The Time Window Assignment Vehicle Routing Problem with Time-Dependent Travel Times

Aiming at the multi-depot heterogeneous vehicle routing problem under the time-dependent road network and soft time window, considering vehicle fixed cost, time window penalty cost and vehicle transportation cost, an optimization model of time-dependent multi-depot heterogeneous vehicle routing problem is established with the objective of minimizing distribution cost. According to the characteristics of the problem, a hybrid genetic algorithm with variable neighborhood search considering the temporal–spatial distance is designed. Customers are clustered according to the temporal–spatial distance to generate initial solutions, which improves the quality of the algorithm. The depth search capability of the variable neighborhood search algorithm is applied to the local search strategy of the genetic algorithm to enhance the local search capability of the algorithm. An adaptive neighborhood search number strategy and a new acceptance mechanism of simulated annealing are proposed to balance the breadth and depth required for population evolution. The validity of the model and algorithm is verified by several sets of examples of different scales. The research results not only deepen and expand the relevant research on vehicle routing problem, but also provide theoretical basis for logistics enterprises to optimize distribution scheme.

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A model for the time dependent vehicle routing problem with time windows under traffic conditions with intelligent travel times

RAIRO - Operations Research ◽

10.1051/ro/2021098 ◽

2021 ◽

Author(s):

Saeed Khanchehzarrin ◽

Maral Shahmizad ◽

Iraj Mahdavi ◽

Nezam Mahdavi-Amiri ◽

Peiman Ghasemi

Keyword(s):

Travel Time ◽

Vehicle Routing ◽

Vehicle Routing Problem ◽

Programming Model ◽

Time Windows ◽

Time Dependent ◽

Mixed Integer ◽

Travel Times ◽

Routing Problem ◽

Traffic Conditions

A new mixed-integer nonlinear programming model is presented for the time-dependent vehicle routing problem with time windows and intelligent travel times. The aim is to minimize fixed and variable costs, with the assumption that the travel time between any two nodes depends on traffic conditions and is considered to be a function of vehicle departure time. Depending on working hours, the route between any two nodes has a unique traffic parameter. We consider each working day to be divided into several equal and large intervals, termed as a scenario. Here, allowing for long distances between some of the nodes, travel time may take more than one scenario, resulting in resetting the scenario at the start of each large interval. This repetition of scenarios has been used in modeling and calculating travel time. A tabu search optimization algorithm is devised for solving large problems. Also, after linearization, a number of random instances are generated and solved by the CPLEX solver of GAMS to assess the effectiveness of our proposed algorithm. Results indicate that the initial travel time is estimated appropriately and updated properly in accordance with to the repeating traffic conditions.

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Dynamic Vehicle Routing Problem with real-time time-dependent travel times

2009 IEEE Intelligent Vehicles Symposium ◽

10.1109/ivs.2009.5164383 ◽

2009 ◽

Cited By ~ 1

Author(s):

Xin Zhao ◽

Gilles Goncalves ◽

Remy Dupas

Keyword(s):

Vehicle Routing ◽

Real Time ◽

Vehicle Routing Problem ◽

Time Dependent ◽

Travel Times ◽

Dynamic Vehicle Routing ◽

Dynamic Vehicle Routing Problem ◽

Routing Problem

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The capacitated vehicle routing problem with soft time windows and stochastic travel times

Revista Facultad de Ingeniería ◽

10.19053/01211129.v28.n50.2019.8782 ◽

2019 ◽

Vol 28 (50) ◽

pp. 19-33

Author(s):

Jorge Oyola

Keyword(s):

Vehicle Routing ◽

Vehicle Routing Problem ◽

Time Window ◽

Time Windows ◽

Neighborhood Search ◽

Travel Times ◽

Routing Problem ◽

Solution Approach ◽

Capacitated Vehicle Routing Problem ◽

Capacitated Vehicle

A full multiobjective approach is employed in this paper to deal with a stochastic multiobjective capacitated vehicle routing problem (CVRP). In this version of the problem, the demand is considered to be deterministic, but the travel times are assumed to be stochastic. A soft time window is tied to every customer and there is a penalty for starting the service outside the time window. Two objectives are minimized, the total length and the time window penalty. The suggested solution method includes a non-dominated sorting genetic algorithm (NSGA) together with a variable neighborhood search (VNS) heuristic. It was tested on instances from the literature and compared to a previous solution approach. The suggested method is able to find solutions that dominate some of the previously best known stochastic multiobjective CVRP solutions.

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