Minimax optimisation approach for the Robust Vehicle Routing Problem with Time Windows and uncertain travel times

2011 ◽  
Vol 10 (4) ◽  
pp. 461 ◽  
Author(s):  
Tharinee Manisri ◽  
Anan Mungwattana ◽  
Gerrit K. Janssens
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Travel Times ◽  
Routing Problem

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.

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

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.

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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