Route Optimization Model Research Articles

A Three Stage Optimal Scheduling Algorithm for AGV Route Planning Considering Collision Avoidance under Speed Control Strategy

With the trend of terminal automation and the requirement for port operation efficiency to be greatly improved, it is very necessary to optimize the traveling route of automatic guided vehicles (AGV) with reference to the connection of loading and unloading equipment. As a complex multi-equipment system, it is inevitable that AGV will collide when traveling due to various accidents in actual operation, which will lead to AGV locking and reduce the efficiency of terminal operation. Considering the locking problem of AGV, we propose a three-stage integrated scheduling algorithm for AGV route planning. Through joint optimization with quay cranes (QC) and yard blocks, a road network model is established in the front area of the container port to optimize the path of AGV in the road network, and a speed control strategy is proposed to solve the problem of AGV collision avoidance. In the first stage, we establish the AGV optimal route model with the goal of minimizing the AGV path according to the AGV road network situation. In the second stage, on the basis of the determination of AGV route planning, and when the container task is known, the AGV task assignment model is established with the goal of minimizing the maximum completion time, and the model is solved by genetic algorithm (GA). In the third stage, on the basis of AGV task assignment and route determination, the AGV route and AGV task assignment scheme are input into the simulation model by establishing the AGV collision avoidance control model for speed control, and establishing the AGV route network simulation model for automated terminals considering collision avoidance in plant simulation software. The maximum completion time obtained from the simulation model is compared with the completion time obtained from the genetic algorithm. The proposed three-stage joint scheduling algorithm can improve the loading and unloading efficiency of the port, reduce the AGV locking situation, and has a certain contribution to the formulation of the actual operation planning of the port.

Railway car routing optimization based on the coordinated utilization of station‐line capacities

AbstractTo optimize railway car routing based on balanced station‐line capacities, thereby improving railway freight traffic organization efficiency, and reducing transportation costs, a railway car routing optimization model based on the coordinated utilization of station‐line capacities was developed. Based on the theory of the membership function in fuzzy mathematics, the level of railway station‐line coordination based on capacity utilization was presented. The occupancy of a railway line by each car was taken as a binary decision variable. Using a set of deviation constraints, the deviation between the station‐line flow of the railway network and the utilization of a certain level was expressed in the form of decision variables that were then penalized to different degrees in the objective function. The total transportation cost and the penalty cost were taken as the objective function for minimization. Based on the characteristics of the fuzzy membership degree, the relationship between the penalty for capacity deviation and the membership function in the optimization model was analyzed, and the calculation method of the penalty for the capacity deviation considering the membership function was presented. An adaptive large‐scale neighbourhood search (ALNS) algorithm was designed to solve the model, and its effectiveness was verified. An actual 36‐station railway network was used to validate the proposed railway car routing optimization model through comparison with existing models. The optimization results showed that, at the expense of increasing the transportation cost by 1.05%, the coordination penalty cost was reduced by 11.24%, the overall cost was reduced by 3.21%, and most of the situation with 90% utilization rate of stations and lines in the railway network was eliminated. The model proposed in this paper can help reduce the pressure on lines and stations with tight capacity in the network, improve the utilization of virtual lines and stations and coordination of station‐line capacities.

Applied Study of the Fluidization Model of Logistics Transportation through the Prism of the Impact Generated on the Environment

A real problem of today's society is the loss of human lives due to road accidents and the pollution caused by freight transport through metropolitan areas. The restrictions imposed in the near future for freight transport could reduce its efficiency and create many more problems. Using data centralization and developing applications or algorithms dedicated to the freight transportation sectors, routes and emissions can be managed much more efficiently. In this work, general aspects are presented, as well as a route optimization model for freight transport, taking into account the environmental impact, based on a heuristic algorithm, that of the ant colony (ACO). A multitude of studies has focused on what represents the benefits created by the applicability of solutions rather than on generalities and perspectives. The paper aims to highlight the usefulness of an optimization model of freight transport routes and the minimization of time and social costs. The study will show us that an optimized route for freight transport has a huge impact on costs, but also on time efficiency and polluting emissions.

