Vehicle routing with soft time windows and stochastic travel times: A column generation and branch-and-price solution approach

2014 ◽  
Vol 236 (3) ◽  
pp. 789-799 ◽  
Author(s):  
D. Taş ◽  
M. Gendreau ◽  
N. Dellaert ◽  
T. van Woensel ◽  
A.G. de Kok
Keyword(s):  
Vehicle Routing ◽  
Column Generation ◽  
Time Windows ◽  
Travel Times ◽  
Branch And Price ◽  
Solution Approach ◽  
Soft Time Windows ◽  
Stochastic Travel Times

scholarly journals A Three-Stage Saving-Based Heuristic for Vehicle Routing Problem with Time Windows and Stochastic Travel Times

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Zheng Wang ◽  
Chunyue Zhou
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Travel Times ◽  
Start Time ◽  
Routing Problem ◽  
Problem Transformation ◽  
Basic Ideas ◽  
Two Stages ◽  
Stochastic Travel Times

This paper presents a saving-based heuristic for the vehicle routing problem with time windows and stochastic travel times (VRPTWSTT). One of the basic ideas of the heuristic is to advance the latest service start time of each customer by a certain period of time. In this way, the reserved time can be used to cope with unexpected travel time delay when necessary. Another important idea is to transform the VRPTWSTT to a set of vehicle routing problems with time windows (VRPTW), each of which is defined by a given percentage used to calculate the reserved time for customers. Based on the above two key ideas, a three-stage heuristic that includes the “problem transformation” stage, the “solution construction” stage, and the “solution improvement” stage is developed. After the problem transformation in the first stage, the work of the next two stages is to first construct an initial solution for each transformed VRPTW by improving the idea of the classical Clarke-Wright heuristic and then further improve the solution. Finally, a number of numerical experiments are conducted to evaluate the efficiency of the described methodology under different uncertainty levels.

scholarly journals A Comparison of Robust Criteria for Vehicle Routing Problem with Soft Time Windows

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Thanasak Mouktonglang ◽  
Phannipa Worapun
Keyword(s):  
Vehicle Routing ◽  
Time Windows ◽  
Weather Conditions ◽  
Travel Times ◽  
Worst Case ◽  
Routing Problem ◽  
Robust Solution ◽  
Soft Time Windows ◽  
Relative Robustness ◽  
Problem Instances

In this study, we focus on robust criteria for vehicle routing problems with soft time windows (VRPSTW). The main objective is to find a robust solution that provides the best for the worst case performance for VRPSTW under uncertain travel times. The robust criteria are used in this study such as absolute robustness, robust deviation, and relative robustness as a basis for comparison. The VRPSTW becomes complex when the travel times are uncertain. This uncertainty can be caused by traffic jams, accidents, or inclement weather conditions. The experiment uses benchmarking problems. The number of scenarios is generated randomly into intervals of travel time, equal to 4, 6, and 8 instances for each problem set. Each set of problem instances can be denoted by the percentage of uncertainty α, equal to 0.2, 0.4, 0.6, and 0.8. This study will demonstrate that the most indicated robust criteria for these situations are robust deviation and relative robustness. The most important part of the decision maker is to determine the uncertainty percentage to cover all uncertainties that need to be considered.

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

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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