Routing Optimization Problem Research Articles

Routing optimization of shared autonomous electric vehicles under uncertain travel time and uncertain service time

The emerging autonomous electric vehicles have facilitated the implementation of the shared autonomous electric vehicle (SAEV) service. In real-life applications, the operations of SAEVs may be affected by various uncertain factors, such as uncertain travel time caused by traffic congestion and uncertain service time caused by unpredictable customer delay. It is essential to consider uncertain factors to design conservative and robust routes for SAEVs. In this paper, we study a routing optimization problem of SAEVs, where the charging schedules, uncertain travel time, and uncertain service time are considered. The objective of the problem is to minimize the total operational cost that consists of fixed cost and travel cost of SAEVs. A branch-and-price algorithm is developed to solve the problem. Specifically, a tailored label setting algorithm is introduced to identify the robust feasible routes with accessible charging schedules for the pricing subproblem. The proposed algorithm is tested on a set of generated instances. The computational results indicate that the proposed algorithm outperforms the commercial solver CPLEX in terms of both solution quality and computational time. Besides, based on the sensitivity analyses, we show the impact of the budgets of uncertainty, maximum deviations of uncertain parameters, battery capacity, fixed cost, and charging speed on the SAEV service. This work can also provide inspirations of both models and algorithms for many other application areas, such as urban logistics.

Route Optimization via Environment-Aware Deep Network and Reinforcement Learning

Vehicle mobility optimization in urban areas is a long-standing problem in smart city and spatial data analysis. Given the complex urban scenario and unpredictable social events, our work focuses on developing a mobile sequential recommendation system to maximize the profitability of vehicle service providers (e.g., taxi drivers). In particular, we treat the dynamic route optimization problem as a long-term sequential decision-making task. A reinforcement-learning framework is proposed to tackle this problem, by integrating a self-check mechanism and a deep neural network for customer pick-up point monitoring. To account for unexpected situations (e.g., the COVID-19 outbreak), our method is designed to be capable of handling related environment changes with a self-adaptive parameter determination mechanism. Based on the yellow taxi data in New York City and vicinity before and after the COVID-19 outbreak, we have conducted comprehensive experiments to evaluate the effectiveness of our method. The results show consistently excellent performance, from hourly to weekly measures, to support the superiority of our method over the state-of-the-art methods (i.e., with more than 98% improvement in terms of the profitability for taxi drivers).

Vehicle routing and capacity planning : A three-phase algorithm

In this paper, a three-phase algorithm is designed for solving a real problem of vehicle capacity and route optimization. The proposed algorithm fulfills non-stochastic demand in a limited time window by optimally allocating vehicles of heterogeneous capacity. The pricing model of the supplier (CMMF- a popular Co-operative Milk Marketing Federation of southern India) to transport fodder from the central production facility to different destinations was not very encouraging for the vehicle owners. In CMMF’s pricing model, it is not possible to reduce the cost of transportation by vehicle capacity optimization or route optimization. CMMF was using the manual system of assignment of the vehicle to different demand locations often resulting in vehicles traveling longer distances with unused capacity. Management saw an opportunity to optimally load vehicles and fulfill various demand locations in such a way that the vehicle travels minimum distance with unused capacity. The proposed algorithm is tested on a sample 100 node network with actual demands and relevant data like demand arrival date, a connected network of demand locations, distance of demand locations from the source, and list of vehicles that are used in the existing system. A decision support system is developed to prescribe solutions to the decision-maker with different threshold unused capacity (5%, 10%, 15%, and 20%) of vehicles. Subject to the imposed constraints, a considerable reduction in the unused capacity (Kilogram-Kilometer) of vehicles is recorded.