Research on Urban Distribution Routes Considering the Impact of Vehicle Speed on Carbon Emissions

To solve the low-carbon vehicle routing problem (VRP) with sustainable development background, the VRP considering the impact of vehicle speed on carbon emissions is studied. A vehicle routing optimization model aims to minimize the total cost of distribution, penalty cost and carbon emission cost. In the model, the driving time and carbon emissions are calculated on the basis of the varying vehicle speed matching the real road network. Moreover, the improved genetic algorithm is applied to optimize vehicle routing in this paper. Using the Solomon standard datasets, the experimental results validate the carbon emission cost and the total cost from the dynamic road network are lower than those from the static network; furthermore, the carbon emission cost and the total cost from the improved GE algorithm are much lower than those from the general GE algorithm in the dynamic road network. Obviously, the built model and the improved algorithm are more feasible and effective.

A data‐driven optimization approach to plan smart waste collection operations

AbstractWaste collection is an important logistic operation that is often inefficient due to the high uncertainty associated with bin fill levels, resulting either in routes that visit empty bins or in bins overflowing due to lack of routes. To reduce such uncertainty, sensors installed in the bins can provide real‐time information on waste levels. However, this is not enough, and the management of this information needs to be combined with dynamic optimization approaches to effectively design smart collection routes. This paper investigates this challenge and uses real‐time information on waste levels, treated through machine learning techniques, to feed a dynamic reverse inventory routing optimization model. The resulting data‐driven optimization approach allows the definition of a medium‐term collection route plan that can be updated daily as new information becomes available. To demonstrate the applicability of such an approach, a real‐world case is solved, and the results show significant improvements in operational efficiency and service levels.

Stochastic optimization models for a home service routing and appointment scheduling problem with random travel and service times

We address a routing and appointment scheduling problem with uncertain service and travel times arising from home service practice. Specifically, given a set of customers within a service region that an operator needs to serve, we seek to find the operator’s route and time schedule. The quality of routing and scheduling decisions is a function of the total operational cost, consisting of customers’ waiting time, and the operator’s travel time, idle time and overtime. We propose and rigorously analyze a stochastic programming model and two distributionally robust optimization (DRO) models to solve the problem, assuming known and unknown service and travel time distributions, respectively. We consider two popular types of ambiguity sets for the DRO models: mean-support and 1-Wasserstein ambiguity sets. We derive equivalent mixed-integer linear programming (MILP) reformulations of both DRO models that can be implemented and efficiently solved using off-the-shelf optimization software, thereby enabling practitioners to use these models. In an extensive numerical experiment, we investigate the proposed models’ computational and operational performance and derive insights into the problem.

Multi-Objective Vehicle Routing Problem for Waste Classification and Collection with Sustainable Concerns: The Case of Shanghai City

With the promotion of an ecological civilization philosophy and a sustainable development strategy, solid waste classification and collection has become an emerging issue in China. Based on the three dimensions of sustainable development, namely economy, society, and environment, the route optimization model of waste collection and transportation is constructed. In order to solve the model aiming to maximize the benefits of sanitation companies under the constraints of workload balance, transportation cleanliness, and route changes due to cost factors, we combine the non-dominated sorting genetic algorithm III with simulated annealing. According to the characteristics of the problem, the probabilistic insertion method is incorporated to generate the initial solution, and the adaptive mutation operator is added to improve the population diversity. Finally, a real case in Xuhui District, Shanghai, a megacity taking the lead in 2019 in mandating a separated collection policy, is presented to verify the proposed model’s performance. The results provide a decision solution for dispatching the collection route of vehicles with some references for sanitary companies.

Collaborative Reverse Logistics Network for Infectious Medical Waste Management during the COVID-19 Outbreak.

