Vehicle Routing Research Articles

A Model for Vehicle Routing Problem under Returns and Emission Consideration in B2C E-Commerce Logistics

Purpose: With the widespread use of the Internet, electronic commerce allows customers to purchase products from the virtual stores of businesses instead of physical stores. This study addressed a mixed-integer linear programming model for a vehicle routing problem under returns and emission considerations in B2C e-commerce logistics. Methodology: This study proposes a mathematical model to solve a variant of the vehicle routing problem. The objective is to minimize the fuel consumption cost, penalty cost for unmet demand of returned items, and fixed cost for operating a vehicle. A clustering-based solution algorithm has been introduced to solve large-sized instances within reasonable solution times. Findings: The numerical analysis for the base case shows that the suggested model can assist decision-makers in coordinating forward distribution and reverse collection decisions within the context of sustainable e-commerce logistics. The result of the adjusted model for minimizing emission shows that a reduction of nearly 17% in total emission amount can be achieved, however, the adjusted model postpones all demand for the collection of returned items. Furthermore, the cluster-based solution approach causes a considerable decrease in solution time while providing promising solutions. Originality: This study represents a contribution to the existing literature on the subject by considering: i) emission to determine effects on the vehicle routing problem, ii) the postponement of the collection of returned items due to the limited delivery time, iii) proposing the clustering-based solution approach to tackle with larger-sized problems.

Drone‐enabled material handling in smart manufacturing

AbstractAmidst labor shortages, increasing land prices, and other aspects of the post‐pandemic world, our logistics chain must consider new technological alternatives. Automated material handling equipment often requires valuable floor space and specialized layouts. Uncrewed aerial vehicles, popularly called drones, offer a flexible and cost‐effective alternative. To explore the potential benefits and challenges of such systems, we used a version of the multi‐trip vehicle routing problem with time windows to model two realistic manufacturing environments as use cases. We provide the mixed integer linear programming formulation of this problem and compare results with those of a human‐only based system in a detailed simulated environment. Furthermore, as a physical proof‐of‐concept, we outfitted a commercially available drone with pick‐up and carry capabilities. We found that, even with limited drone carrying capacity, there are economic benefits realized from time savings, compared to ground‐based material handling systems. Besides these time savings, drones are sustainable as they operate on battery power and do not impair the air quality or the traffic experienced on the ground.

Model Proposal for Ammunition Demand Forecasting and Ammunition Distribution Network Design: Military Unit Application

During the war, the safe provision of the required ammunition at the desired place and time can affect the fate of the war and even the country. Determining the amount of ammunition to be needed in the war is as important as planning its distribution. Two problems are important research topics in the field of military logistics. A two-stage methodology is presented in the study to solve these problems. In the first stage, an analytical approach based on suitability tests is presented to estimate the amount of ammunition that military artillery units will need based on past general data, and ammunition requirements for possible battles are estimated. In the second stage, a distribution network design is carried out for the distribution of ammunition from the ammunition depot to the deployed artillery units. This distribution network also includes reverse logistics activities, as it is necessary to collect the waste left by the ammunition used by the artillery units. In order to achieve this in the study, a Multi-Period Multi Depot Simultaneous Pick Up&Delivery Vehicle Routing Problem Model has been developed. The results obtained as a result of the solution of the model have been examined in detail and analyzed by comparing with the current situation.

Discrete optimization model for multi-product multi-supplier vehicle routing problem with relaxed time window

This study examines the complicated logistics optimization issue known as the vehicle routing problem for multi-product and multi-suppliers(VRP-MPMS), which deals with the effective routing of a fleet of vehicles to convey numerous items from multiple suppliers to a set of consumers. In this problem, products from various suppliers need to be delivered to different customers while considering vehicle capacity constraints, time windows, and minimizing transportation costs. We propose a hybrid approach that combines a generalized reduced gradient method to identify feasible regions with a feasible neighborhood search to achieve optimal or near-optimal solutions. The aim of the exact method is to get the region of feasible solution. Then we explore the region using feasible neighborhood search, to get an integer feasible optimal (suboptimal) solution. Computational experiments demonstrate that our model and method effectively reduce transportation costs while satisfying vehicle capacity constraints and relaxed time windows. Our findings provide a viable solution for improving logistics operations in real-world scenarios.

