Vehicle Routing Problem Model Research Articles

Decision-Making Support in Vehicle Routing Problems: A Review of Recent Literature

The Vehicle Routing Problem (VRP) involves a set of customers with known locations, each having a certain demand for goods or services. There is also a fleet of vehicles available, each with limited capacity and often a fixed starting point. With the aid of mathematical programming tools, this paper offers an overview of the most recent VRP research. This study also examines the algorithms for solving VRP models and categorizes them in terms of their application areas. For these reasons, related publications that appeared in the international journal have been compiled and studied. According to the literature review, multi-criteria decision-making (MCDM) techniques have yet to constitute the most mathematical programming methods used to solve the VRP problem.

Multi-objective Optimization for Green Delivery Routing Problems with Flexible Time Windows

ABSTRACT This paper presents a model and heuristic solution algorithms for the Green Vehicle Routing Problem with Flexible Time Windows. A scenario of new vehicle routing is analyzed in which customers are asked to provide alternative time windows to offer flexibility to help route planners find more fuel-efficient routes (“green delivery”). Customers can rank their preferred time windows as first, second, and third. The optimization model aims to reduce tour costs, promote electromobility over fossil fuels, such as diesel, and meet customer preferences when possible and affordable. The study incorporates a multi-objective optimization model with three objectives, which are overall cost, use of fossil fuel, and customer satisfaction. For the new problem, a set of realistic benchmark problems is created and four mainstream solvers are applied for the Pareto front approximation: NSGA-II, NSGA-III, MOEA/D, and SMS-EMOA. These algorithms are compared in terms of their effectiveness in achieving the objectives of minimizing travel costs, promoting electromobility, and meeting customer preferences. The study uses five different problems of single-vehicle route planning. Two major findings are that the selection of the metaheuristic can make a big difference in terms of algorithm performance. The resulting 3-D Pareto fronts reveal the nature of this new class of problems: Interestingly, in the new model with flexible time windows, most users can still be delivered in their most preferred time windows with only small concessions to the other objectives. However, using only one time window per user can lead to an increasingly drastic cost and fossil fuel consumption.

Three-stage multi-modal multi-objective differential evolution algorithm for vehicle routing problem with time windows

In this paper, the mathematical model of Vehicle Routing Problem with Time Windows (VRPTW) is established based on the directed graph, and a 3-stage multi-modal multi-objective differential evolution algorithm (3S-MMDEA) is proposed. In the first stage, in order to expand the range of individuals to be selected, a generalized opposition-based learning (GOBL) strategy is used to generate a reverse population. In the second stage, a search strategy of reachable distribution area is proposed, which divides the population with the selected individual as the center point to improve the convergence of the solution set. In the third stage, an improved individual variation strategy is proposed to legalize the mutant individuals, so that the individual after variation still falls within the range of the population, further improving the diversity of individuals to ensure the diversity of the solution set. Based on the synergy of the above three stages of strategies, the diversity of individuals is ensured, so as to improve the diversity of solution sets, and multiple equivalent optimal paths are obtained to meet the planning needs of different decision-makers. Finally, the performance of the proposed method is evaluated on the standard benchmark datasets of the problem. The experimental results show that the proposed 3S-MMDEA can improve the efficiency of logistics distribution and obtain multiple equivalent optimal paths. The method achieves good performance, superior to the most advanced VRPTW solution methods, and has great potential in practical projects.

