scholarly journals Getting Beyond the First Result of Solving a Vehicle Routing Problem

2021 ◽  
Author(s):  
John F. Wellington ◽  
Stephen A. Lewis
Keyword(s):  
Vehicle Routing ◽  
Operations Research ◽  
Vehicle Routing Problem ◽  
Solution Method ◽  
Research Management ◽  
Problem Solver ◽  
Routing Problem ◽  
Science Courses ◽  
Introductory Textbooks ◽  
Student Problem

The simple vehicle routing problem (VRP) is a common topic of discussion in introductory operations research/management science courses. The VRP can be framed in a variety of ways, and it can be difficult to solve to optimality. For solution purposes, introductory textbooks demonstrate how Excel’s Evolutionary Solver (ES) add-in produces a routing. The ES utilizes a genetic algorithm with a heuristic stopping rule to produce a routing that is not guaranteed to be optimal. Beyond pointing out that search controls, such as maximum execution time, may be extended and followed by restart(s) of ES, textbook treatments do not offer alternative ways to continue the search for a possibly better routing. In this paper, a suite of ways is presented in which students may investigate beyond what ES produces or any other optimality-uncertain VRP solution method. The suite includes perturbation methods and other ways that function within an Excel spreadsheet environment that is popular with students and textbook writers. Because there is no demonstrable feature that confirms optimality, the student problem Solver must settle for a ‘best found’ result as unsettling as it may be. The incertitude is addressed.

scholarly journals A Heuristic Solution Method for Multi-Depot Vehicle Routing-Based Waste Collection Problems

2020 ◽  
Vol 10 (7) ◽  
pp. 2403
Author(s):  
Yanjun Shi ◽  
Lingling Lv ◽  
Fanyi Hu ◽  
Qiaomei Han
Keyword(s):  
Vehicle Routing ◽  
Waste Disposal ◽  
Vehicle Routing Problem ◽  
Solution Method ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Waste Collection ◽  
Heuristic Solution ◽  
Routing Problem ◽  
Combination Optimization

This paper addresses waste collection problems in which urban household and solid waste are brought from waste collection points to waste disposal plants. The collection of waste from the collection points herein is modeled as a multi-depot vehicle routing problem (MDVRP), aiming at minimizing the total transportation distance. In this study, we propose a heuristic solution method to address this problem. In this method, we firstly assign waste collection points to waste disposal plants according to the nearest distance, then each plant solves the single-vehicle routing problem (VRP) respectively, assigning customers to vehicles and planning the order in which customers are visited by vehicles. In the latter step, we propose the sector combination optimization (SCO) algorithm to generate multiple initial solutions, and then these initial solutions are improved using the merge-head and drop-tail (MHDT) strategy. After a certain number of iterations, the optimal solution in the last generation is reported. Computational experiments on benchmark instances showed that the initial solutions obtained by the sector combination optimization algorithm were more abundant and better than other iterative algorithms using only one solution for initialization, and the solutions with distance gap were obtained using the merge-head and drop-tail strategy in a lower CPU time compared to the Tabu search algorithm.

scholarly journals A Hybrid Solution Method for the Capacitated Vehicle Routing Problem Using a Quantum Annealer

2019 ◽  
Vol 6 ◽  
Author(s):  
Sebastian Feld ◽  
Christoph Roch ◽  
Thomas Gabor ◽  
Christian Seidel ◽  
Florian Neukart ◽  
...  
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Solution Method ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Hybrid Solution ◽  
Capacitated Vehicle

Learning-Based Branch-and-Price Algorithms for the Vehicle Routing Problem with Time Windows and Two-Dimensional Loading Constraints

2021 ◽  
Author(s):  
Xiangyi Zhang ◽  
Lu Chen ◽  
Michel Gendreau ◽  
André Langevin
Keyword(s):  
Vehicle Routing ◽  
Operations Research ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Learning Model ◽  
Exact Algorithms ◽  
Two Dimensional ◽  
Branch And Price ◽  
Routing Problem ◽  
Loading Constraints

A capacitated vehicle routing problem with two-dimensional loading constraints is addressed. Associated with each customer are a set of rectangular items, the total weight of the items, and a time window. Designing exact algorithms for the problem is very challenging because the problem is a combination of two NP-hard problems. An exact branch-and-price algorithm and an approximate counterpart are proposed to solve the problem. We introduce an exact dominance rule and an approximate dominance rule. To cope with the difficulty brought by the loading constraints, a new column generation mechanism boosted by a supervised learning model is proposed. Extensive experiments demonstrate the superiority of integrating the learning model in terms of CPU time and calls of the feasibility checker. Moreover, the branch-and-price algorithms are able to significantly improve the solutions of the existing instances from literature and solve instances with up to 50 customers and 103 items. Summary of Contribution: We wish to submit an original research article entitled “Learning-based branch-and-price algorithms for a vehicle routing problem with time windows and two-dimensional loading constraints” for consideration by IJOC. We confirm that this work is original and has not been published elsewhere, nor is it currently under for publication elsewhere. In this paper, we report a study in which we develop two branch-and-price algorithms with a machine learning model injected to solve a vehicle routing problem integrated the two-dimensional packing. Due to the complexity brought by the integration, studies on exact algorithms in this field are very limited. Our study is important to the field, because we develop an effective method to significantly mitigate computational burden brought by the packing problem so that exactness turns to be achievable within reasonable time budget. The approach can be generalized to the three-dimensional case by simply replacing the packing algorithm. It can also be adapted for other VRPs when high-dimensional loading constraints are concerned. Broadly speaking, the study is a typical example of adopting supervised learning to achieve acceleration for operations research algorithms, which expands the envelop of computing and operations research. Hence, we believe this manuscript is appropriate for publication by IJOC.