Vehicle routing in one-way carsharing service with ridesharing options: A variable neighborhood search algorithm

Vehicle routing in one-way carsharing service with ridesharing options: A variable neighborhood search algorithm

Routing of autonomous vehicles for optimal total system cost under equilibrium

Routing of autonomous vehicles for optimal total system cost under equilibrium

The two-echelon truck-unmanned ground vehicle routing problem with time-dependent travel times

The two-echelon truck-unmanned ground vehicle routing problem with time-dependent travel times

A Deep Reinforcement Learning-Based Adaptive Large Neighborhood Search for Capacitated Electric Vehicle Routing Problems

A Deep Reinforcement Learning-Based Adaptive Large Neighborhood Search for Capacitated Electric Vehicle Routing Problems

A comparison of reinforcement learning policies for dynamic vehicle routing problems with stochastic customer requests

This paper presents directions for using reinforcement learning with neural networks for dynamic vehicle routing problems (DVRPs). DVRPs involve sequential decision-making under uncertainty where the expected future consequences are ideally included in current decision-making. A frequently used framework for these problems is approximate dynamic programming (ADP) or reinforcement learning (RL), often in conjunction with a parametric value function approximation (VFA). A straightforward way to use VFA in DVRP is linear regression (LVFA), but more complex, non-linear predictors, e.g., neural network VFAs (NNVFA), are also widely used. Alternatively, we may represent the policy directly, using a linear policy function approximation (LPFA) or neural network PFA (NNPFA). The abundance of policies and design choices complicate the use of neural networks for DVRPs in research and practice. We provide a structured overview of the similarities and differences between the policy classes. Furthermore, we present an empirical comparison of LVFA, LPFA, NNVFA, and NNPFA policies. The comparison is conducted on several problem variants of the DVRP with stochastic customer requests. To validate our findings, we study realistic extensions of the stylized problem on (i) a same-day parcel pickup and delivery case in the city of Amsterdam, the Netherlands, and (ii) the routing of robots in an automated storage and retrieval system (AS/RS). Based on our empirical evaluation, we provide insights into the advantages and disadvantages of neural network policies compared to linear policies, and value-based approaches compared to policy-based approaches.

Planning of truck platooning for road-network capacitated vehicle routing problem

Planning of truck platooning for road-network capacitated vehicle routing problem

Green Vehicle Routing Problem Optimization for LPG Distribution: Genetic Algorithms for Complex Constraints and Emission Reduction

Green Vehicle Routing Problem Optimization for LPG Distribution: Genetic Algorithms for Complex Constraints and Emission Reduction

A Reinforcement Learning-Based Solution for the Capacitated Electric Vehicle Routing Problem from the Last-Mile Delivery Perspective

The growth of the urban population and the increase in e-commerce activities have resulted in challenges for last-mile delivery. On the other hand, electric vehicles (EVs) have been introduced to last-mile delivery as an alternative to fossil fuel vehicles. Electric vehicles (EVs) not only play a pivotal role in reducing greenhouse gas emissions and air pollution but also contribute significantly to the development of more energy-efficient and environmentally sustainable urban transportation systems. Within these dynamics, the Electric Vehicle Routing Problem (EVRP) has begun to replace the Vehicle Routing Problem (VRP) in last-mile delivery. While classic vehicle routing ignores fueling, both the location of charging stations and charging time should be included in the Electric Vehicle Routing Problem due to the long recharging time. This study addresses the Capacitated EVRP (CEVRP) with a novel Q-learning algorithm. Q-learning is a model-free reinforcement learning algorithm designed to maximize an agent’s cumulative reward over time by selecting optimal actions. Additionally, a new dataset is also published for the EVRP considering field constraints. For the design of the dataset, real geographical positions have been used, located in the province of Eskisehir, Türkiye. It also includes environmental information, such as streets, intersections, and traffic density, unlike classical EVRP datasets. Optimal solutions are obtained for each instance of the EVRP by using the mathematical model. The results of the proposed Q-learning algorithm are compared with the optimal solutions of the presented dataset. Test results show that the proposed algorithm provides remarkable advantages in obtaining routes in a shorter time for EVs.

Data-Driven Order Consolidation with Vehicle Routing Optimization

This study compares time-based and quantity-based consolidation strategies within the Vehicle Routing Problem (VRP) framework to optimize supplier profitability and logistical efficiency. The time-based model consolidates deliveries at fixed intervals, offering predictable routes, reduced customer wait times, and cost efficiency in stable markets. Conversely, the quantity-based model dynamically adjusts delivery volumes to meet fluctuating demand, providing flexibility in dynamic environments but potentially increasing long-term costs due to logistical complexity. Using a mixed-integer linear programming (MILP) model, sensitivity analyses, and scenario-based experiments, the study demonstrates that the time-based model excels in stable conditions, while the quantity-based model performs better in highly variable demand scenarios. These findings provide actionable insights for selecting consolidation strategies that optimize delivery operations and enhance supply chain performance based on market dynamics.

