An artificial bee colony algorithm for the vehicle routing problem with backhauls and time windows (original) (raw)
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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.
Bee Algorithm for the vehicle routing problems with time windows
—The natural honeybee's behavior has been modelled by researchers to solve optimization problems. This paper introduces the Bee algorithm based on honeybee behavior for the vehicle routing problem with time windows (VRPTW). The algorithm has been tested on Solomon instances. The encoding of the problem and the operations needed to implement algorithm are outlined. Experiments show that on the considered instances, using BA performs almost as well as planning for long-term, while using much less computation time. Overall, computational investigations show that the proposed algorithm is good and promising approach for the VRPTW. Keywords—Foraging behaviour; bee algorithm; vehicle routing problem with time windows.
This paper presents a bee colony optimisation (BCO) algorithm to tackle the vehicle routing problem with time window (VRPTW). The VRPTW involves recovering an ideal set of routes for a fleet of vehicles serving a defined number of customers. The BCO algorithm is a population-based algorithm that mimics the social communication patterns of honeybees in solving problems. The performance of the BCO algorithm is dependent on its parameters, so the online (self-adaptive) parameter tuning strategy is used to improve its effectiveness and robustness. Compared with the basic BCO, the adaptive BCO performs better. Diversification is crucial to the performance of the population-based algorithm, but the initial population in the BCO algorithm is generated using a greedy heuristic, which has insufficient diversification. Therefore the ways in which the sequential insertion heuristic (SIH) for the initial population drives the population toward improved solutions are examined. Experimental comparisons indicate that the proposed adaptive BCO-SIH algorithm works well across all instances and is able to obtain 11 best results in comparison with the best-known results in the literature when tested on Solomon’s 56 VRPTW 100 customer instances. Also, a statistical test shows that there is a significant difference between the results.
Bees Algorithm for Vehicle Routing Problems with Time Windows
International Journal of Machine Learning and Computing, 2018
This paper presents the bees algorithm for vehicle routing problems within time windows (VRPTW). The VRPTW aims to determine the optimal route for a number of vehicles when serving a set of customers within a predefined time interval (the time window). The objective in VRPTW is to minimize overall transportation cost. Various heuristic and metaheuristic approaches have been developed in literature to produce high-quality solutions for this problem because of its high complication rate and extensive implementation in real-life applications. This research investigates the use of bee algorithms (BA) for VRPTW and identifying the strengths and weaknesses. Index Terms-Foraging behaviour, bees algorithm, vehicle routing problem with time windows. I. INTRODUCTION Vehicle routing problems (VRP) have an important role in the domains of transportation, delivery, and logistics. Hence, numerous research works have been undertaken to study VRP since 1959 [1]. The work presented by Dantzig and Ramser in 1959 proposed the problem as a generalized Travelling Salesman Problem (TSP). Later, a huge number of studies have been conducted on number of VRP divisions (e.g. VRP with Time Windows (VRPTW), VRP with PickUp and Delivery (VRPPD), Multiple Depot VRP (MDVRP), MDVRP with Time Windows (MD-VRPTW), and Capacitated VRP (CVRP)). VRPTW is an NP-hard problem, which is concerned with determining the best routing of a set of limited capacity vehicles between a central depot and a number of scattered customers, where customers must be visited within predefined time duration (the time window). Several approaches have been developed for VRPTW. Solomon [2] first introduced heuristics to solve this problem. In recent years, metaheuristics has become increasingly popular. Metaheuristics can be classified either as single-based approaches such as tabu search and simulated
Knowledge Engineering and Data Science
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 car...
2014
— The vehicle routing problem with time windows, widely used in practice, is an NP-hard problem. This paper presents a new meta-heuristic algorithm for solving the problem. Unlike traditional two-steps algorithms, the proposed optimization algorithm, based on the artificial bee colony algorithm combined with the vector evaluated technique, solves the problem as a multi-objective problem. As a result, the algorithm provides a simultaneous solution set. The approach was tested on standard Solomon's benchmark problems. The result shows that this algorithm was better than (in terms of number of vehicles) or equal to other existing heuristic algorithms. Keywords- Vehicle routing problem; artificial bee colony; multi-objective I.
Bee-route: A Bee Algorithm for the Multi-objective Vehicle Routing Problem
Proceedings of the 15th International Conference on Software Technologies
The vehicle routing problem has attracted a lot of interest during many decades because of its wide range of applications in real life problems. This paper aims to test the efficiency and capability of bee colony optimization for this kind of problem. We present a Bee-route algorithm: a multi-objective artificial Bee Colony algorithm for the Vehicle Routing Problem with Time Windows. We have performed our experiments on well known benchmarks in the literature to compare our proposed algorithm results with other state-of-theart algorithms.
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.
Revista română de informatică şi automatică, 2022
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.