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 On the Selective Vehicle Routing Problem

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 771 ◽  
Author(s):  
Cosmin Sabo ◽  
Petrică C. Pop ◽  
Andrei Horvat-Marc
Keyword(s):  
Mathematical Model ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Mixed Integer ◽  
The Novel ◽  
Routing Problem ◽  
Proposed Model ◽  
Benchmark Instances ◽  
Number Of Customers ◽  
Optimal Set

The Generalized Vehicle Routing Problem (GVRP) is an extension of the classical Vehicle Routing Problem (VRP), in which we are looking for an optimal set of delivery or collection routes from a given depot to a number of customers divided into predefined, mutually exclusive, and exhaustive clusters, visiting exactly one customer from each cluster and fulfilling the capacity restrictions. This paper deals with a more generic version of the GVRP, introduced recently and called Selective Vehicle Routing Problem (SVRP). This problem generalizes the GVRP in the sense that the customers are divided into clusters, but they may belong to one or more clusters. The aim of this work is to describe a novel mixed integer programming based mathematical model of the SVRP. To validate the consistency of the novel mathematical model, a comparison between the proposed model and the existing models from literature is performed, on the existing benchmark instances for SVRP and on a set of additional benchmark instances used in the case of GVRP and adapted for SVRP. The proposed model showed better results against the existing models.

scholarly journals Development Of A New Genetic Algorithm For Solving Capacitated Vehicle Routing Problem In Short Time

2019 ◽  
Vol 8 (11) ◽  
pp. 903-907
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Benchmark Instances ◽  
Full Capacity ◽  
Short Time ◽  
Number Of Customers ◽  
Capacitated Vehicle

In this paper a new genetic algorithm is developed for solving capacitated vehicle routing problem (CVRP) in situations where demand is unknown till the beginning of the trip. In these situations it is not possible normal metaheuristics due to time constraints. The new method proposed uses a new genetic algorithm based on modified sweep algorithm that produces a solution with the least number of vehicles, in a relatively short amount of time. The objective of having least number of vehicles is achieved by loading the vehicles nearly to their full capacity, by skipping some of the customers. The reduction in processing time is achieved by restricting the number of chromosomes to just one. This method is tested on 3 sets of standard benchmark instances (A, M, and G) found in the literature. The results are compared with the results from normal metaheuristic method which produces reasonably accurate results. The results indicate that whenever the number of customers and number of vehicles are large the new genetic algorithm provides a much better solution in terms of the CPU time without much increase in total distance traveled. If time permits the output from this method can be further improved by using normal established metaheuristics to get better solution

An Integrated Hub Location and Multi-Depot Vehicle Routing Problem

2013 ◽  
Vol 409-410 ◽  
pp. 1188-1192 ◽  
Author(s):  
Ji Ung Sun
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Solution Method ◽  
Location Problem ◽  
Total System ◽  
Ant Colony Optimization Algorithm ◽  
Hub Location ◽  
Hub Location Problem ◽  
Routing Problem ◽  
Number Of Customers

This paper considers the integrated hub location and multi-depot vehicle routing problem. In this type of problem, we have to determine the location of hubs within a set of candidate locations, allocation of customers to each selected hub location and routes of the vehicles to meet the demands of number of customers in order to minimize the total system cost. To solve these problems simultaneously we apply a hierarchical structure, which hub location as the main problem and vehicle routing as a subordinate one. An integrated solution method based on ant colony optimization algorithm is developed which solves hub location problem and vehicle routing problem hierarchically. Its performance is examined through a comparative study.

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 Single-Commodity Vehicle Routing Problem with Pickup and Delivery Service

2008 ◽  
Vol 2008 ◽  
pp. 1-17 ◽  
Author(s):  
Goran Martinovic ◽  
Ivan Aleksi ◽  
Alfonzo Baumgartner
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Search Space ◽  
Global Optimum ◽  
Pickup And Delivery ◽  
Routing Problem ◽  
Practical Applications ◽  
Delivery Service ◽  
Number Of Customers ◽  
Single Commodity

We present a novel variation of the vehicle routing problem (VRP). Single commodity cargo with pickup and delivery service is considered. Customers are labeled as either cargo sink or cargo source, depending on their pickup or delivery demand. This problem is called a single commodity vehicle routing problem with pickup and delivery service (1-VRPPD). 1-VRPPD deals with multiple vehicles and is the same as the single-commodity traveling salesman problem (1-PDTSP) when the number of vehicles is equal to 1. Since 1-VRPPD specializes VRP, it is hard in the strong sense. Iterative modified simulated annealing (IMSA) is presented along with greedy random-based initial solution algorithm. IMSA provides a good approximation to the global optimum in a large search space. Experiment is done for the instances with different number of customers and their demands. With respect to average values of IMSA execution times, proposed method is appropriate for practical applications.

scholarly journals A granular tabu search for the refrigerated vehicle routing problem with homogeneous fleet

2022 ◽  
Vol 13 (1) ◽  
pp. 135-150 ◽  
Author(s):  
John Willmer Escobar ◽  
José Luis Ramírez Duque ◽  
Rafael García-Cáceres
Keyword(s):  
Energy Consumption ◽  
Tabu Search ◽  
Vehicle Routing ◽  
Thermodynamic Model ◽  
Vehicle Routing Problem ◽  
High Quality ◽  
Routing Problem ◽  
Benchmark Instances ◽  
Capacitated Vehicle ◽  
Selection Of

The Refrigerated Capacitated Vehicle Routing Problem (RCVRP) considers a homogeneous fleet with a refrigerated system to decide the selection of routes to be performed according to customers' requirements. The aim is to keep the energy consumption of the routes as low as possible. We use a thermodynamic model to understand the unloading of products from trucks and the variables' efficiency, such as the temperature during the day influencing energy consumption. By considering various neighborhoods and a shaking procedure, this paper proposes a Granular Tabu Search scheme to solve the RCVRP. Computational tests using adapted benchmark instances from the literature demonstrate that the suggested method delivers high-quality solutions within short computing times, illustrating the refrigeration system's effect on routing decisions.

scholarly journals A Simulated Annealing Heuristic For The Green Vehicle Routing Problem

2015 ◽  
Vol 1 (2) ◽  
pp. 17-21
Author(s):  
Moch Yasin ◽  
Vincent F. Yu
Keyword(s):  
Simulated Annealing ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Alternative Fuel ◽  
The United States ◽  
Routing Problem ◽  
Transportation Sector ◽  
Alternative Fuel Vehicle ◽  
Driving Range ◽  
Vehicle Fleet

Nowadays, the encouragement of the use of green vehicle is greater than it previously has ever been. In the United States, transportation sector is responsible for 28% of national greenhouse gas emissions in 2009. Therefore, there have been many studies devoted to the green supply chain management including the green vehicle routing problem (GVRP). GVRP plays a very important role in helping organizations with alternative fuel-powered vehicle fleets overcome obstacles resulted from limited vehicle driving range in conjunction with limited fuel infrastructure. The objective of GVRP is to minimize total distance traveled by the alternative fuel vehicle fleet. This study develops a mathematical model and a simulated annealing (SA) heuristic for the GVRP. Computational results indicate that the SA heuristic is capable of obtaining good GVRP solutions within a reasonable amount of time.

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