Multi-strategy ant colony optimization with k-means clustering algorithm for capacitated vehicle routing problem

Multi-strategy ant colony optimization with k-means clustering algorithm for capacitated vehicle routing problem

Vehicle-routing problem for low-carbon cold chain logistics based on the idea of cost–benefit

In the low-carbon economy, the fresh industry constitutes an “impossible triangle” in products, prices and services. Therefore, based on the idea of cost–benefit, a comprehensive vehicle routing problem optimization model with the objective function of minimizing the cost of unit satisfied customer is presented. Then, a hybrid algorithm called local search genetic algorithm (LSGA) is proposed, which amalgamates the destroy-repair operator with GA algorithm. Extensive numerical experiments verify the feasibility and effectiveness of the proposed model and algorithm. Furthermore, the sensitivity analysis of freshness-keeping cost, carbon price and customer satisfaction weights were conducted. The experimental results show that appropriate freshness-keeping effort can reduce total costs and improve customer satisfaction. Increasing carbon price within a certain range can effectively reduce carbon emissions, and there is a trade-off relationship between carbon emissions and customer satisfaction. The results of considering both time satisfaction and freshness satisfaction are better than considering time satisfaction alone.

A memetic algorithm for the flexible periodic vehicle routing problem

A memetic algorithm for the flexible periodic vehicle routing problem

Solution of the Capacity-Constrained Vehicle Routing Problem Considering Carbon Footprint Within the Scope of Sustainable Logistics with Genetic Algorithm

Solution of the Capacity-Constrained Vehicle Routing Problem Considering Carbon Footprint Within the Scope of Sustainable Logistics with Genetic Algorithm

"Application of Genetic Algorithms to Solve the Clustered Generalized Traveling Salesman Problem"

"This paper explores the application of genetic algorithms (GAs) to solve the Traveling Salesman Problem (TSP) and its variants, specifically the Clustered Generalized Traveling Salesman Problem (CGTSP). In CGTSP, nodes (or cities) are grouped into clusters, and the primary objective is to determine the most efficient route that includes precisely one node from each cluster while minimizing the overall travel distance This particular variation plays a crucial role in practical scenarios, such as logistics, vehicle routing, and network design. In these applications, destinations are naturally grouped, and visiting one representative from each group is often essential or mandatory. CGTSP effectively tackles the complexity and real-world constraints more adeptly than the traditional TSP by integrating the element of clustering. This feature makes it a versatile and applicable model for a wide range of industrial and scientific problems. The study aims to assess the effectiveness of Genetic Algorithms (GAs) in solving these complex optimization problems. The paper provides an overview of the TSP and CGTSP, shows how to rewrite a CGTSP in the form of a TSP and vice versa, discusses the fundamentals of GAs, and presents the application of GAs to the problem variants. In addition, we investigate a new method to generate the initial population in the genetic algorithm and evaluate the proposed algorithm’s performance through experimental results. The findings highlight the potential of GAs as powerful tools for solving challenging optimization problems."

Q-Learning-Driven Butterfly Optimization Algorithm for Green Vehicle Routing Problem Considering Customer Preference.

This paper proposes a Q-learning-driven butterfly optimization algorithm (QLBOA) by integrating the Q-learning mechanism of reinforcement learning into the butterfly optimization algorithm (BOA). In order to improve the overall optimization ability of the algorithm, enhance the optimization accuracy, and prevent the algorithm from falling into a local optimum, the Gaussian mutation mechanism with dynamic variance was introduced, and the migration mutation mechanism was also used to enhance the population diversity of the algorithm. Eighteen benchmark functions were used to compare the proposed method with five classical metaheuristic algorithms and three BOA variable optimization methods. The QLBOA was used to solve the green vehicle routing problem with time windows considering customer preferences. The influence of decision makers' subjective preferences and weight factors on fuel consumption, carbon emissions, penalty cost, and total cost are analyzed. Compared with three classical optimization algorithms, the experimental results show that the proposed QLBOA has a generally superior performance.

A neighborhood search-based heuristic for the dynamic vehicle routing problem

A neighborhood search-based heuristic for the dynamic vehicle routing problem