The development of COVID-19 in China has gradually become normalized; thus, the prevention and control of the pandemic has encountered new problems: the amount of infectious medical waste (IMW) has increased sharply; the location of outbreaks are highly unpredictable; and the pandemic occurs everywhere. Thus, it is vital to design an effective IMW reverse logistics network to cope with these problems. This paper firstly introduces mobile processing centers (MPCs) into an IMW reverse logistics network for resource-saving, quick response, and the sufficient capacity of processing centers. Then, a multi-participant-based (public central hospitals, disposal institutions, the logistics providers, and the government) collaborative location and a routing optimization model for IMW reverse logistics are built from an economic, environmental perspective. An augmented ε-constraint method is developed to solve this proposed model. Through a case study in Chongqing, it is found that for uncertain outbreak situations, fixed processing centers (FPCs) and MPCs can form better disposal strategies. MPC can expand the processing capacity flexibly in response to the sudden increase in IMW. The results demonstrate good performance in reduction in cost and infection risk, which could greatly support the decision making of IMW management for the government in the pandemic prevention and control.

Multi‐objective moth swarm based sailfish model for optimal routing in wireless sensor network

SummaryIn recent years, the WSN are emerging swiftly since it finds applications in various domains including weather monitoring, attack detection, industrial monitoring, monitoring of submarine organisms, patient monitoring as well as the monitoring of ecological disorders. But WSN is also influenced by various other factors like network lifetime and energy consumption. It is necessary to provide an energy effective protocol to conquer certain troubles that includes packet delivery ratio, network lifetime, residual energy as well as effective routing in WSN. Therefore, this article aims to propose a novel protocol to enhance the energy efficiency of the network thereby providing an optimal routing path. This can be achieved by selecting an optimal cluster head that maintains communication between the base station and the sensor node. In this article, a novel multi‐objective moth swarm based sailfish (MOMS‐SF) technique is employed in selecting an optimal cluster head. The proposed MOMS‐SF technique enhances the network lifetime and minimizes the energy consumption of the network. Finally, the evaluation results are conducted to determine the network performances of the proposed MOMS‐SF approach. Also, a comparative analysis is carried out and the graphical analyzes for various parameters are made for various approaches to determine the effectiveness of the proposed system.

A logistics distribution route optimization model based on hybrid intelligent algorithm and its application

A logistics distribution route optimization model based on hybrid intelligent algorithm and its application

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.

Model construction of material distribution system based on digital twin

Aiming at the problems of poor periodicity of workshop material distribution, difficult prediction of station material demand time node, and redundant distribution route, this paper proposes a model construction method of material distribution system based on digital twin. Build a material distribution control mode based on digital twin, and establish a full-cycle material distribution mechanism on this basis to comprehensively optimize the distribution cycle from the material preparation stage, dynamic replenishment stage, and data transmission stage of adjacent distribution cycles. Build the digital twin model of material distribution system, establish the material demand time node dynamic prediction operation mechanism based on LSTM, accurately predict the station material demand time node, establish the material distribution route optimization model with the lowest total cost, and optimize the AGV route. Finally, it is applied to the asynchronous line workshop of A enterprise to verify the effectiveness of this method.

Two-Stage Solution for Meal Delivery Routing Optimization on Time-Sensitive Customer Satisfaction

The online-to-offline (O2O) meal delivery mode in which takeout meals are ordered online and delivered offline is recently emerging. The fast delivery of huge meal orders for time-sensitive customers imposes great challenges on O2O meal delivery platforms. This study establishes a two-stage solution for meal delivery routing optimization with the objective of maximizing time-sensitive customer satisfaction. In the first stage, a large number of meal orders are hierarchically classified and merged into delivery bundles based on the nearest pickup location rule by applying the hierarchical agglomerative clustering (HAC) algorithm, to increase fast meal delivery efficiency. In the second stage, a genetic algorithm (GA) is applied to solve the cluster-based delivery routing optimization model to find an optimal delivery route for meal orders in each delivery bundle. The numerical simulation results verify that the two-stage routing optimization solution is effective to schedule timely meal delivery and improve customer time satisfaction. The comparison of the results indicates the superiority of the proposed two-stage solution with HAC and GA on customer satisfaction while ensuring the delivery of all orders within 60 minutes. The sensitivity analysis shows the impact of time-sensitive customer heterogeneity on meal delivery satisfaction. This research has significant managerial insights for fast delivery services of O2O meal delivery platforms.