Vehicle Routing Problem as a Solution for Determining Goods Delivery Routes PT. Kreasi Beton Nusa Persada

VRP distributions have had difficulty overcoming the problem of finding channels with minimal depots to locations that have different places with different total demand. The purpose of this study is to analyze the problem of transportation routes in the distribution of products obtained from the initial location of distribution to users. This type of research is qualitative research. This research was conducted at PT. Nusa Persada Concrete Creations. The Nearest Neighbor method is used to determine the distribution of routes. The Local Search method is carried out to evaluate and improve the distribution of routes carried out at the beginning with the Nearest Neighbors method. The data analysis process consists of several stages with the Nearest Neighbor method and the LocalSearch method. The results of the study, namely the Model Vehicle Routing Problem (VRP) applied in determining ready mix delivery routes at PT. Nusapersada Concrete Creation using nearest and local neighbor methods. Vehicle Routing Problem (VRP) models using nearest and local neighbor methods can be used applied in determining ready mix delivery routes to limited companies. Nusapersada Concrete Creations. This makes distance and time more effective, as well as more cost efficient. New routes generated This is a route improvement solution that PT. The application of the Nusapersada Concrete Creations model results in a new route that reduces the distance closer, faster completion time, and fuel cost savings for truck vehicles compared to the initial route. This makes distance and time more effective, as well as more cost efficient.

Penerapan Algoritma Sweep dan Particle Swarm Optimization (PSO) sebagai Alternatif Menentukan Rute Distribusi

<em><span lang="EN-ID">One aspect of marketing activities is distribution. In the process of distributing goods, it is important to determine the optimal route that minimize mileage and reduce costs. This study aims to provide alternative solutions in determining distribution routes with the shortest distance which has implications for shorter travel times and lower costs. This research adapts the Capacitated Vehicle Routing Problem (CVRP) model with the approach of sweep and Particle Swarm Optimization (PSO) algorithm to determine the route. To generate a comparison route, we use the Nearest Neighbor (NN) algorithm. The result was that 100 agents were divided into 6 clusters and the total distance of the PSO-generated route is 218.115 units or 85.70% of the route distance generated by Nearest Neighbor algorithm</span></em><span lang="EN-ID">.</span>

An integrated vehicle routing model to optimize agricultural products distribution in retail chains

The Vehicle Routing Problem (VRP) represents a thoroughly investigated domain within operations research, yielding substantial cost savings in global transportation. The fundamental objective of the VRP is to determine the optimal route plan that minimizes the overall distance traveled. This study employs VRP to address the challenge of distributing fresh agricultural products within retail chains. It introduces an integrated bi-objective VRP model that concurrently optimizes resource allocation, order scheduling, and route planning. The proposed model incorporates two objective functions with the goals of minimizing total distribution costs and ensuring timely product deliveries to retail outlets. Real-world characteristics are integrated to enhance practical applicability. All solution algorithms and the developed VRP model undergo testing using data from one of Sri Lanka's largest retail chains. Numerical experiments showcase the efficiency of the proposed algorithm in solving real-world VRP problems. Moreover, the proposed VRP model achieves a 19% reduction in daily distribution costs, including a 24% saving in fuel costs. This not only provides financial benefits but also contributes to the reduction of the carbon footprint of retail chains. The model ensures on-time deliveries to 95% of retail outlets, which is crucial for maintaining the quality of fresh food. The findings of this study underscore the significant cost savings, enhanced sustainability, and improved quality associated with the efficient distribution of fresh agricultural products within retail chains.

A Review of the Multitasking Allocation Model for Vertical Farming System

Vertical Farming System (VFS) can produce food efficiently in times of dramatic climate change, regional conflicts that affect food transportation, or space exploration. The high degree of automation in the system, a large number of sensors and robots work together, so an efficient task allocation model can significantly enhance operational efficiency, reduce energy consumption, and elevate the overall economic performance of VFS.With this background, the purpose of this review is to describe the current available multi-task allocation models applicable to VFS or related contexts. Through a literature analysis over the past 5 years, researchers have developed and analyzed numerous multi-task allocation models in the relevant fields, such as Multi-Objective Knapsack Problem (MOKP) models and Vehicle Routing Problem (VRP) models. Depending on the nature of the problem and the constraints of the environment, diverse types of multi-task allocation models have been proposed. Tasks generated within the VFS must be completed within specified time limits, and certain tasks require collaborative efforts among different robots. Taking into consideration these task requirements and drawing upon existing multi-task allocation models proposed by other researchers, this review propose the VFSRTP (VFS Robot Task Planning) model. It is expected that this work will have a positive impact on the research of VFS multi-task allocation model, and provide some model descriptions and guidelines.

