Genetic algorithms for vehicle routing problem in delivery system

A parallel memetic algorithm for the NP-hard vehicle routing problem with time windows (VRPTW) is proposed. The algorithm consists of components which are executed as parallel processes. A process runs either a heuristic algorithm or a hybrid of a genetic algorithm and some local refinement procedures. In order to improve the results, processes co-operate periodically using a novel randomized scheme. During each phase of co-operation processes exploit their best solutions found so far. The purpose of the work is to devise the parallel memetic algorithm which determines the VRPTW solutions of the highest possible quality. The experiments on Gehring and Homberger's (GH) benchmarking tests show that the algorithm achieves very good results. By making use of it the best-known solutions to 171 out of 300 GH tests were improved.

Evolutionary algorithms, including genetic algorithm, usually appear premature convergence. In this paper, new crossover and mutation operators are introduced. This paper addresses an application of improved genetic algorithms (IGA) for solving the Vehicle Routing Problem (VRP). After the introduction of new genetic operators, diversity of population becomes abundant. The ability of global search is obviously improved in new algorithm. Simulations indicate that new genetic algorithm is competitive with other modern heuristics.

This paper studied the VRPSPDTW (Vehicle Routing Problem with Simultaneous Pickups and Deliveries and Time-Windows). An integer programming model is constructed, its objective is to minimize the delivery time. To further improve the convergence speed and optimization capability of the genetic algorithm, an improved genetic algorithm with local search (IGALS) is proposed, which is based on a hybrid crossover operation with elitist strategy and a local search using the thought of allowed delay time. The paper analyzed the advantages of heuristic algorithm to solve the problem by comparing the computation complexity between exact algorithm and heuristic algorithm. Testing by Solomon Benchmarks instances, the results show the IGALS has better performance than simple GA, which has better global optimization capability and faster convergence speed.

A hybrid tabu search algorithm, which generates initial solution by genetic algorithm, is proposed here to solve the vehicle routing problems with time windows (VRPTW). Experimental result shows that this algorithm is better than the other known algorithms.

This paper addresses the problem of delivering continuously deteriorating agri-fresh produce from a wholesaler to a number of retailers, within specific time windows. The prime objective is to decide the routes in such a way that the overall cost incurred in transportation, deterioration and penalty is minimised. To model these conflicting objectives a mathematical modelling approach is proposed. The Vehicle Routing Problem with Time Windows (VRPTW) is a Non-deterministic Polynomial-time hard (NP-hard) problem, without considering the business constraints, and becomes computationally prohibitive with the increase in number of retailers. To solve the VRPTW within feasible time limits, Artificial Immune System (AIS)-based solution methodology is proposed. The algorithm is tested on real-life instances generated from Azadpur wholesale market, New Delhi (India). An experiment is performed on the same problems with other algorithms, such as Genetic Algorithm (GA) and Simulated Annealing (SA), to compare the effectiveness and efficiency of the proposed approach. It is found from the quality of solution and rate of convergence that AIS performed better compared to the other applied approaches.

Vehicle Routing Problems (VRPs) are usually treated as static routing problem, since all the information is known before the routes been constructed. However, the real world VRPs are dynamic, where information relevant to the routing can change after the initial scheme has been done. This paper treats the dynamic VRPs with real time traffic information. We formulate the mathematic model of the VRPs, and propose a dynamic programming algorithm based on chaos optimization, whereas the route calculating module and dynamic programming module are introduced. Finally, the efficiency of the proposed algorithm is demonstrated by computer simulations.

The Vehicle Routing Problem with Time Windows (VRPTW) consists of a homogenous set of vehicles and a set of customer located in a city. In VRPTW all the vehicles starts from the depot visit the customer and end at the depot. Each customer is visited exactly by one vehicle within the specified time window. The objective is to minimize the number of vehicles and total distance travelled simultaneously. This represents the multiobjective Vehicle Routing Problem with Time Windows. The proposed work consists of Hybrid Genetic Search with Diversity Control using the Genetic Algorithm for solving the VRPTW. The Pareto approach is used for finding the set of optimal solutions for achieving the multiobjective. The crossover operator is used for exchanging the best routes, which have shortest distance. Two mutation operators such as relocation mutation operator and split mutation operator were used in this application. In this, it accounts penalty for an infeasible solutions with respect to time-window and duration constrains. The computations are performed using the instances which are obtained from the VRPLIB.

Vehicle routing problem is a typical NP hard problem in combinatorial optimization. This paper proposes an improved algorithm, namely Immune Ant Colony Algorithm to solve the problem of vehicle routing problem. The algorithm is based on the intelligent optimization idea- Ant Colony Algorithm and Artificial Immune Algorithm. In this paper, in order to solve the problem of vehicle routing problem effectively four aspects are modified to improve the basic ant colony algorithm. And the improved Artificial Immune Algorithm is used to solve the problem of Ant Colony Algorithm to be apt to plunge into as the local optimum solution. On the basis of these improvements, the framework of the new algorithm—Immune Ant Colony Algorithm is given. Finally, it was proved that Immune Colony Algorithm can quicken the convergence rate to obtain optimal solution and decrease computing time. In addition, the computing simulation examples show its validity.

