Demand coverage diversity based ant colony optimization for dynamic vehicle routing problems

Dynamic vehicle routing problem (DVRP) with single depot has received increasing interest from engineers and scientists. Dynamic multi-depot vehicle routing problem (DMDVRP), an extension of DVRP, however, has not received much attention. In our paper, a distance-based clustering approach is introduced to simplify the DMDVRP by allocating each customer to its nearest depot. Thus, DMDVRP is decomposed to a sequence of DVRPs. An improved ant colony optimisation (IACO) with ant-weight strategy and mutation operation is presented to optimise vehicle routing problem (VRP) in this paper. Moreover, to satisfy the real-time feature of DMDVRP, the nearest addition approach is used to handle the new orders occurring during a time slice on the basis of VRP solution. Finally, the computational results for 17 benchmark problems are reported to validate that IACO with the distance-based clustering approach is more suitable for solving DMDVRP.

The Vehicle Routing Problem (VRP) is a complex and high-level set of routing problems. Two of its important variants are the Dynamic Vehicle Routing Problem (DVRP) and the Green Vehicle Routing Problem (GVRP). The first one has become a challenging research topic in the last two decades, in which not all informations are known in advance, but are revealed as the system progresses. The second one is seen as a new application and solution in new logistics patterns, more specifically, for finding routes of vehicles to serve a set of customers while minimizing the total amount of CO 2 emissions, by increasing the loading rate and reducing the number of empty trips could reduce from 10% to 40% km traveled and therefore CO 2 emissions [1]. In this paper, we combine these two variants (DVRP and GVRP), where we try to minimize, in the dynamic environment, the greenhouse gas especially the carbon dioxide CO 2 , which is the immediate consequence of the depletion of the ozone layer and time duration. We will call this combination the Dynamic Green Vehicle Routing Problem (DGVRP). To study this new problem, we present the technique employed to estimate the amount of CO 2 emissions, the emission matrix and integrate them into the DGVRP model, then we propose the hybridization based on Ant Colony Optimization (ACO) algorithm with a Large Neighborhood Search (LNS) algorithm to solve our problem. The effectiveness of this approach is tested on a set of the dynamic green problems instances, which are adopted in this work from the static GVRP benchmark datasets.

Dynamic vehicle routing problems (DVRPs) arise in several applications such as technician routing, meal delivery, and parcel shipping. We consider the DVRP with stochastic customer requests (DVRPSR), in which vehicles must be routed dynamically with the goal of maximizing the number of served requests. We model the DVRPSR as a multi-stage optimization problem, where the first-stage decision defines route plans for serving scheduled requests. Our main contributions are knapsack-based linear models to approximate accurately the expected reward-to-go, measured as the number of accepted requests, at any state of the stochastic system. These approximations are based on representing each vehicle as a knapsack with a capacity given by the remaining service time available along the vehicle’s route. We combine these approximations with optimal acceptance and assignment decision rules and derive efficient and high-performing online scheduling policies. We further leverage good predictions of the expected reward-to-go to design initial route plans that facilitate serving dynamic requests. Computational experiments on very large instances based on a real street network demonstrate the effectiveness of the proposed methods in prescribing high-quality offline route plans and online scheduling decisions.

: In this paper we consider the dynamic routing of unmanned aerial vehicles (UAVs) currently in operational use with the US Air Force. Dynamic vehicle routing problems (VRP) have always been challenging, and the airborne version of the VRP adds dimensions and difficulties not present in typical ground-based applications. Previous UAV routing work has focused on primarily on static, pre-planned situations; however, scheduling military operations, which are often ad-hoc, drives the need for a dynamic route solver that can respond to rapidly evolving problem constraints. With these considerations in mind, we examine the use of a Java-encoded metaheuristic to solve these dynamic routing problems, explore its operation with several general problem classes, and look at the advantages it provides in sample UAV routing problems. The end routine provides routing information for a UAV virtual battlespace simulation and allows dynamic routing of operational missions.