A novel collaborative electric vehicle routing problem with multiple prioritized time windows and time-dependent hybrid recharging

Due to global warming and environmental deterioration, the development of electric vehicles (EVs) is seen as a key measure to promote energy saving and emission reduction in logistics industries. However, EVs’ limited driving range and long recharging time make them unable to be widely applied in logistics distribution activities. Thus, this study establishes a model for a novel collaborative electric vehicle routing problem with multiple prioritized time windows and time-dependent hybrid recharging to address the above difficulties. The model quantifies and incorporates the priority of different time windows into costs to better satisfy customer demands while minimizing the total costs. A time-dependent hybrid recharging strategy that integrates three modes of in-station fast, in-station normal, and en-route battery swapping with collaborative mobile battery swapping vans (BSVs) is presented to reduce the impact of recharging time on route decision-making. The possible queuing time due to the limited capacity of recharging stations is considered to simulate the practical application scenarios. To effectively solve the complex model of synchronous optimization of EV and BSV routes, an extended adaptive large neighborhood search algorithm with two-dimensional coding scheme is proposed. In the proposed algorithm, several removal and insertion operators are integrated to effectively evolve the solutions, and the variable neighborhood descent algorithm is combined with local search operators to further improve the algorithmic performance. Comparison experiments were conducted to verify the effectiveness of the proposed algorithm by comparing with the other baseline algorithms.

Optimization of vehicle routing problem using guided local search and simulated annealing

Transportation concerns in the supply chain are important because they have the potential to greatly raise logistical requirements. The proposed solution to the issue is the vehicle routing problem. The Heterogeneous Fleet Vehicle Routing Problem (HFVRP) model will be implemented in this study using the metaheuristic methodology together with the Guided Local Search (GLS) and Simulated Annealing (SA) approaches for case studies. Python 3.10 software was used to analyze the SA and GLS, and the Google OR-Tools module was used. Implementing the SA and GLS algorithm based on data from the case study in a logistics company involved in the distribution of chicken feed and gas is the goal of this project. By doing so, it will be possible to determine whether the current situation is ideal or if it could be improved to reduce the number of vehicles used, find the best route, lower shipping costs, and increase efficiency. The findings of this study show that when compared to the GLS algorithm, research employing SA produces better outcomes in terms of cost and route management. According to actual data, SA increases income by an average amount between 3% and 10%.

Cloud‐fog assisted human tracing and disaster evacuation framework

AbstractMany kinds of natural hazards and man‐made calamities have occurred in recent years, each of which has left a huge number of victims and has been challenging to deal with. The solution is an effective evacuation planning system, which is gaining the interest of governments, academic institutions, and industries all across the world. This study focuses on quick and organized evacuation from densely populated areas during disasters to decrease damage successfully. This research proposes a fog‐cloud‐assisted effective disaster evacuation framework. The data accumulation layer uses IoT, mobile networks, and GPS technologies to collect environmental and location‐based data to track the inhabitants. The fog layer is utilized for (i) disaster event detection and (ii) human tracing and crowd density analysis. A cloud layer makes it easier for an evacuation algorithm to produce an evacuation map that will point evacuees to the exit while computing a quick and safe path utilizing environmental and inhabitant information gathered from the fog. The findings reveal that the performance of Model A surpasses the split delivery vehicle routing problem model with the minimum demand of vehicles and with improvement of 38.1% average. Also, A* takes less time (18.3 s) than other state‐of‐the‐art techniques to find the best optimal route for evacuation. Implementation and performance analysis demonstrate the efficiency and effectiveness of the proposed framework.

