An artificial bee colony algorithm for the capacitated vehicle routing problem

Session MF-03: Population-based metaheuristics for routing problems - Stream: Metaheuristics - Invited session no. 3This paper introduces an artificial bee colony heuristic for the capacitated vehicle routing problem. The artificial bee colony heuristic is a swarm-based heuristic, which mimics the foraging behavior of a honey bee swarm. The performance of the heuristic is evaluated on two sets of benchmark instances. A new scheme is also developed to improve the performance of the artificial bee colony heuristic. Computational results show that the heuristic with the new scheme produces good solutions.postprintThe 24th European Conference on Operational Research (EURO 24), Lisbon, Portual, 11-14 July 2010. In Abstract Book of EURO 24, 2010, p. 89, MF-03-

An artificial bee colony algorithm for the capacitated vehicle routing problem

This paper introduces an artificial bee colony heuristic for solving the capacitated vehicle routing problem. The artificial bee colony heuristic is a swarm-based heuristic, which mimics the foraging behavior of a honey bee swarm. An enhanced version of the artificial bee colony heuristic is also proposed to improve the solution quality of the original version. The performance of the enhanced heuristic is evaluated on two sets of standard benchmark instances, and compared with the original artificial bee colony heuristic. The computational results show that the enhanced heuristic outperforms the original one, and can produce good solutions when compared with the existing heuristics. These results seem to indicate that the enhanced heuristic is an alternative to solve the capacitated vehicle routing problem. © 2011 Elsevier B.V. All rights reserved.postprin

A stochastic hybrid algorithm for multi-depot and multi-product routing problem with heterogeneous vehicles

Abstract. A mathematical model and heuristic method for solving multi-depot and multi-product vehicle routing problem (MD-MPVRP) with heterogeneous vehicles have been proposed in this article. Customers can order eclectic products and depots are supposed to deliver customers' orders before the lead time, using vehicles with diverse capacities, costs and velocities. Hence, mathematical model of multi-depot vehicle routing problem has been developed to mirror these conditions. This model is aimed at minimizing the serving distances which culminates in a reduction in prices and also serving time. As the problem is so complex and also solving would be too time-taking, a heuristic method has been offered. The heuristic method, at first, generates an initial solution through a three-step procedure which encompasses grouping, routing and vehicle selection, scheduling and packaging. Then it improves the solution by means of simulated annealing. We have considered the efficiency of offered algorithm by comparing its solutions with the optimum solutions and also during a case study. [V. Mahdavi Asl, S.A. Sadeghi, MR. Ostadali Makhmalbaf. A stochastic hybrid algorithm for multi-depot and multi-product routing problem with heterogeneous vehicles

Edge detection of aerial images using artificial bee colony algorithm

Edge detection techniques are the one of the best popular and significant implementation areas of the image processing. Moreover, image processing is very widely used in so many fields. Therefore, lots of methods are used in the development and the developed studies provide a variety of solutions to problems of computer vision systems. In many studies, metaheuristic algorithms have been used for obtaining better results. In this paper, aerial images are used for edge information extraction by using Artificial Bee Colony (ABC) Optimization Algorithm. Procedures were performed on gray scale aerial images which are taken from RADIUS/DARPA-IU Fort Hood database. Initially bee colony size was specified according to sizes of images. Then a threshold value was set for each image, which related with images’ standard deviation of gray scale values. After the bees were distributed, fitness values and probability values were computed according to gray scale value. While appropriate pixels were specified, the other ones were being abandoned and labeled as banned pixels therefore bees never located on these pixels again. So the edges were found without the need to examine all pixels in the image. Our improved method’s results are compared with other results found in the literature according to detection error and similarity calculations’. All the experimental results show that ABC can be used for obtaining edge information from images.Publisher's Versio

PENGOPTIMUMAN BIAYA DISTRIBUSI MENGGUNAKAN INTEGER PROGRAMMING DALAM MENYIKAPI KEBIJAKAN GANJIL-GENAP DI JAKARTA

Kebijakan Ganjil-Genap merupakan salah satu aturan yang diterapkan di Jakarta untuk mengurangi kemacetan. Kebijakan ini mengakibatkan kendaraan bermotor tidak bisa melalui ruas jalan tertentu, jika ganjil/genapnya nomor-polisi kendaraan tidak sesuai dengan ganjil/genapnya tanggal kendaraan tersebut ketika melintasi ruas jalan yang terkena kebijakan. Ada beberapa jenis kendaraan yang terkena dampak kebijakan ini, di antaranya ialah kendaraan distribusi perusahaan ekspedisi. Kebijakan ini membuat biaya distribusi perusahaan ekspedisi meningkat karena jarak perjalanan menuju konsumen menjadi lebih jauh untuk menghindari ruas jalan Ganjil-Genap ketika plat nomor polisi kendaraan yang digunakan untuk distribusi tidak sesuai dengan jenis tanggal distribusi. Proses distribusi yang meminimumkan biaya pengeluaran memerlukan penentuan rute yang optimal. Masalah penentuan rute optimal ini diformulasikan ke dalam Vehicle Routing Problem menggunakan Integer Linear Programming. Masalah ini diselesaikan menggunakan perangkat lunak LINGO 18.0 dan solusi optimal yang diperoleh berupa rute pendistribusian barang menggunakan kendaraan tertentu serta meminimumkan biaya distribusi

An artificial bee colony algorithm for public bike repositioning problem

Paper PresentationConference Theme: Informing transport’s future through practical researchPublic bike repositioning is crucial in public bike sharing systems due to the imbalanced distribution of public bikes. This paper models the public bike repositioning problem (PBRP) involving two non-linear objectives, which are to minimize total service duration and the duration of the longest vehicle route. It includes practical constraints such as the tolerance of demand dissatisfaction and the limitation of duration on the longest route. These objective functions and constraints make the PBRP become NP-hard, so here introduces an artificial bee colony (ABC) algorithm to solve this PBRP. Three neighbourhood operators are introduced to improve the solution search. A modified ABC is proposed to further improve the solution quality. The performance of the modified heuristic was evaluated with the network of Vélib', and compared with the original heuristic and the Genetic Algorithm. These results may therefore prove that the modified heuristic can be an alternative to solve the PBRP. The numerical studies demonstrated that the two objective functions performed differently in which the increase in fleet size may not improve the objective value. This paper will therefore discuss on the practical implications of the trade-offs and provide suggestions about similar repositioning operations.postprin

Hybrid Artificial Bee Colony and Improved Simulated Annealing for the Capacitated Vehicle Routing Problem

Capacitated Vehicle Routing Problem (CVRP) is a type of NP-Hard combinatorial problem that requires a high computational process. In the case of CVRP, there is an additional constraint in the form of a capacity limit owned by the vehicle, so the complexity of the problem from CVRP is to find the optimum route pattern for minimizing travel costs which are also adjusted to customer demand and vehicle capacity for distribution. One method of solving CVRP can be done by implementing a meta-heuristic algorithm. In this research, two meta-heuristic algorithms have been hybridized: Artificial Bee Colony (ABC) with Improved Simulated Annealing (SA). The motivation behind this idea is to complete the excess and the lack of two algorithms when exploring and exploiting the optimal solution. Hybridization is done by running the ABC algorithm, and then the output solution at this stage will be used as an initial solution for the Improved SA method. Parameter testing for both methods has been carried out to produce an optimal solution. In this study, the test was carried out using the CVRP benchmark dataset generated by Augerat (Dataset 1) and the recent CVRP dataset from Uchoa (Dataset 2). The result shows that hybridizing the ABC algorithm and Improved SA could provide a better solution than the basic ABC without hybridization