Vehicle Routing Problem Model Research Articles

Vehicle Routing Problem Model with Practicality

Truck platooning has recently become an essential issue in automatic driving. Though truck platooning can increase safety and reduce fuel consumption and carbon emissions, the practical vehicle routing problem involved in truck platooning has not been sufficiently addressed. Therefore, we design a mixed-integer linear programming model for the routing problem in truck platooning considering the deadline of vehicles, continuous-time units, different fuel reduction rates, traffic congestion avoidance, and heterogeneous vehicles. In addition, a forward–backward heuristic called the “greedy heuristic” is presented for reasonable computation time. To validate the model’s performance, several parameters, such as the percentage of fuel reduction, percentage of detour vehicles, and percentage of platooned links (road segments), are considered. Additionally, various cases are considered with varying fuel reduction rates, traffic flow rates, and time windows.

Method and evaluations of the effective gain of artificial intelligence models for reducing CO2 emissions

Nowadays, there is an increasing use of digital technologies and Artificial Intelligence (AI) such as Machine Learning (ML) models that leverage data to optimize the performances of systems in almost all activity sectors, including ML models for optimizing solutions related to CO2 capture from the atmosphere or CO2 emissions reduction from human activities. However, on the other hand, the use of AI models is leading to an increasing energy consumption that also raises environmental issues (in terms of CO2 emissions) which are less studied in the literature. This then raises the new question of a more realistic estimate of the carbon footprint (CO2 emissions in particular) of AI models in general, and particularly AI models aimed at reducing CO2 emissions. Thus, in this paper, for an AI model in this latter context, we propose a method to quantify both his negative impacts (quantity of CO2 emissions emitted by the training and use of the model) and his positive impacts (quantity of CO2 emissions saved when the model is used). The method is evaluated with three state-of-the-art AI models: (i) an artificial neural network model for managing the energy demand of Brazilian households, (ii) an adaptive neuro-fuzzy inference system for photovoltaic power forecast in Tunisia, (iii) and a Bayesian regression model for the electric vehicle routing problem in Sweden and Luxembourg. Results show that, if only the positive impacts are considered, the reduction of CO2 emitted due to the usage of the models is significant, but depends on each context (34%, 73%, and 9%, respectively). However, when both positive and negative impacts are considered, the negative impacts are sometimes higher than the positive impacts (the first and the third model) for a nominal use (1 user). Nevertheless, the balance becomes highly positive again, when these two projects are scaled up (realistic projections with many users). The second model cannot be scaled up, but the balance remains positive, even if the gains are much smaller. More generally, the CO2 emissions gain metrics provided by our method can be used as new metrics for comparing the efficiency of AI models (for reducing CO2 emissions) beyond predictive capacity-based traditional ML evaluation metrics. Based on the lessons learned from our study, we also provide seven global recommendations that can contribute to the reduction of the carbon footprint of ML models in general.

Retraction Note: Soft computing-based fuzzy time series model for dynamic vehicle routing problem

Retraction Note: Soft computing-based fuzzy time series model for dynamic vehicle routing problem

Strategi Optimasi Jaringan Distribusi Sampah Organik di Tangerang Selatan

South Tangerang City is the youngest city that officially separated from the Tangerang Regency in 2008. Area of South Tangerang City 147,19 km2 or 1,63 percent from the area of Banten Province with the widest area of Pondok Aren subdistrict with an area of 2.988 hectares. While administratively, South Tangerang City has 7 sub-districts namely Pamulang, Setu, Ciputat, East Ciputat, Serpong, North Serpong and Pondok Aren, and has 54 villages. Optimizing the distribution network of organic waste supply chain can be formulated in Vehicle Routing Problem (VRP) model. Variants of VRP include Capacitated Vehicle Routing Problem (CVRP) can be used as a model in optimizing the waste transport route and the determination of the fastest route for the transport process of organic waste in all intermediate transfer points spread in South Tangerang city. The results of this research formulate some models of optimization organic waste transport routes in each sub-district in South Tangerang city. As in Setu district there is one optimum route, Serpong district there are 7 optimum route, Pamulang district have 6 optimum route, Ciputat district have 3 optimum route, East Ciputat District have 1 optimum route, Pondok Aren district have 4 optimum route, North Serpong there are 2 optimum routes.