Dynamics between warehouse operations and vehicle routing

When scheduling the distribution of ordered items from a warehouse to customers, the transportation planning is generally done first and serves as input for planning warehouse operations. Such a sequential approach can lead to substantial inefficiencies when the customer deliveries are restricted by time windows, and the warehouse has limited resources available (both order pickers and space in the staging area). This paper studies the trade‐offs between warehouse operations and transportation planning. The goal is to understand the impact of three specific managerial interventions: adopting an integrated planning approach, expanding the available staging space, and expanding the delivery time windows. To this end, we propose a mathematical model for a general vehicle routing problem that incorporates order batching, order picker scheduling, staging, and vehicle loading. We introduce a novel idea to express the picking time of an order batch as a function of the batch size and develop a metaheuristic to solve this integrated problem. Furthermore, we develop exact algorithms to provide optimal solutions for the individual warehouse and transportation problems in a sequential planning approach. Managerial insights are distilled from case studies in two warehouses, one for ambient products and the other for refrigerated products, of a leading grocery retailer in the Netherlands. Our results show that integrated planning outperforms the other managerial interventions and generates cost savings between 9% and 11%. Savings are generally realized by executing larger order batch sizes to be picked in the warehouses at the expense of additional routing cost (around 2–3%). The second intervention in the form of time window expansions of only 15 minutes for customer deliveries can lead to cost savings between 4% and 6%, which results from a reduction in both transportation and warehousing cost. Expanding the capacity of the staging area is only meaningful when the staging space is highly utilized and only results in cost savings for the warehouse operations.

Vehicle routing problem optimization with machine learning in imbalanced classification vehicle route data

The object of this research is a combinatorial optimization problem arising in the problem of the route of goods delivery vehicles. In this study, the proposed method for solving combinatorial optimization problems consists of several stages: Data Cleaning, Data Preprocessing, K-NN and Cavacity Vehicle Routing Problem model. The results show that the machine learning approach can optimise combinatorial optimization problems, especially in generating vehicle route points and delivery capacity. The characteristics in determining vehicle routes by considering latitude and longitude points. This research builds a framework and implements it in a multi-class optimization model to reduce overfitting and misclassification results caused by unbalanced multiclassification from the influence of the number of 'nodes' on vehicle routes with machine learning. The purpose of the model in general is to gain an understanding of the mechanism in the problem so that it can classify unbalanced vehicle route data based on Jalur Nugraha Ekakurir delivery routes. So that with the availability of the model can be a model in determining vehicle routes based on the capacity limit of the number of shipments of goods. The results of research with machine learning models and vehicle routing problems with testing K values 11, 13, 15. Where it has a percentage of K=11 accuracy 57.3265 % and K=13 accuracy 57.3265 % and K=15 accuracy 81.8645 %. From the test results with odd K values have better accuracy and the K 15 K=15 value is better with a percentage of 81.8645 % compared to K 11 K=11, and 13 K=13. As a result, the developed model in terms of accuracy of the cavacity vehicle routing problem model has an accuracy of 93.80 % and the time series achieves an average precision of 93.31 % and with a recall value of 93.80 %. The results obtained can be useful in developing a more modern model, Cavacity Vehicle Routing Problem with Machine Learning

Providing an integrated multi-depot vehicle routing problem model with simultaneous pickup and delivery and package layout under uncertainty with fuzzy-robust box optimization method

