scholarly journals Vehicle Routing Optimisation in Humanitarian Operations: A Survey on Modelling and Optimisation Approaches

2021 ◽  
Vol 11 (2) ◽  
pp. 667
Author(s):  
Wadi Khalid Anuar ◽  
Lai Soon Lee ◽  
Stefan Pickl ◽  
Hsin-Vonn Seow
Keyword(s):  
Vehicle Routing ◽  
Operations Research ◽  
Vehicle Routing Problem ◽  
Critical Role ◽  
Routing Problem ◽  
Solution Approach ◽  
Humanitarian Operations ◽  
Hybrid Solutions ◽  
The World ◽  
Model Characteristic

The growing field of humanitarian operations is driven by frequent events of disasters seen in the world today. Within this field, Operations Research (OR) plays a critical role in alleviating the suffering of victims that are impacted by disasters. This paper focuses on the branch of a well-known OR problem, known as the Vehicle Routing Problem (VRP), within the selected scope of humanitarian operations. A total of 123 papers of the last decade are reviewed and classified under the humanitarian operations of supply and delivery, evacuation as well as rescue operations. Besides specific disaster management phases and disaster types, various modelling challenges are highlighted, hinting towards a richer and more complex VRP seen under selected model characteristic classifications. Furthermore, established solution approaches, including hybrid solutions, are highlighted and classified, discussing how they are applied in the context of these humanitarian operations. The inclusion of a machine learning solution approach under the same classification is proposed. Finally, the trend and future outlook of VRP for the suggested humanitarian operations are discussed and highlighted.

scholarly journals A Solution Approach from an Analytic Model to Heuristic Algorithm for Special Case of Vehicle Routing Problem with Stochastic Demands

2008 ◽  
Vol 2008 ◽  
pp. 1-16
Author(s):  
Selçuk K. İşleyen ◽  
Ö. Faruk Baykoç
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Large Scale ◽  
Iterated Local Search ◽  
Routing Problem ◽  
Solution Approach ◽  
Stochastic Demands ◽  
Expected Length ◽  
The Traveling Salesman Problem ◽  
Special Case

We define a special case for the vehicle routing problem with stochastic demands (SC-VRPSD) where customer demands are normally distributed. We propose a new linear model for computing the expected length of a tour in SC-VRPSD. The proposed model is based on the integration of the “Traveling Salesman Problem” (TSP) and the Assignment Problem. For large-scale problems, we also use an Iterated Local Search (ILS) algorithm in order to reach an effective solution.

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 A path-based solution approach for the Green Vehicle Routing Problem

2019 ◽  
Vol 103 ◽  
pp. 109-122 ◽  
Author(s):  
M. Bruglieri ◽  
S. Mancini ◽  
F. Pezzella ◽  
O. Pisacane
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Routing Problem ◽  
Solution Approach

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.

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