Vehicle Routing Problem with Time Windows (VRPTW) is a constrained NP-hard problem that designs the least cost routes from one depot to a set of geographically scattered points. Most of traditional techniques are difficult to solve this kind of problem. In this paper, we explored the validity of another swarm-intelligence-based model-Bacterial Foraging Optimization (BFO) for VRPTW solving. Original BFO, BFO with linear decreasing chemotaxis step (BFO-LDC) and BFO with non-linear decreasing chemotaxis step (BFO-NDC) are used to obtain the best solutions of a given VRPTW problem, respectively. The experimental results demonstrated that the proposed BFO algorithms have the potential to solve Vehicle Routing Problem with Time Windows (VRPTW) with rapid convergence rate and the good result accuracy.

The growth in e-commerce sales has been constant and, driven by the Covid-19 pandemic, has exceeded previous expectations. Owing to social isolation, parcel lockers - systems for goods collection - have become widely used to reduce physical contact between people involved in the delivery process. This resource has also become an important strategy for reducing costs, as it enables deliveries from multiple customers to be consolidated in the same location. In the context of smart cities, in which the optimization of urban transport plays a key role in building more sustainable and efficient environments, the inclusion of parcel lockers is essential. Thus, research in this area that considers their use aligns with the concept of smart cities, promoting the use of innovative technologies to improve mobility and urban logistics. Specifically in this context, the Vehicle Routing Problem With Parcel Lockers (VRPPL) is an extension of the Vehicle Routing Problem with TimeWindows (VRPTW), introducing the use of lockers, with the objective to reduce the total distance traveled. In this study, an algorithm based on Iterated Greedy (IG) and Random Variable Neighborhood Descent (RVND) was developed to reduce the costs and computational times presented in the literature. We selected a set of 168 well-known instances for the problem. Computational experiments and comparative analyses were performed. The developed algorithm achieved better results in 167 instances, with an average gap of −1.315%. The best execution times were obtained for all instances, thereby reducing the mean execution time from 240.293s to 0.104s.

This paper investigates a well-known complex combinatorial problem known as the vehicle routing problem with time windows (VRPTW). Unlike the standard vehicle routing problem, each customer in the VRPTW is served within a given time constraint. This paper solves the VRPTW using an improved artificial bee colony (IABC) algorithm. The performance of this algorithm is improved by a local optimization based on a crossover operation and a scanning strategy. Finally, the effectiveness of the IABC is evaluated on some well-known benchmarks. The results demonstrate the power of IABC algorithm in solving the VRPTW.

In this paper, a useful variant of the vehicle routing problem, Vehicle Routing Problem with Time Windows and a limited number of vehicles (m-VRPTW) is given. The problem is to serve a number of customers at minimum cost by using a limited number of vehicles, without violating the time window constraint and the vehicle capacity constraint. The feasible solution of m-VRPTW may contain some unserved customers and third-party vehicles, such as taxies, are hired to serve these unserved customers. The mixed integer programming model of m-VRPTW is proposed and a two-phase algorithm based on insertion algorithm and tabu search algorithm is proposed to solve it. Experimental results show that our algorithm can yield effective and efficient solution and be capable of dealing with the m-VRPTW problems in real life conditions.

Path optimization is a critical issue in vehicle routing problems (VRPs). This study tends to solve a VRP with time windows (VRPTW), which is an important version of VRP. Its principal objective is to find out the minimum cost routes to serve the customers within service time intervals, by a fleet of vehicles having confined capacity. An improved ant colony optimization (ACO) is applied to the problem, which uses a new pheromone reset and update function to enhance the route searching and a 2-opt method to improve the optimized path. The proposed ACO is tested on standard benchmarks of VRPTW. The results verifies the effectiveness of the algorithm and confirms it as better path optimizing method for VRPTW, in comparison to the traditional ACO and other heuristics.

The competition between companies requires finding the optimized vehicle routing, for this reason researches are more and more interested in transportation problems, this article attempts to address a practical variant of the vehicle routing problem (VRP), known as the VRP with soft time windows (VRPSTW), where deliveries are still possible outside the time windows, that often arise in practice.

Real life distribution problems present high degree of complexity mostly derived by the need to respect a variety of constraints. Moreover they are not considered by the classical models of the vehicle routing literature. In this paper we consider a vehicle routing problem with heterogeneous vehicle fleet with different capacity, multi-dimensional capacity constraints, order/vehicle, item/vehicle, and item/item compatibility constraints, different start and end locations for vehicles, and multiple time windows restrictions. We propose an evolutionary algorithm based on the combination of a genetic algorithm and local search heuristics. We investigated the performance of the implemented algorithm in the large-scale retail and in the waste collection industries.