This paper modeled and solved an integrated multi-depot vehicle routing problem (MDVRP) with simultaneous pickup and delivery (SPD) with package layout under unpredictable pickup, delivery, and transfer costs. The model described in this paper is divided into two stages. In the first stage, the SCA algorithm is used to optimize the package dimensions (a collection of commodities consumers need). The NSGA II and MOALO algorithms are used in the second stage to optimize the three objective functions of 1 simultaneously) minimizing total costs, 2) minimizing co2 emissions, and 3) minimizing the maximum working hours of drivers based on the optimal dimensions (length, width, and height) obtained from solving the first stage model. Determining the quantity and ideal location of possible warehouses, the best route for trucks to take to deliver and collect customer items, and the distribution of customers to warehouses are the key goals of the second stage. Since the model is unclear, the problem's uncertainty parameters are controlled using a novel fuzzy-robust box optimization (FRBO) technique. This technique, which combines the advantages of fuzzy programming with robust box-based optimization, produces excellent results when used to optimize objective functions. The numerical calculations in the numerical example show that the total network costs and CO2 emissions increased in the second stage in the presented model with an increasing uncertainty rate. At the same time, the maximum working hours of drivers decreased due to the shortened communication route and the number of vehicles increasing. Finally, the MOALO algorithm was used to resolve a case study at Safir Broadcasting Company because of its excellent efficiency in resolving the created model, the findings of which revealed the presence of 13 potential effective solutions. The quantity of greenhouse gas emissions rose by 1.11%, the overall expenditures climbed by 1.72%, and the number of hours that drivers worked fell by 11.98% when the uncertainty rate was raised from 0.5 to 0.7, according to research on the FRBO.

Accessible taxi routing strategy based on travel behavior of people with disabilities incorporating vehicle routing problem and Gaussian mixture model

The government of Seoul has implemented accessible taxis, following the Convention on the Rights of Persons with Disabilities established by the United Nations in 2006. Accessible taxis are an emerging transportation mode that improves transportation accessibility for people with disabilities (PWD). The main idea of this study is to classify PWD into short-distance and long-distance groups and operate corresponding short-distance and long-distance taxis to improve the operational efficiency and user convenience of accessible taxi services. This study aims to find an optimal routing strategy for accessible taxis considering the travel behavior of PWD. Specifically, the travel distance distribution of PWD was divided into multiple distributions, i.e., short-distance and long-distance distributions, using Gaussian Mixture Model. Then, a vehicle routing problem with time windows was developed to find the optimal routing strategy for accessible taxis. The objective function was set to minimize users’ total wait time, and the decision variables included the number of user classifications, the number of accessible taxis, and the route for each vehicle. The results of the proposed strategy showed about an 8.0% reduction in total wait time and a 1.3% reduction in the total operating distance of accessible taxis compared to the current strategy. Specifically, the wait time of the short-distance user group with a distance of less than 4.3 km was reduced by about 26%. An economic benefit analysis was also performed to identify the applicability of the proposed strategy, and the results showed that the proposed model was economically beneficial.

Direct and indirect emissions: a bi-objective model for hybrid vehicle routing problem

Direct and indirect emissions: a bi-objective model for hybrid vehicle routing problem

Last mile delivery Optimisation model for drone-enabled Vehicle Routing Problem

In light of e-commerce's exponential growth trajectory over recent years, it has never been more critical to evaluate the last-mile delivery segment for its efficiency and cost-effectiveness. Autonomous vehicles (drones) offer considerable promise as potential solutions with ongoing investigations into emerging technologies in this context. To address problems related to last mile-delivery in logistics operations, the practicality of adopting a hybrid truck-drone delivery system is examined through this study. The researchers utilized Mixed-integer linear programming (MILP)and Gurobi optimization solvers both for optimizing performance as well as facilitating execution. While testing a drone dataset of a well-known logistics company as part of their research using an optimization model, the findings suggested that were remarkable competitive advantages including significant gains in reduction of timing. Nevertheless, there are several constraints like maintenance, recharging, difficult weather conditions & traffic congestion-necessitating focused innovative AI-based approaches. In spite of these impediments, a hybrid truck-drone’s potential applicability can remarkably boost the efficiency of last-mile delivery operations.