A comparative study between dynamic adapted PSO and VNS for the vehicle routing problem with dynamic requests

A dynamic vehicle routing problem with time-dependent travel times

The last-mile delivery problem is one of the most complex and resource-intensive aspects of modern logistics, especially within the growing e-commerce sector. As online shopping continues to expand, companies are under immense pressure to deliver goods more quickly, efficiently, and at lower costs, all while meeting the demands of increasingly time-sensitive customers. This has created a need for innovative solutions that can tackle challenges related to dynamic traffic patterns, fluctuating customer preferences, and operational constraints such as vehicle capacities and delivery windows. In response to these challenges, this paper explores the application of predictive analytics as a tool for optimizing last-mile delivery routes in real-time.The study begins by identifying the core challenges inherent in last-mile logistics, particularly in the U.S. e-commerce landscape, where the cost of last-mile delivery can represent up to 53% of total shipping costs. With traffic congestion, unpredictable customer availability, and delivery time constraints posing significant hurdles, conventional static route planning models often fall short. In this paper, predictive analytics is proposed as a solution to these challenges, utilizing real-time data to inform more efficient routing decisions. By processing vast amounts of real-time traffic data, customer preferences, and delivery constraints, predictive models can offer a more flexible and responsive approach to last-mile delivery.The research then presents a comprehensive literature review of existing route optimization methods, such as the traditional Vehicle Routing Problem (VRP) and its extensions, including VRP with Time Windows (VRPTW), Dynamic VRP (DVRP), and Capacitated VRP (CVRP). While these models have proven useful, their limitations are exposed when faced with real-time operational complexities in the e-commerce sector. Therefore, this study introduces an advanced dynamic routing model that integrates machine learning algorithms—such as decision trees and neural networks—with traditional VRP frameworks. These machine learning models, trained on historical data, are capable of predicting future traffic patterns, customer behavior, and delivery time windows.A case study is conducted using data from U.S.-based e-commerce companies to demonstrate the practical application of predictive analytics in optimizing last-mile delivery. The case study outlines how predictive models are used to dynamically adjust delivery routes based on real-time conditions, leading to significant improvements in efficiency, cost savings, and customer satisfaction. Key performance indicators such as delivery times, fuel consumption, and vehicle utilization are examined before and after the implementation of the predictive models, with the results showing a reduction in delivery time by 20% and fuel costs by 15%, alongside improved on-time delivery rates.The paper concludes by presenting the proposed dynamic route optimization model as a solution that combines the flexibility and responsiveness of predictive analytics with the robust framework of traditional VRP models. Through the integration of machine learning, real-time data processing, and dynamic routing, the model is shown to significantly improve last-mile delivery efficiency. This study's findings highlight the potential for predictive analytics to revolutionize the logistics industry, particularly in the high-demand e-commerce sector, where quick and reliable delivery is paramount. The research suggests that as e-commerce continues to grow, predictive analytics will play an increasingly critical role in ensuring that last-mile delivery is both cost-effective and responsive to the evolving needs of consumers.

A vehicle routing problem involves finding a set of optimal route for a fleet of capacitated vehicles which are available at a location to service the demands of a set of customers. In its simplest form, a customer is required to be visited once and the capacity of a vehicle must not be exceeded. In this paper, a review of four common and applicable variants of the vehicle routing problem, namely, capacitated vehicle routing problem, vehicle routing problem with time windows, periodic vehicle routing problem and the dynamic vehicle routing problem, are considered based on formulation techniques, methods of solution and areas of application. A summary table is presented for each variant to emphasis some key features that represent direction of current research.

In recent decades, research concern in vehicle routing has progressively more concentrated on dynamic and stochastic approaches in solving sophisticated dynamic vehicle routing problems (DVRP) due to its importance in helping potential customers to better manage their applications and to provide e-freight and e-commerce systems similarly. Many techniques were introduced in the DVRP field to solve part of its challenges since it’s hard to achieve equilibrium between finding feasible set of tours, minimum total travel time, shortest routing path and the capability of redirect a stirring vehicle to a new demand for additional savings; due to the fact that each one can be achieved at the expense of the other and combining them does not give an ideal result which requires a solution to track the optimum route during changes. In this paper, the game theory (GT) is integrated with Ant Colony Optimization algorithms (ACO) to adjust the attractiveness of arc and the pheromone level as it considers them competing players that prefer one according to the expected payoff. In addition, GT can be adapted inside the algorithm to facilitate the optimization, as it is considered a powerful technique in decision making and to find optimal solutions to conditions of conflict and cooperation. Experimental results show that the integration of GT with ACO algorithm improves the system performance in tackling DVRPs.