Application of the Logistics Management System Based on Cloud Computing Technology

In order to solve the vehicle routing optimization problem of cold chain logistics distribution, this paper proposes a vehicle routing optimization method of cold chain logistics distribution based on cloud computing technology. This paper analyzes the advantages of processing the real-time routing of distribution vehicles under the cloud computing mode and establishes the application service architecture of vehicle routing optimization in cold chain logistics; under this architecture, multisource real-time traffic information is obtained, the vehicle distribution time and comprehensive cost are analyzed, the vehicle routing optimization model of cold chain logistics distribution is constructed, and the coarse-grained parallel genetic algorithm is used to solve the model in the cloud computing environment. The experimental results show that if W 1 = 0 , W 2 = 1 , the optimal path is 0-6-7-3-1-4-2-8-5, the total cost is 1620 yuan, the total mileage is 72 km, the vehicle driving time is 2.17 hours, and the calculation time is 10.79 s; let W 1 = 1 , W 2 = 0 , the optimal path is 0-5-6-7-2-4-3-1-8, the vehicle travel time is 1.84 hours, the total cost is 1736 yuan, the total mileage is 76 km, and the calculation time is 11.56 s. The optimal path is obtained in the effective time range. Conclusion. The real-time route optimization method of refrigerated vehicles in the cloud computing environment is effective. This method has practical significance to realize fine control of cold chain logistics distribution cost and improve distribution service efficiency.

A graph-rotation model for optimal ship routing in ice conditions

Ship independent navigation in ice-infested waters is a risky operation. However, it is widely practiced in the Northern Baltic and other freezing regions. Up to now, there is no conventional algorithmic approach, which provides robust and efficient routes for winter navigation. In the article, we propose a new graph method for algorithmic finding of a route in the complicated environment. This method is based on a concept of local grid rotation and provides optimal routes in sense of its length and number of direction changes. Unlike Dijkstra and A*, the new method allows finding a short route with minimum number of zig-zags, which ensures distance from shores. The presented method is computationally rather efficient and employs extended wave algorithm which has been developed earlier by the authors. We provide the case study of finding an efficient route in ice conditions of the Baltic Sea given by an ice chart. Calculations are performed for a vessel with Ice2 class travelling to Gulf of Bothnia. The route found by the new method is compared with the globally shortest path evaluated by Dijkstra's algorithm on a graph with advanced grid. The new algorithm provided much smoother route with nearly the same travel distance.

Collaborative Routing Optimization Model for Reverse Logistics of Construction and Demolition Waste from Sustainable Perspective.

The construction industry is developing rapidly along with the acceleration of urbanization but accompanied by an increased amount of construction and demolition waste (CDW). From the perspective of sustainability, the existing research has mainly focused on CDW treatment or landfill disposal, but the challenge of reverse logistics of CDW recycling that provides overall CDW route planning for multiple participants and coordinates the transportation process between multiple participants is still unclear. This paper develops an optimization model for multi-depot vehicle routing problems with time windows (MDVRPTW) for CDW transportation that is capable of coordinating involved CDW participants and suggesting a cost-effective, environment-friendly, and resource-saving transportation plan. Firstly, economic cost, environmental pollution, and social impact are discussed to establish this optimization-oriented decision model for MDVRPTW. Then, a method combined with a large neighborhood search algorithm and a local search algorithm is developed to plan the transportation route for CDW reverse logistics process. With the numerical experiments, the computational results illustrate the better performance of this proposed method than those traditional methods such as adaptive large neighborhood search algorithm or adaptive genetic algorithm. Finally, a sensitivity analysis considering time window, vehicle capacity, and carbon tax rate is conducted respectively, which provides management implications to support the decision-making of resource utilization maximization for enterprises and carbon emission management for the government.

Train Routing and Track Allocation Optimization Model of Multi-Station High-Speed Railway Hub

A multi-station high-speed railway hub is the interaction of multiple high-speed railway lines, and its operation determines the efficiency of the whole network. In this study, we focus on the train routing problem for a multi-station railway hub and propose a flexible scheme to improve the capacity utilization. Based on the flexible scheme, a mixed-integer programming node-arc model is formulated to minimize the total cost of train and passenger routes. Specifically, for diversifying the train routes, we consider the individual train rather than the train flow as the basic unit, which differs from the approach in previous studies. For each train route, in addition to the macro-scale route between stations, the micro-scale track allocation inside stations is also considered. Afterward, the optimization solver Gurobi is used to solve the model and obtain the optimization scheme. A case study based on real data from the Zhengzhou railway hub in China is implemented to evaluate the effectiveness of the optimization model. By comparing the optimization scheme with the fixed scheme and the sequential scheme, the results show our method could reduce the total cost by 29.35% and 22.58%, and the line and track capacity utilization of the optimization scheme is more reasonable. We provide some suggestions to help railway managers improve the operation efficiency and service quality of multi-station high-speed railway hubs.

