips and depots are expressed as vertices, and compatible connections between any two vertices are connected using arcs [6][7][8]. Considering that the connection-based method could bring an immense number of deadhead connection arcs when the number of trips grows, a space-time network modeling concept has been commonly used [8,9]. Bertossi 2 Journal of Advanced Transportation et al [10] have proved that single depot vehicle scheduling problem (SDVSP) can be solved in polynomial time; however multiple depot vehicle scheduling problem (MDVSP) is NPhard even for two depots.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Vehicle Scheduling Based on Variable Timetable by Benders-and-Price Approach

Lan

Song

et al. 2019

In practice, vehicle scheduling is planned on a variable timetable so that the departure times of trips can be shifted in tolerable ranges, rather than on a fixed timetable, to decrease the required fleet size. This paper investigates the vehicle scheduling problem on a variable timetable with the constraint that each vehicle can perform limited trips. Since the connection-based model is difficult to solve by optimization software for a medium-scale or large-scale instance, a designed path-based model is developed. A Benders-and-Price algorithm by combining the Benders decomposition and column generation is proposed to solve the LP-relaxation of the path-based model, and a bespoke Branch-and-Price is used to obtain the integer solution. Numerical experiments indicate that a variable timetable approach can reduce the required fleet size with a tolerable timetable deviation in comparison with a fixed timetable approach. Moreover, the proposed algorithm is greatly superior to GUROBI in terms of computational efficiency and guarantees the quality of the solution.