Stochastic vehicle routing problem with heterogeneous vehicles and multiple prioritized time windows: Mathematical modeling and solution approach

2019 ◽  
Vol 131 ◽  
pp. 187-199 ◽  
Author(s):  
Vahid Baradaran ◽  
Amir Shafaei ◽  
Amir Hossein Hosseinian
Keyword(s):  
Mathematical Modeling ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Routing Problem ◽  
Solution Approach ◽  
Stochastic Vehicle Routing

The Robust Vehicle Routing Problem with Time Window Assignments

2021 ◽  
Vol 55 (2) ◽  
pp. 395-413
Author(s):  
Maaike Hoogeboom ◽  
Yossiri Adulyasak ◽  
Wout Dullaert ◽  
Patrick Jaillet
Keyword(s):  
Travel Time ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Service Providers ◽  
Time Window ◽  
Time Windows ◽  
Branch And Cut ◽  
Optimal Time ◽  
Routing Problem ◽  
Solution Approach

In practice, there are several applications in which logistics service providers determine the service time windows at the customers, for example, in parcel delivery, retail, and repair services. These companies face uncertain travel times and service times that have to be taken into account when determining the time windows and routes prior to departure. The objective of the proposed robust vehicle routing problem with time window assignments (RVRP-TWA) is to simultaneously determine routes and time window assignments such that the expected travel time and the risk of violating the time windows are minimized. We assume that the travel time probability distributions are not completely known but that some statistics, such as the mean, minimum, and maximum, can be estimated. We extend the robust framework based on the requirements’ violation index, which was originally developed for the case where the specific requirements (time windows) are given as inputs, to the case where they are also part of the decisions. The subproblem of finding the optimal time window assignment for the customers in a given route is shown to be convex, and the subgradients can be derived. The RVRP-TWA is solved by iteratively generating subgradient cuts from the subproblem that are added in a branch-and-cut fashion. Experiments address the performance of the proposed solution approach and examine the trade-off between expected travel time and risk of violating the time windows.

scholarly journals Robust Solution Approach for the Dynamic and Stochastic Vehicle Routing Problem

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Marcella Bernardo ◽  
Jürgen Pannek
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
A Priori ◽  
Stochastic Program ◽  
Higher Moments ◽  
Routing Problem ◽  
Robust Solution ◽  
Solution Approach ◽  
Stochastic Vehicle Routing ◽  
Second Stage

The dynamic and stochastic vehicle routing problem (DSVRP) can be modelled as a stochastic program (SP). In a two-stage SP with recourse model, the first stage minimizes the a priori routing plan cost and the second stage minimizes the cost of corrective actions, performed to deal with changes in the inputs. To deal with the problem, approaches based either on stochastic modelling or on sampling can be applied. Sampling-based methods incorporate stochastic knowledge by generating scenarios set on realizations drawn from distributions. In this paper we proposed a robust solution approach for the capacitated DSVRP based on sampling strategies. We formulated the problem as a two-stage stochastic program model with recourse. In the first stage the a priori routing plan cost is minimized, whereas in the second stage the average of higher moments for the recourse cost calculated via a set of scenarios is minimized. The idea is to include higher moments in the second stage aiming to compute a robust a priori routing plan that minimizes transportation costs while permitting small changes in the demands without changing solution structure. Additionally, the approach allows managers to choose between optimality and robustness, that is, transportation costs and reconfiguration. The computational results on a generic dynamic benchmark dataset show that the robust routing plan can cover unmet demand while incurring little extra costs as compared to the preplanning. We observed that the plan of routes is more robust; that is, not only the expected real cost, but also the increment within the planned cost is lower.

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 Tabu Search for the Vehicle Routing Problem with Multiple Trips, Time Windows, and Simultaneous Delivery-Pickup

2018 ◽  
Vol 19 (2) ◽  
pp. 75
Author(s):  
Suprayogi Suprayogi ◽  
Yusuf Priyandari
Keyword(s):  
Genetic Algorithm ◽  
Tabu Search ◽  
Local Search ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Objective Function Value ◽  
Time Windows ◽  
Routing Problem ◽  
Solution Approach ◽  
Simultaneous Delivery

This paper discusses a vehicle routing problem with multiple trips, time windows, and simultaneous delivery-pickup (VRPMTTWSDP). This problem is a variant of the basic vehicle routing problem (VRP) including the following characteristics: multiple trips, time windows, and simultaneous delivery-pickup.  In this paper, a solution approach based on tabu search (TS) is proposed. In the proposed TS, the sequential insertion (SI) algorithm is used to construct an initial solution. A neighbor structure is generated by applying an operator order consisting of eleven operators of relocation, exchange, and crossover operators. A tabu solution code (TSC) method is applied as a tabu restriction mechanism. Computational experiments are carried out to examine the performance of the proposed TS using hypothetical instances. The performance of the proposed TS is compared to the local search (LS) and the genetic algorithm (GA). The comparison shows that the proposed TS is better in terms of the objective function value.

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.

A Hybrid Genetic Algorithm-Simulated Annealing Approach for the Multi-Objective Vehicle Routing Problem with Time Windows

2011 ◽  
pp. 79-97
Author(s):  
Gülfem Tuzkaya ◽  
Bahadir Gülsün ◽  
Ender Bildik ◽  
E. Gözde Çaglar
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Hybrid Genetic Algorithm ◽  
Benchmark Problems ◽  
Routing Problem ◽  
Solution Approach ◽  
Multi Objective

In this study, the vehicle routing problem with time windows (VRPTW) is investigated and formulated as a multi-objective model. As a solution approach, a hybrid meta-heuristic algorithm is proposed. Proposed algorithm consists of two meta-heuristics: Genetic Algorithm (GA) and Simulated Annealing (SA). In this algorithm, SA is used as an improvement operator in GA. Besides, a hypothetical application is presented to foster the better understanding of the proposed model and algorithm. The validity of the algorithm is tested via some well-known benchmark problems from the literature.

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