A dynamic location-arc routing optimization model for electric waste collection vehicles

Waste collection management plays a crucial role in controlling pandemic outbreaks. Electric waste collection systems and vehicles can improve the efficiency and effectiveness of sanitary processes in municipalities worldwide. The waste collection routing optimization involves designing routes to serve all customers with the least number of vehicles, total traveling distance, and time considering the vehicle capacity. This paper proposes a dynamic location-arc routing optimization model for electric waste collection vehicles. The proposed model suggests an optimal routing plan for the waste collection vehicles and determines the optimal locations of the charging stations, dynamic charging arcs, and waste collection centers. A genetic algorithm and grey wolf optimizer are used to solve the large-sized random generated NP-hard location-arc routing problems. We present a case study for the city of Edmonton in Canada and show the grey wolf optimizer outperforms the genetic algorithm. We further demonstrate the total number of waste collection centers, charging stations, and arcs for dynamic charging needed to ensure a minimum required service for electric vehicles throughout Edmonton's entire waste collection system.

EXPLORING QUANTUM COMPUTING POTENTIALS IN SOLVING A COMBINATORIAL OPTIMIZATION PROBLEM TO MINIMIZE EXPOSURE TO COVID-19 DURING A CITY JOURNEY

Abstract. COVID-19 is an airborne virus that can be spread directly or indirectly from one person to another. Spreading the virus strongly depends on the location and time and hence, a Spatio-temporal event. Moreover, traffic congestion will increase the spread of the virus not only because of the vicinity but also because of increased temperature and humidity in these spaces for a short or long time. This paper introduces a vehicle routing optimization model to reduce COVID-19 exposure risk during a city journey by solving it as a quadratic unconstrained binary optimization problem on a quantum annealing computer. Indeed, the objective of the COVID-19 prevention optimization problem is to minimize the risk of exposure for a given set of road users between origins and destinations. Microsoft Taxi data from the city of Beijing have been used to simulate road users’ movement. The problem has been run onto three different solvers. One of the solvers is executed on classical computers, and two other solvers are executed on hybrid quantum solvers. Hybrid solvers return the solution within less than 0.03 seconds on quantum processing unit time. However, the results will be returned at least 5 seconds after the execution in the classical solver. It is worth mentioning that, as there is no direct access to the quantum computers, it is hard to compare the results on the same scale as the queries will go on a queue in D-wave quantum computers. Applying the proposed model on the trajectory data shows a better distribution of the vehicles on the road network.

Research on emergency distribution optimization of mobile power for electric vehicle in photovoltaic-energy storage-charging supply chain under the energy blockchain

As a representative of clean energy, photovoltaic is expected to become a major supplier of electricity in the future. The combination of electric vehicle (EV) battery and charging station provides a feasible way to promote the effective consumption of photovoltaic. However, the efficiency of mobile power supply is limited by information asymmetry and security problems, and it is urgent to optimize the distribution process. Firstly, the article introduces the energy blockchain to improve the security level of electricity transaction, and designs the photovoltaic-energy storage-charging supply chain. Secondly, based on the selected road network and the actual situation of EV mobile power emergency distribution, the distribution logistics network with 50 distribution points is built. Thirdly, taking the delivery time and comprehensive cost as objective functions, the mathematical model of emergency distribution route optimization for EV mobile power supply is established, and the adaptive NSGA-II algorithm is adopted for example analysis. Finally, the parameter variation of NSGA-II and comparison with two algorithms of GA and MOPSO are carried out to validate the feasibility and applicability of proposed method. The purpose of the research is to quickly and effectively select the optimal distribution route of mobile power supply from many roads by maximizing customer demands and reducing costs, so as to promote the photovoltaic consumption.