Recent advances in commercial vehicle operations (CVO), especially in communication and information technologies, allow the study of dynamic vehicle routing problems under new and updated information, such as traffic conditions and new customers. Two major operational benefits of CVO include ( a) dynamically assigning vehicles to time-sensitive demands, and ( b) efficiently rerouting vehicles according to current traffic conditions. In this research, stochastic vehicle routing problems (SVRP) are considered and extended to incorporate real-time information for dynamic vehicle routing problems. The SVRP model is formulated by a chance-constrained model and is solved by CPLEX with branch-and-bound techniques. Numerical experiments are conducted in a Taichung city network to investigate dynamic vehicle routing strategies under real-time information supply strategies and to assess the effectiveness of such strategies in a dynamic perspective.

Dynamic rerouting of a fleet of vehicles in agricultural operations through a Dynamic Multiple Depot Vehicle Routing Problem representation

A dynamic approach for the multi-compartment vehicle routing problem in waste management

Over the years, several variations of the dynamic vehicle routing problem (DVRP) have been considered due to its similarities with many real-world applications. Several methods have been applied to address DVRPs, in which ant colony optimization (ACO) has shown promising results due to its adaptation capabilities. In this chapter, we generate another variation of the DVRP with traffic factor and propose a memetic algorithm based on the ACO framework to address it. Multiple local search operators are used to improve the exploitation capacity and a diversity scheme based on random immigrants is used to improve the exploration capacity of the algorithm. The proposed memetic ACO algorithm is applied on different test cases of the DVRP with traffic factors and is compared with other peer ACO algorithms. The experimental results show that the proposed memetic ACO algorithm shows promising results.

The dynamic vehicle routing problem, DVRP, consists in optimally routing a fleet occur at random. The optimization consists in finding the solution that minimizes the total length of tours and satisfying the customer delivery time window. In this paper, we present a hybrid ant system to handle the dynamism by means of modifying the pheromone matrix and candidate sets strategy. In addition, we make further improvements on vehicle routes with the local search heuristics. The results show that the hybrid ant system works effectively to find competitive solutions.

Delivery services have reached an all-time high in terms of demands due to the COVID-19 pandemic. An optimal routing plan for these different courier services is a must. The vehicle routing problem (VRP) is an NP-hard problem in logistics research and it can be solved by exact algorithms, heuristics, and machine learning through reinforcement learning. This study introduces a reoptimization alternative as opposed to trivial Q-learning retraining on a newly introduced subproblem of dynamic VRP and VRPPD, the dynamic vehicle routing problem with pickup, delivery, and cancellation (DVRPPDC). The reoptimization technique is called Floyd-Warshall LookUp Reoptimization of Rewards Yearned (FLURRY). Combined with a one-time Q-learning computation beforehand, the Q matrix produced is updated at the cell level by a lookup table containing all the shortest paths among the pairs of parcel lockers. The lookup table is generated via Floyd-Warshall algorithm, a well-known shortest path algorithm. Upon testing on a new dataset for DVRPPDC, one-time Q-learning combined with FLURRY is 6.1x to 10.6x faster to compute than Q-learning retraining. Furthermore, an additional study done after the main series of experiments reveal that the methodology does not necessitate any Q-learning training at the first time step at all. FLURRY can be done on a Q matrix of zeroes and achieve the same path output and traversal time as Q-learning with FLURRY. The standalone FLURRY algorithm further speeds up the computation, compared to the naive Q-learning approach, from 6.1x – 10.6x to 26.5x – 117.5x.