scholarly journals Combining a hierarchical task network planner with a constraint satisfaction solver for assembly operations involving routing problems in a multi-robot context

2018 ◽  
Vol 15 (3) ◽  
pp. 172988141878208 ◽  
Author(s):  
Jorge Muñoz-Morera ◽  
Francisco Alarcon ◽  
Ivan Maza ◽  
Anibal Ollero
Keyword(s):  
Vehicle Routing ◽  
Constraint Satisfaction ◽  
Vehicle Routing Problem ◽  
Problem Solver ◽  
Routing Problem ◽  
Hierarchical Task Network ◽  
The Cost ◽  
Assembly Operations ◽  
Structure Assembly ◽  
Multi Robot

This work addresses the combination of a symbolic hierarchical task network planner and a constraint satisfaction solver for the vehicle routing problem in a multi-robot context for structure assembly operations. Each planner has its own problem domain and search space, and the article describes how both planners interact in a loop sharing information in order to improve the cost of the solutions. The vehicle routing problem solver gives an initial assignment of parts to robots, making the distribution based on the distance among parts and robots, trying also to maximize the parallelism of the future assembly operations evaluating during the process the dependencies among the parts assigned to each robot. Then, the hierarchical task network planner computes a scheduling for the given assignment and estimates the cost in terms of time spent on the structure assembly. This cost value is then given back to the vehicle routing problem solver as feedback to compute a better assignment, closing the loop and repeating again the whole process. This interaction scheme has been tested with different constraint satisfaction solvers for the vehicle routing problem. The article presents simulation results in a scenario with a team of aerial robots assembling a structure, comparing the results obtained with different configurations of the vehicle routing problem solver and showing the suitability of using this approach.

scholarly journals A more efficient cutting planes approach for the green vehicle routing problem with capacitated alternative fuel stations

2021 ◽  
Author(s):  
Maurizio Bruglieri ◽  
Simona Mancini ◽  
Ornella Pisacane
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Solution Method ◽  
Ad Hoc ◽  
Cutting Plane ◽  
Alternative Fuel ◽  
Routing Problem ◽  
Benchmark Instances ◽  
Number Of Customers ◽  
Fleet Manager

AbstractThe Green Vehicle Routing Problem with Capacitated Alternative Fuel Stations assumes that, at each station, the number of vehicles simultaneously refueling cannot exceed the number of available pumps. The state-of-the-art solution method, based on the generation of all feasible non-dominated paths, performs well only with up to 2 pumps. In fact, it needs cloning the paths between every pair of pumps. To overcome this issue, in this paper, we propose new path-based MILP models without cloning paths, for both the scenario with private stations (i.e., owned by the fleet manager) and that with public stations. Then, a more efficient cutting plane approach is designed for addressing both the scenarios. Numerical results, obtained considering a set of benchmark instances ad hoc generated for this work, show both the efficiency and the effectiveness of this new cutting plane approach proposed. Finally, a sensitivity analysis, carried out by varying the number of customers to be served and their distribution, shows very good performances of the proposed approach.

scholarly journals The vehicle routing problem with heterogeneous locker boxes

2021 ◽  
Vol 29 (1) ◽  
pp. 113-142 ◽  
Author(s):  
Jasmin Grabenschweiger ◽  
Karl F. Doerner ◽  
Richard F. Hartl ◽  
Martin W. P. Savelsbergh
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Solution Method ◽  
Time Window ◽  
Mathematical Formulation ◽  
Routing Problem ◽  
Compensation Payment ◽  
Slot Size ◽  
Size Model ◽  
Last Mile Delivery

AbstractTo achieve logistic efficiency and customer convenience in last-mile delivery processes, a system with alternative delivery points in the form of locker box stations can be used. In such a system, customers can be served either at their home address within a certain time window, or at a locker box station where parcels can be picked up at any time. Customers can get a compensation payment when being served at a locker box. They can have a request of more than one parcel and the parcels can be of different sizes. At a locker box station, a limited number of slots of different sizes is available; we assume that parcels of one customer can be stored together in a slot. We consider the vehicle routing problem with heterogeneous locker boxes, where the total cost—consisting of routing and compensation costs—has to be minimized while taking into account the packing of parcels into locker boxes. We provide a mathematical formulation of the problem and propose a metaheuristic solution method. Instances and results from the literature for the problem with a single parcel and a single slot size are used to benchmark our metaheuristic solution method. For the problem with different sizes, we compare a unit-size model to a multi-size model, packing being considered in the latter. Finally, we analyze how different configurations of locker box stations work for different demand scenarios.

Sign in / Sign up

Export Citation Format

Share Document