Data-driven Game-theoretic Model Based on Blockchain for Managing Resource Allocation and Vehicle Routing in Modular Integrated Construction

ABSTRACT In modular integrated construction (MiC), the resource allocation problem (RAP) adopted by construction sites and vehicle routing problem (VRP) adopted by logistics companies are highly interdependent. However, this interdependence has been overlooked in the literature. Thus, the plans determined by each problem cannot be achieved practically. Moreover, there is no existing VRP model suitable for the MiC application. This study aims to propose a VRP model suitable for MiC, investigate the interdependence between the RAP and proposed VRP model, and develop a blockchain system to secure information sharing between the RAP and VRP. The first two objectives are achieved by developing a coordinated system formulated as a leader-follower Stackelberg game model (LFSGM). This model is presented as a bi-level optimisation model and solved using a nested ant colony optimisation-based algorithm. The effectiveness of the LFSGM is validated using a real case study. The results show that using the traditional approach (i.e. a separate RAP and proposed VRP) succeeds in providing a plan for the construction sites but not for the logistics company. In contrast, the LFSGM offers applicable plans for both the construction sites and logistics company. Lastly, some managerial implications are identified and discussed.

A Multipopulation Multiobjective Ant Colony System Considering Travel and Prevention Costs for Vehicle Routing in COVID-19-Like Epidemics

As transportation system plays a vastly important role in combatting newly-emerging and severe epidemics like the coronavirus disease 2019 (COVID-19), the vehicle routing problem (VRP) in epidemics has become an emerging topic that has attracted increasing attention worldwide. However, most existing VRP models are not suitable for epidemic situations, because they do not consider the prevention cost caused by issues such as viral tests and quarantine during the traveling. Therefore, this paper proposes a multi-objective VRP model for epidemic situations, named VRP4E, which considers not only the traditional travel cost but also the prevention cost of the VRP in epidemic situations. To efficiently solve the VRP4E, this paper further proposes a novel algorithm named multi-objective ant colony system algorithm for epidemic situations, termed MOACS4E, together with three novel designs. First, by extending the efficient “multiple populations for multiple objectives” framework, the MOACS4E adopts two ant colonies to optimize the travel and prevention costs respectively, so as to improve the search efficiency. Second, a pheromone fusion-based solution generation method is proposed to fuse the pheromones from different colonies to increase solution diversity effectively. Third, a solution quality improvement method is further proposed to improve the solutions for the prevention cost objective. The effectiveness of the MOACS4E is verified in experiments on 25 generated benchmarks by comparison with six state-of-the-art and modern algorithms. Moreover, the VRP4E in different epidemic situations and a real-world case in the Beijing-Tianjin-Hebei region, China, are further studied to provide helpful insights for combatting COVID-19-like epidemics.

Hot-Rolling Planning Based on the Modified Vehicle Routing Problem

With fierce market competition and diversification of customer demand, steel industries are currently facing the challenge of a multi-variety, small-batch, and high-quality production mode. In this study, different constraints, optimization objectives and solving algorithms in hot rolling planning (HRP) are summarized. Considering the particularity of the HRP problem, the vehicle routing problem model was modified, and a corresponding algorithm based on the actual hardness and thickness constraints was designed to solve it. The HRP problem is analysed in detail and solved by Python with an actual production dataset. The developed model has been implemented in a large steel plant, and experimental results show the effectiveness of the proposed model.

An Integrated Model of Continuous Review Inventory and Vehicle Routing Problem with Time Windows

Item stock-out or shortage is a great issue for customer satisfaction and can be countered by providing safety stocks. This study discusses an authorized distributor of smartphone and tablet computer products in Indonesia. Currently, the company is considering a plan to take over the role of a logistics service company to reduce the total logistics costs that must be incurred by making improvements in terms of inventory management. Inventory management can also protect the company from the impact of inflation and price increases that will impose logistics costs. This study has assumed that the vehicle travel speed or travel time between two nodes is fixed. This study contributes to minimizing total logistics costs by integrating the concept of a continuous review lot size-reorder point (Q, R) inventory model with lost sales and vehicle routing problems with time windows (VRPTW). Continuous review (Q, R) inventory model with lost sales shows the retailers’ cycle demand ranges from 130 to 234 units. This condition leads to the value of the expected average annual inventory cost IDR 27.263.204.625,59. Based on the VRPTW calculation, the total distribution cost per trip is IDR 445.631.642,7. Based on the current total distribution costs, it was found that the total distribution costs were IDR 849.454.616,2, so there is a decrease in distribution costs of 47.54%. The total logistics cost that must be incurred by the company to deliver smartphone products to 10 retailers in Jakarta and its surrounding areas is IDR 27,708,836,268.29.

