Time-dependent vehicle routing problem with time windows of city logistics with a congestion avoidance approach

Changshi, Liu; Kou, Gang; Zhou, Xiancheng; Peng, Yi; Sheng, Huyi; Alsaadi, Fawaz E.

doi:10.1016/j.knosys.2019.06.021

Cited by 97 publications

(56 citation statements)

References 43 publications

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“…Through the overall optimization of facility locations, quantities and capacities, imbalances between facility resources and customer needs can be reduced. The location and demand of demand points are known, and each demand point is met by only one vehicle [56], [57]. Vehicles also have capacity limitations, with only one vehicle on each itinerant routing, and each vehicle needs to return to the distribution center after completing the distribution task.…”

Section: Methodology a Problem Descriptionmentioning

confidence: 99%

Robust Optimization of a Distribution Network Location-Routing Problem Under Carbon Trading Policies

Zhou

et al. 2020

IEEE Access

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Taking carbon emissions into account in decision-making on distribution network operations contributes to achieving the goal of promoting energy conservation and emissions reduction. The focus of this paper is to research multicapacity hierarchical location-routing robust optimization in distribution network design under carbon trading policies. First, this problem is described as a mixed integer nonlinear programming model. Then, based on strong duality theory, the nonlinear model is transformed into a linear robust equivalent model. Finally, GUROBI software is used for numerical calculation and analysis. The results suggest the following: carbon trading policies have a carbon abatement effect; with a decrease in the carbon emissions cap and an increase in carbon trading prices, carbon emissions undergo a ladder-like downward trend; uncertain fluctuations in freight units will influence the optimal decision-making patterns of enterprises; and making more vehicles available will reduce carbon emissions. The government should set a reasonable carbon emissions cap according to market conditions. Enterprises could adopt robust control parameters on the basis of their decision-making preferences and consider the impact of carbon trading policy in formulating and adjusting an optimal decision-making scheme. INDEX TERMS Carbon emissions, carbon trading, green location-routing problem, robust optimization.

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Section: Methodology a Problem Descriptionmentioning

confidence: 99%

Robust Optimization of a Distribution Network Location-Routing Problem Under Carbon Trading Policies

Zhou

et al. 2020

IEEE Access

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“…For i = 1: N, whether S r (i) = 1 and node i is triggered by adjacent node j. If so, then update all arc radii of the ripple i in the remaining time according to formulas (8) and (9). Let F r (i) = j and S r (i) = 2.…”

Section: Static Delivery Sequence Determination Testsmentioning

confidence: 99%

“…Due to the boosting number of vehicles in urban networks, regular traffic congestion has been a common phenomenon that imposes some negative impacts on social development and daily life [9]. Especially for the logistics industry, several inevitable challenges are brought to impede the further research of the VRP and its multiple variants.…”

Section: Introductionmentioning

confidence: 99%

“…How to effectively solve VRP plays a considerable role in minimizing delivery costs and ecological environment protection [10]. For example, the daily traffic congestions may cause huge uncertainties for vehicle route planning, when the vehicles intend to travel across a planned path with shorter length but will waste a long time waiting at the blocked areas [9]. e fuel consumption highly depends on the length and travelling time of the route.…”

Section: Introductionmentioning

confidence: 99%

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A Fissile Ripple Spreading Algorithm to Solve Time-Dependent Vehicle Routing Problem via Coevolutionary Path Optimization

2020

Journal of Advanced Transportation

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The time-dependent vehicle routing problems have lately received great attention for logistics companies due to their crucial roles in reducing the time and economic costs, as well as fuel consumption and carbon emissions. However, the dynamic routing environment and traffic congestions have made it challenging to make the actual travelling trajectory optimal during the delivery process. To overcome this challenge, this study proposed an unconventional path optimization approach, fissile ripple spreading algorithm (FRSA), which is based on the advanced structure of coevolutionary path optimization (CEPO). The objective of the proposed model is to minimize the travelling time and path length of the vehicle, which are the popular indicators in path optimization. Some significant factors usually ignored in other research are considered in this study, such as congestion evolution, routing environment dynamics, signal control, and the complicated correlation between delivery sequence and the shortest path. The effectiveness of the proposed approach was demonstrated well in two sets of simulated experiments. The results prove that the proposed FRSA can scientifically find out the optimal delivery trajectory in a single run via global research, effectively avoid traffic congestion, and decrease the total delivery costs. This finding paves a new way to explore a promising methodology for addressing the delivery sequence and the shortest path problems at the same time. This study can provide theoretical support for the practical application in logistics delivery.

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“…Such regular phenomenon can be predicted by analyzing daily traffic data. By means of valid stochastic information, a vehicle routing scheme considering varying travelling times is devised to avert unnecessary traffic jams, which refers to time-dependent vehicle routing problem (TDVRP) [3]. Still and all, certain unexpected disruptive events may cause troubles for vehicle travelling en route, such as terrible traffic block, vehicle breakdown, which would result in enormous transportation delay leading the initial routing plan no longer optimal or even infeasible.…”

Section: Introductionmentioning

confidence: 99%

A Disruption Recovery Model for Time-Dependent Vehicle Routing Problem With Time Windows in Delivering Perishable Goods

Bin

Zhou

2020

IEEE Access

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In delivering perishable goods, certain unexpected disruptive events may cause the initial routing scheme to be infeasible. A new routing scheme must be generated quickly to alleviate delivery disturbances. According to the idea of disruption management, a disruption recovery model with a distinctive type of split delivery is developed for inter-route recourse based on an initial time-dependent vehicle routing model with time windows, which synthesizes the perishable nature of delivered goods and dynamic travel route choice in urban road networks. Then, a tabu search algorithm is proposed to solve the initial routing problem and further extended to generate the disruption recovery plan. Three computational experiments on the instances adapted from Solomon's and Gehring and Homberger's benchmark problems are conducted to illustrate the effectiveness of the proposed model and algorithm. INDEX TERMS Vehicle routing, disruption management, time-dependent, time windows, split delivery.

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Time-dependent vehicle routing problem with time windows of city logistics with a congestion avoidance approach

Cited by 97 publications

References 43 publications

Robust Optimization of a Distribution Network Location-Routing Problem Under Carbon Trading Policies

Robust Optimization of a Distribution Network Location-Routing Problem Under Carbon Trading Policies

A Fissile Ripple Spreading Algorithm to Solve Time-Dependent Vehicle Routing Problem via Coevolutionary Path Optimization

A Disruption Recovery Model for Time-Dependent Vehicle Routing Problem With Time Windows in Delivering Perishable Goods

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