Multi-objective medical supplies distribution open vehicle routing problem with fairness and timeliness under major public health emergencies

Fair and timely delivery of supplies plays a critical role under major public health emergencies. In this paper, aiming at fairness and timeliness, an optimization model of open vehicle routing problem for medical supplies distribution is established considering the urgency of the demand. We adopt a differential evolutionary algorithm with fast non-dominated solution sorting to solve the proposed model, obtaining an approximate Pareto optimal solution set. Through the comparison of algorithms, the results showed that the differential evolutionary algorithm with non-dominated sorting is superior with a shorter runtime and more diverse solutions, while the epsilon constraint method has more accurate solutions. In the case verification, the quality of the solutions of both algorithms was within the acceptable range, but the runtime of the epsilon constraint method was too long to be applicable. The results can provide theoretical suggestions and practical guidance for decision-makers in emergency supplies distribution.

Optimization Model for Electric Vehicle Routing Problem with Two Charging Options

The use of electric vehicles recently is an alternative way to lower pollutants and the emissions of carbon dioxide resulting from the usage of motor vehicle fuel oil. The limited battery capacity of this electric vehicle is a problem for its users. Vehicle routing problem (VRP) is a problem of integer programming and combinatorial optimization that is frequently used in planning and decision-making processes. One application of this issue is to find the best path for delivering items from a corporation to customers. VRP problems are frequently utilized in order to reduce internal expenditure. Model optimization in this paper uses two rehcarging options, recharging and swapping battery. The purpose of this paper is to present a VRP optimization model for electric vehicle routing problems to find the best route option that minimizes the total of the fees for fixed vehicles, transit, charging, battery changing, and waiting.
  

Environmental Benefits of Using New Last-Mile Solutions and Using Electric Vehicles in China

Unlike much of the existing research into the environmental impacts of home shopping and delivery activities, which focus on evaluating the operational efficiency of home delivery failures, this paper aims to investigate the CO2 emissions associated with treatments of failed-delivery parcels. The paper considers the potential environmental profits from using some innovative home delivery solutions: 1) the utilization of new home delivery solutions, under which the self-pickup point (SPP)/smart cabinet (SCB) is used as an intermediate address for customer’s collection and 2) upgrading fuel-powered goods vehicles to battery vans to make deliveries. Two sets of vehicle routing problem models for fuel-powered vans and battery vans are established. A sample network comprising of 500 customers and several SPPs/SCBs in Beijing, China, is proposed in the case study, to verify the proposed models and heuristics. The numerical results suggest that: 1) reductions in CO2 per average parcel delivered by new home delivery solutions increase with the ratio of home delivery failures, 2) it is proved that using battery freight vehicles obtains more environmental benefits than using the fuel-powered vans, and 3) customers’ personal trips for collecting their failed-delivery packages from the local depot or SPPs/SCBs share a large proportion of CO2 per parcel. The research has practical implications for both government and industry practitioners. Possible suggestions include: 1) promote a cleaner transportation mode for residents, by encouraging them to either take the bus or drive a battery passenger car, 2) careful design of a combined SPP and SCB network in urban areas, and 3) accelerate the process of vehicle electrification in the logistics industry by issuing allowances for purchasing electric vehicles from the government.

Evolving Dispatching Rules for Dynamic Vehicle Routing with Genetic Programming

Many real-world applications of the vehicle routing problem (VRP) are arising today, which range from physical resource planning to virtual resource management in the cloud computing domain. A common trait of these applications is usually the large scale size of problem instances, which require fast algorithms to generate solutions of acceptable quality. The basis for many VRP approaches is a heuristic which builds a candidate solution that may subsequently be improved by a local search procedure. Since there are many variants of the basic VRP model, specialised algorithms must be devised that take into account specific constraints and user-defined objective measures. Another factor is that the scheduling process may be carried out in dynamic conditions, where future information may be uncertain or unavailable or may be subject to change. When all of this is considered, there is a need for customised heuristics, devised for a specific problem variant, that could be used in highly dynamic environments. In this paper, we use genetic programming (GP) to evolve a suitable dispatching rule to build solutions for different objectives and classes of VRP problems, applicable in both dynamic and stochastic conditions. The results show great potential, since this method may be used for different problem classes and user-defined performance objectives.