A Hybrid Optimization Model for Vehicle Routing Problem, a Case Study at Zoomlion Ghana Limited, Shama District

The aim of this study was to develop a hybrid optimization model for solving the routing problem identified at Zoomlion Ghana Limited in Shama district in the Western Region of Ghana. Two main optimization models were considered: Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). A hybrid algorithm was developed from the two by merging crossover and mutation operators of GA with PSO. A sample of 20 breakpoints was run through 10,000 iterations for all the models and the results of the proposed hybrid model was compared with PSO and GA separately. The optimal results of PSO, GA and the proposed models are 1160.6km, 1190.3km and 1132.3km respectively. The proposed model’s results were also compared with other hybrid models to test the robustness of the new model. This result was achieved because the new model eliminates the low convergence rate in PSO and also prevents it from easily falling into local optimum in high-dimensional space and the inclusion of crossover and mutation operators of GA improves the diversity of the iterations. After the iterations, PSO reduced a field distance of 1700 km to 1160.6 km within 780.4098 seconds. GA on the other hand reduced the same field distance of 1700 km to 1190.3km within 397.3308 seconds. The proposed hybrid model reduced the same field distance from 1700 km to 1132.3 km within 550.2527 seconds. This indicates that the proposed hybrid model performed better than PSO and GA separately. A performance test between the proposed hybrid model and other hybrid models showed that merging crossover and mutation operators into PSO gives a better optimal result. MATLAB was used for the iterations.

Mathematical Model for the Generalized VRP Model

The Vehicle Routing Problem (VRP) is a highly investigated logistics problem. VRP can model in-plant and out-plant material handling or a whole supply chain. The first Vehicle Routing Problem article was published in 1959 by Dantzig and Ramser, and many varieties of VRP have appeared since then. Transport systems are becoming more and more customized these days, so it is necessary to develop a general system that covers many transport tasks. Based on the literature, several components of VRP have appeared, but the development of an integrated system with all components has not yet been completed by the researchers. An integrated system can be useful because it is easy to configure; many transportation tasks can be easily modeled with its help. Our purpose is to present a generalized VRP model and show, in the form of case studies, how many transport tasks the system can model by including (omitting) each component. In this article, a generalized system is introduced, which covers the main VRP types that have appeared over the years. In the introduction, the basic Vehicle Routing Problem is presented, where the most important Vehicle Routing Problem components published so far are also detailed. The paper also gives the mathematical model of the generalization of the Vehicle Routing Problem and some case studies of the model are presented.

A Time-Dependent Vehicle Routing Problem for Instant Delivery Based on Memetic Algorithm

Instant delivery is an intermediary bridge for same-city O2O services and an important part of urban short-distance logistics. The route planning and scheduling of instant delivery need to balance cost and customer satisfaction and consider the impact of traffic conditions on the distribution process. In this paper, we propose a vehicle routing problem model considering two types of customer time windows under time-dependent road networks and design a memetic algorithm combined with genetic algorithm and variable neighborhood search to solve the problem. By comparing the results of the different time periods and conducting sensitivity analysis for the two types of customer time windows, the effectiveness of the model and algorithm is verified.

Intelligent evacuation model in disaster mitigation

Mitigation is a pre-disaster action that aims to prepare for a disaster situation. One of the mitigation activities is evacuation, which aims to reduce disaster-related losses. Because disaster damage cannot be predicted, dealing with evacuation efforts requires a dynamic model. This study will utilize a dynamic model that combines the game theory model for choosing the evacuation location and the open vehicle routing problem (OVRP) model for selecting evacuation routes to create an intelligent system. The game theory model will be used to supplement the selection of alternate evacuation locations by taking into account geographical features that are very uncertain in the event of a crisis. An evacuation route equipped with an OVRP model with the goal of optimizing travel time is required to mobilize disaster-affected people. With the development of the Intelligent Evacuation System idea, combining the two models will create a new model. The simulation model is evaluated using linear, interactive, and discrete optimizer (LINDO), which may minimize the evacuation time from the evacuation route to a safe destination by 50 minutes if there is no contra-flow to allow more people to be evacuated.

