A comparison of Artificial Bee Colony algorithm and the Genetic Algorithm with the purpose of minimizing the total distance for the Vehicle Routing Problem (original) (raw)
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Procedia Engineering, 2014
Job shop scheduling is predominantly an Non deterministic polynomial (NP)-complete challenge which is successfully tackled by the ABC algorithm by elucidating its convergence. The Job Shop Scheduling Problem (JSSP) is one of the most popular scheduling models existing in practice which is among the hardest combinatorial optimization problems. The ABC (Artificial Bee Colony) technique is concerned, it is observed that the entire specific artificial bees move about in a search space and select food sources by suitably adapting their location, know-how and having a full awareness of their nest inhabitants. Moreover, several scout bees soar and select the food sources discretely without making use of any skills. In the event of the quantity of the nectar in the fresh source becoming larger than the nectar quantity of an available source, they remember the fresh location and conveniently disregard the earlier position. In this way, the ABC system integrates local search techniques, executed by employed and onlooker bees, with universal search approaches, administered by onlookers and scouts. In our ambitious approach we have employed these three bees to furnish optimization in makespan, machine work load and the whole run period in an optimized method. In this way, with the efficient employment of our effective technique we make an earnest effort to minimize the makespan and number of machines. This paper compares GA to minimize the make span of the job scheduling process with ABC and proved that ABC algorithm produces the better result.
2016
This paper presents an artificial bee colony algorithm to solve the vehicle routing problem with backhauls and time windows (VRPBTW). This problem is a combination of the vehicle routing problem with backhauls (VRPB) and the vehicle routing problem with time windows (VRPTW). In VRPBTW, a homogenous fleet of vehicles are utilized to deliver goods to customers in linehaul set and then to pick up goods from customers in backhaul set. Vehicle capacity, backhaul and time windows are the major constraints for this problem. The objective of VRPBTW is to minimize the sum of route distance that satisfy all constraints. An artificial bee colony (ABC) algorithm with local search procedures are proposed to solve the modified Solomon's VRPTW benchmark problems. The results of computational experiments reveal that the performance of the proposed ABC algorithm is comparable to the other metaheuristics in terms of the quality of solution.
Modified artificial bee colony for the vehicle routing problems with time windows
The natural behaviour of the honeybee has attracted the attention of researchers in recent years and several algorithms have been developed that mimic swarm behaviour to solve optimisation problems. This paper introduces an artificial bee colony (ABC) algorithm for the vehicle routing problem with time windows (VRPTW). A Modified ABC algorithm is proposed to improve the solution quality of the original ABC. The high exploration ability of the ABC slows-down its convergence speed, which may due to the mechanism used by scout bees in replacing abandoned (unimproved) solutions with new ones. In the Modified ABC a list of abandoned solutions is used by the scout bees to memorise the abandoned solutions, then the scout bees select a solution from the list based on roulette wheel selection and replace by a new solution with random routs selected from the best solution. The performance of the Modified ABC is evaluated on Solomon benchmark datasets and compared with the original ABC. The computational results demonstrate that the Modified ABC outperforms the original ABC also produce good solutions when compared with the best-known results in the literature. Computational investigations show that the proposed algorithm is a good and promising approach for the VRPTW.
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019
Vehicle Routing Problem is one of the classic problems in GIS (Geospatial Information System) which had been studied for long times. An answer can be accepted as a good solution if it would be able to optimize the total length of the route or decrease the number of vehicles. A VRP defines finding the optimum route for some vehicles that serve to some customers and return to the service center. This problem is economically important because the cost and the time of serving to costumers are related to optimization of the problem's answer. Furthermore, there are many problems like BUS management, Post pickup and delivery system and other servicing systems, which are technically similar to VRP. The aim of these problems is finding a composition of optimum routes between server and costumers. In addition, as the cost is related to time, finding shortest path means decreasing cost serving and decreasing time. In this article, a hybrid model using Artificial Bee Colony and Genetic Algorithm is proposed to solve VRP. In the first step, Artificial Bee Colony has been used to find a solution for five vehicles. The scout and the onlooker bees produced in 8 modes by two methods including the nearest neighborhood and the wide neighborhood. In the second step, the Genetic Algorithm helps to optimize the solutions. The results show that the production of the scout bees is the most effective factor in the answers to the problem and helps greatly converging the answers as soon as possible.
Using A Bee Colony Algorithm For Neighborhood Search In Job Shop Scheduling Problems
ECMS 2007 Proceedings edited by: I. Zelinka, Z. Oplatkova, A. Orsoni, 2007
This paper describes a population-based approach that uses a honey bees foraging model to solve job shop scheduling problems. The algorithm applies an efficient neighborhood structure to search for feasible solutions and iteratively improve on prior solutions. The initial solutions are generated using a set of priority dispatching rules. Experimental results comparing the proposed honey bee colony approach with existing approaches such as ant colony, tabu search and shifting bottleneck procedure on a set of job shop problems are presented. The results indicate the performance of the proposed approach is comparable to other efficient scheduling approaches.
Lecture Notes in Management and Industrial Engineering, 2019
Job shop scheduling for labor-intensive and project type manufacturing is a too hard task because the operation times are not known before production and change according to the orders' technical specifications. In this paper, a case study is presented for scheduling a labor-intensive and project type workshop. The aim is to minimize the makespan of the orders. For this purpose, the artificial bee colony algorithm (ABC) is used to determine the entry sequence of the waiting orders to the workshop and dispatching to the stations. 18 different orders and 6 welding stations are used for the scheduling in this case. The input data of the algorithm are the technical specifications (such as weight and width of the demanded orders) and processing times of the orders which vary according to the design criteria demanded by the customers. According to the experimental results, it is observed that the ABC algorithm has reduced the makespan.
An artificial bee colony algorithm for the vehicle routing problem with backhauls and time windows
2019
The vehicle routing problem with backhauls and time windows (VRPBTW) aims to find a feasible vehicle route that minimizes the total traveling distance while imposing capacity, backhaul, and time-window constraints. We present an enhanced artificial bee colony algorithm (EABCA), which is a meta-heuristic, to solve this problem. Three strategies-a forbidden list, the sequential search for onlookers, and the combination of 1-move intra-route exchange and λ-interchange technique-are introduced for EABCA. The proposed method was tested on a set of benchmark instances. The computational results show that the EABCA can produce better solutions than the basic ABCA, and it discovered many new best-known solutions.