scholarly journals A two-level based genetic algorithm for solving the soft-clustered vehicle routing problem

2021 ◽  
Vol 38 (1) ◽  
pp. 117-128
Author(s):  
OVIDIU COSMA ◽  
◽  
PETRICĂ C. POP ◽  
CORINA POP SITAR ◽  
◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Optimization Problem ◽  
A Priori ◽  
Minimum Cost ◽  
Routing Problem ◽  
Solution Approach ◽  
Benchmark Instances ◽  
Generalized Vehicle Routing Problem

The soft-clustered vehicle routing problem (Soft-CluVRP) is a relaxation of the clustered vehicle routing problem (CluVRP), which in turn is a variant of the generalized vehicle routing problem (GVRP). The aim of the Soft-CluVRP is to look for a minimum cost group of routes starting and ending at a given depot to a set of customers partitioned into a priori defined, mutually exclusive and exhaustive clusters, satisfying the capacity constraints of the vehicles and with the supplementary property that all the customers from the same cluster have to be supplied by the same vehicle. The considered optimization problem is NP-hard, that is why we proposed a two-level based genetic algorithm in order to solve it. The computational results reported on a set of existing benchmark instances from the literature, prove that our novel solution approach provides high-quality solutions within acceptable running times.

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

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 Algoritma Genetika untuk Pemecahan Masalah Rute Kendaraan dengan Ukuran dan Campuran Armada, Trip Majemuk, Pengiriman Terbagi, Produk Majemuk, dan Kendaraan dengan Kompartemen Majemuk

2018 ◽  
Vol 19 (2) ◽  
pp. 115
Author(s):  
Suprayogi Suprayogi ◽  
Daniel Bunga Paillin
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Routing Problem ◽  
Fleet Size ◽  
Split Delivery ◽  
Solution Approach ◽  
Fleet Size And Mix

This paper discusses a variant of the basic vehicle routing problem (VRP) by including the following characteristics: fleet size and mix, multiple trips, split delivery, and multiple compartments. One of real cases of this problem is related to determining tanker’s routes in delivering fuel products from a supply port to a number of destination ports. Each tanker has several compartments where each compartment is dedicated to a certain fuel product. In this paper, a solution approach based on genetic algorithm (GA) is proposed and tested using nine hypothetical instances. Experiment results show that the proposed GA gives consistent results measured by coefficient variations

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.

A Memetic Algorithm for the Multi-Depot Vehicle Routing Problem with Limited Stocks

2015 ◽  
pp. 411-445 ◽  
Author(s):  
Shi Li ◽  
Yahong Zheng
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Memetic Algorithm ◽  
Central Place ◽  
Combinatorial Problems ◽  
Logistics Management ◽  
Routing Problem ◽  
Benchmark Instances

The Vehicle Routing Problem (VRP) is one of important combinatorial problems, which holds a central place in logistics management. One of the most widely studied problems in the VRP family is the Multi-Depot Vehicle Routing Problem (MDVRP), where more than one depot is considered. In this chapter, the authors focus on a new extension of the MDVRP in which goods loaded by the vehicle are restricted due to limited stocks available at warehouses. More specifically, this extension consists in determining a least cost routing plan that can satisfy all the customs demands by delivering available stocks. Indeed, this problem is often encountered when goods are shortage in some warehouses. To deal with the problem efficiently, a memetic algorithm is proposed in this chapter. The authors study this approach on a set of modified benchmark instances and compare its performance to a pure genetic algorithm.

scholarly journals A specialized genetic algorithm for the fuel consumption heterogeneous fleet vehicle routing problem with bidimensional packing constraints

2021 ◽  
pp. 191-204 ◽  
Author(s):  
Luis Miguel Escobar-Falcón ◽  
David Álvarez-Martínez ◽  
John Wilmer-Escobar ◽  
Mauricio Granada-Echeverri
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Fuel Consumption ◽  
Vehicle Routing Problem ◽  
Computational Study ◽  
Real Life ◽  
Great Success ◽  
Routing Problem ◽  
Heterogeneous Vehicle Routing Problem ◽  
Benchmark Instances

The vehicle routing problem combined with loading of goods, considering the reduction of fuel consumption, aims at finding the set of routes that will serve the demands of the customers, arguing that the fuel consumption is directly related to the weight of the load in the paths that compose the routes. This study integrates the Fuel Consumption Heterogeneous Vehicle Routing Problem with Two-Dimensional Loading Constraints (2L-FHFVRP). To reduce fuel consumption taking the associated environmental impact into account is a classical VRP variant that has gained increasing attention in the last decade. The objective of this problem is to design the delivery routes to satisfy the customers’ demands with the lowest possible fuel consumption, which depends on the distances of the paths, the assigned vehicles, the loading/unloading pattern and the load weight. In the vehicle routing problem literature, the approximate algorithms have had great success, especially the evolutionary ones, which appear in previous works with quite a sophisticated structure, obtaining excellent results, but that are difficult to implement and adapt to other variants such as the one proposed here. In this study, we present a specialized genetic algorithm to solve the design of routes, keeping its main characteristic: the easy implementation. By contrast, the loading of goods restriction is validated by means of a GRASP algorithm, which has been widely employed for solving packing problems. With a view of confirming the performance of the proposed methodology, we provide a computational study that uses all the available benchmark instances, allowing to illustrate the savings achieved in fuel consumption. In addition, the methodology suggested can be adapted to the version of solely minimizing the total distance traveled for serving the customers (without the fuel consumption) and it is compared to the best works presented in the literature. The computational results show that the methodology manages to be adequately adapted to this version and it is capable of finding improved solutions for some benchmark instances. As for future work, we propose to adjust the methodology to consider the three-dimensional loading problem so that it adapts to more real-life conditions of the industry.

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.

scholarly journals Machine Learning-Based Parameter Tuned Genetic Algorithm for Energy Minimizing Vehicle Routing Problem

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
P. L. N. U. Cooray ◽  
Thashika D. Rupasinghe
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Business Practices ◽  
Routing Problem ◽  
Solution Approach ◽  
Sustainable Logistics

During the last decade, tremendous focus has been given to sustainable logistics practices to overcome environmental concerns of business practices. Since transportation is a prominent area of logistics, a new area of literature known as Green Transportation and Green Vehicle Routing has emerged. Vehicle Routing Problem (VRP) has been a very active area of the literature with contribution from many researchers over the last three decades. With the computational constraints of solving VRP which is NP-hard, metaheuristics have been applied successfully to solve VRPs in the recent past. This is a threefold study. First, it critically reviews the current literature on EMVRP and the use of metaheuristics as a solution approach. Second, the study implements a genetic algorithm (GA) to solve the EMVRP formulation using the benchmark instances listed on the repository of CVRPLib. Finally, the GA developed in Phase 2 was enhanced through machine learning techniques to tune its parameters. The study reveals that, by identifying the underlying characteristics of data, a particular GA can be tuned significantly to outperform any generic GA with competitive computational times. The scrutiny identifies several knowledge gaps where new methodologies can be developed to solve the EMVRPs and develops propositions for future research.

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