Distributionally robust equilibrious hybrid vehicle routing problem under twofold uncertainty

A novel nonlinear hybrid vehicle routing problem is examined, and its nonlinear component is linearised considering the transportation costs associated with electricity and traditional fuel-based driving. Due to the fact that the transportation cost and fuel consumption involve the twofold uncertainty of randomness and fuzziness, and only partial probability distribution information may be available. Therefore, these two parameters are considered as random fuzzy variables with ambiguous probability distributions. An ambiguous equilibrium risk value objective function and an ambiguous equilibrium chance constraint are formulated. Accordingly, a distributionally robust equilibrium approach is proposed, in which the ambiguity sets are used to characterize the ambiguous probability distributions of the random fuzzy variables. Specifically, this paper first applies the central limit theorem to construct the ambiguity sets. Subsequently, the inner ambiguous probability constraint and the outer credibility constraint are derived into their equivalent counterparts, respectively. In this manner, the proposed model is successfully converted into a mixed integer second-order cone programming model, where the conventional branch-and-cut algorithm is adopted to obtain the optimal routing. Finally, the performance of the proposed model and its price of distributional robustness are verified in the numerical experiments. Overall, the main achievements of this paper are summarized as (1) the proposal of a distributionally robust equilibrium optimization model for a nonlinear hybrid vehicle routing problem, (2) the definitions of an ambiguous equilibrium risk value objective function and an ambiguous equilibrium chance constraint under twofold uncertainty, and (3) the derivation of an equivalent second-order cone programming model for computationally solvable.

Multipoint Distribution Vehicle Routing Optimization Problem considering Random Demand and Changing Load

In the distribution scenario, the using cost of vehicles is closely related to energy consumption, and the energy consumption rate of a vehicle is closely related to the size of its load. The traditional vehicle routing optimization model takes the shortest distance as the optimization goal when the customer demand is determined, while the influence of the random demand and the changing load on the energy consumption and cost of vehicles in the process of distribution is ignored. Therefore, in this paper, load varying vehicle routing problem with stochastic demands (LVGVRPSD) model is proposed with the goal of minimizing transportation energy consumption and considering the load variability and the randomness of customer demand. K-means clustering algorithm is combined with ant colony optimization (ACO) to solve the problem, and the constraint of risk probability is introduced to describe the vehicle overload problem. Examples in the standard vehicle routing problem test data set are provided and analyzed. LVGVRPSD is also compared with the traditional capacitated vehicle routing problem (CVRP) model. The case study results show that the vehicle energy consumption can be reduced by 2% in the model that considers changing load compared to the model that does not consider changing load. The results illustrate that the method of path optimization is more advantageous and reasonable in the pursuit of reducing energy consumption, when the changing load and the random demand of customer are considered.

Multi-compartment vehicle routing optimization based on a hybrid Genetic Algorithm

To solve the problem of efficient distribution of convenience store chains, this paper creates a multi-compartment vehicle routing problem model with time- windows (MCVRP-TW) to meet the goal of minimizing the total distribution cost. The solution is based on a hybrid genetic algorithm, which using the “destruction” and “repair” operators of the large-scale neighborhood search algorithm and the Metropolis criterion of simulated annealing for local search operations. The results show that the proposed multi-compartment vehicle routing model with time windows and the hybrid algorithm can meet the requirement of practical route design and reduce the total distribution cost.

