Integrated production and multiple trips vehicle routing with time windows and uncertain travel times

2019 ◽  
Vol 103 ◽  
pp. 1-12 ◽  
Author(s):  
Dujuan Wang ◽  
Jiaqi Zhu ◽  
Xiaowen Wei ◽  
T.C.E. Cheng ◽  
Yunqiang Yin ◽  
...  
Keyword(s):  
Vehicle Routing ◽  
Time Windows ◽  
Travel Times ◽  
Integrated Production

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 On the stochastic vehicle routing problem with time windows, correlated travel times, and time dependency

4OR ◽  
2021 ◽  
Author(s):  
Federica Bomboi ◽  
Christoph Buchheim ◽  
Jonas Pruente
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Adaptive Sampling ◽  
Time Windows ◽  
Chance Constraint ◽  
Travel Times ◽  
Routing Problem ◽  
Stochastic Vehicle Routing ◽  
Time Dependencies ◽  
Feasibility Test

AbstractMost state-of-the-art algorithms for the Vehicle Routing Problem, such as Branch-and-Price algorithms or meta heuristics, rely on a fast feasibility test for a given route. We devise the first approach to approximately check feasibility in the Stochastic Vehicle Routing Problem with time windows, where travel times are correlated and depend on the time of the day. Assuming jointly normally distributed travel times, we use a chance constraint approach to model feasibility, where two different application scenarios are considered, depending on whether missing a customer makes the rest of the route infeasible or not. The former case may arise, e.g., in drayage applications or in the pickup-and-delivery VRP. In addition, we present an adaptive sampling algorithm that is tailored for our setting and is much faster than standard sampling techniques. We use a case study for both scenarios, based on instances with realistic travel times, to illustrate that taking correlations and time dependencies into account significantly improves the quality of the solutions, i.e., the precision of the feasibility decision. In particular, the nonconsideration of correlations often leads to solutions containing infeasible routes.

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