A Study of Community Group Purchasing Vehicle Routing Problems Considering Service Time Windows

In this paper, a vehicle routing problem (VRP) model considering delivery time windows and variable service time is established for the delivery problem in community group purchasing. A solution model for an improved ant colony algorithm (ACA) is proposed by improving the initial feasible solution and the neighbourhood search mechanism of the ant colony algorithm. The algorithm of the improved ant colony and the commonly used algorithm are solved for real cases and publicly available benchmark datasets, respectively, for comparative analysis. The results show that the improved ACA has stronger optimization capability, faster convergence speed, and has advantages in solving VRPTW problems with variable service time. The computational efficiency is also improved by 41% over the genetic algorithm (GA) in the solution of the benchmark dataset, which provides a certain reference for solving the community group distribution problem.

A Region Enhanced Discrete Multi-Objective Fireworks Algorithm for Low-Carbon Vehicle Routing Problem

A constrained multi-objective optimization model for the low-carbon vehicle routing problem (VRP) is established. A carbon emission measurement method considering various practical factors is introduced. It minimizes both the total carbon emissions and the longest time consumed by the sub-tours, subject to the limited number of available vehicles. According to the characteristics of the model, a region enhanced discrete multi-objective fireworks algorithm is proposed. A partial mapping explosion operator, a hybrid mutation for adjusting the sub-tours, and an objective-driven extending search are designed, which aim to improve the convergence, diversity, and spread of the non-dominated solutions produced by the algorithm, respectively. Nine low-carbon VRP instances with different scales are used to verify the effectiveness of the new strategies. Furthermore, comparison results with four state-of-the-art algorithms indicate that the proposed algorithm has better performance of convergence and distribution on the low-carbon VRP. It provides a promising scalability to the problem size.

Co-Simulation of Multiple Vehicle Routing Problem Models

Complex systems are often designed in a decentralized and open way so that they can operate on heterogeneous entities that communicate with each other. Numerous studies consider the process of components simulation in a complex system as a proven approach to realistically predict the behavior of a complex system or to effectively manage its complexity. The simulation of different complex system components can be coupled via co-simulation to reproduce the behavior emerging from their interaction. On the other hand, multi-agent simulations have been largely implemented in complex system modeling and simulation. Each multi-agent simulator’s role is to solve one of the VRP objectives. These simulators interact within a co-simulation platform called MECSYCO, to ensure the integration of the various proposed VRP models. This paper presents the Vehicle Routing Problem (VRP) simulation results in several aspects, where the main goal is to satisfy several client demands. The experiments show the performance of the proposed VRP multi-model and carry out its improvement in terms of computational complexity.

Optimal Model and Algorithm of Medical Materials Delivery Drone Routing Problem under Major Public Health Emergencies

To reduce distribution risk and improve the efficiency of medical materials delivery under major public health emergencies, this paper introduces a drone routing problem with time windows. A mixed-integer programming model is formulated considering contactless delivery, total travel time, and customer service time windows. Utilizing Dantzig–Wolfe decomposition, the proposed optimization model is converted into a path-based master problem and a pricing subproblem based on an elementary shortest path problem with resource constraints. We embed the pulse algorithm into a column generation framework to solve the proposed model. The effectiveness of the model and algorithm is verified by addressing different scales of Solomon datasets. A case study on COVID-19 illustrates the application of the proposed model and algorithm in practice. We also perform a sensitivity analysis on the drone capacity that may affect the total distribution time. The experimental results enrich the research related to vehicle routing problem models and algorithms under major public health emergencies and provide optimized relief distribution solutions for decision-makers of emergency logistics.

Robust optimization model for the electric vehicle routing problem under battery energy consumption uncertainty with arc segmentation

As the greenhouse gas emission regulations have strengthened, establishing a sustainable transportation system has become more essential. Thus, studies on the transportation system using electric vehicles have received more research attention. However, operation using electric vehicles has obstacles such as technical limitations of vehicle batteries and insufficient number of charging stations, which can be much affected by the traffic flow changes. Therefore, we propose a robust electric vehicle routing model under road traffic flow uncertainty. An arc segmentation method is used to apply the road traffic flow uncertainty more realistically and mitigate the conservativeness of the solutions. We also use the genetic algorithm with three-layer chromosome encoding schemes. Three-layer chromosome encoding schemes guarantee the feasibility of the solutions by checking the product and battery capacity of electric vehicles. The experiments show that the solutions of the robust electric vehicle routing model tend to visit charging stations more often due to the uncertain road traffic flow. However, simulation experiments show that the robust electric vehicle routing model provides better performance.