Optimal Open-Loop Routing and Threshold-Based Allocation in TWO Parallel QUEUEING Systems with Heterogeneous Servers

In this paper, we study the problem of optimal routing for the pair of two-server heterogeneous queues operating in parallel and subsequent optimal allocation of customers between the servers in each queue. Heterogeneity implies different servers in terms of speed of service. An open-loop control assumes the static resource allocation when a router has no information about the state of the system. We discuss here the algorithm to calculate the optimal routing policy based on specially constructed Markov-modulated Poisson processes. As an alternative static policy, we consider an optimal Bernoulli splitting which prescribes the optimal allocation probabilities. Then, we show that the optimal allocation policy between the servers within each queue is of threshold type with threshold levels depending on the queue length and phase of an arrival process. This dependence can be neglected by using a heuristic threshold policy. A number of illustrative examples show interesting properties of the systems operating under the introduced policies and their performance characteristics.

Efficiency-complexity evaluation methods of routing algorithms in mobile ad hoc networks

Routing in multidomain and multilayer networks is the subject of constant theoretical research, with special emphasis on routing optimization algorithms based on several criteria. Such research results in new proposals. The basic task of the algorithm is to perform the given task in a finite and reasonable period of time and with reasonable resource requirements. When new solutions are compared with previous solutions, it is necessary to consider as much information as possible about the characteristics and differences between these algorithms, which ultimately determines the degree of success of the algorithm. Routing algorithms depend on the goals to be achieved and most often solve a certain group of problems with certain simplifications of the overall problem and to the detriment of performance that are not crucial for a given routing optimization problem. Therefore, it is necessary to have acceptable methods for efficiency-complexity evaluation methods of routing algorithms with certain, universally applicable, metrics. Several theoretical approaches, including graph theory, optimization theory, complexity theory, allow approaches to compare the algorithms and the results achieved with the help of these algorithms.

An ACO-based heuristic approach for a route and speed optimization problem in home health care with synchronized visits and carbon emissions

An ACO-based heuristic approach for a route and speed optimization problem in home health care with synchronized visits and carbon emissions

Cooperative Path Planning for Heterogeneous Agents

Cooperation among different vehicles is a promising concept for route planning of Mobility as a Service (MaaS). For instance, vehicle platooning on highways decreases fuel consumption because it reduces the air resistance and several trucks cooperate with each other when planning. Traditional platooning, however, cannot model cooperation among different types of vehicles because it assumes the homogeneity of vehicle types. We study a model that permits heterogeneous cooperation and discuss a route optimization problem under assumption that the heterogeneous cooperation benefits the objective function. We experimentally evaluate the formulation through using synthetic and real graphs based on a modern integer programming solver with various parameter settings, which are not tried in previous studies. We also compare the results by the solves with simple heuristic method developed in this paper and discuss the results to reveal the properties of the optimization problem with heterogeneous vehicle types.

Hybrid modified ant system with sweep algorithm and path relinking for the capacitated vehicle routing problem

Vehicle routing problem is a widely researched combinatorial optimization problem. We developed a hybrid of three strategies—a modified ant system, a sweep algorithm, and a path relinking—for solving a capacitated vehicle routing optimization problem, a vehicle routing problem with a capacity constraint. A sweep algorithm was used to generate initial solutions and assign customers to vehicles, followed by a modified ant system to generate new generations of good solutions. Path relinking was used for building a better solution (candidate) from a pair of guiding and initial solutions. Finally, a local search method—swap, insert, reverse and greedy search operations—was used to prevent solutions from getting trapped in a local minimum. Performance of the proposed algorithm was evaluated on three datasets from CVRPLIB. Our proposed method was at least competitive to state-of-the-art algorithms in terms of the total route lengths. It even surpassed the best-known solution in the P-n55-k8 instance. Our findings can lower the transportation cost by reducing the travelling distance and efficiently utilizing the vehicle capacity.

A study on resilient and cost-based design in power distribution network against severe hurricane

Vulnerability mitigation is the solution for creating resilient distribution networks aims to prevent the uncontrollable outage propagation. This paper presents a new resilience index as a powerful tool to enhance the resilience of distribution systems against extreme hurricane events. The suggested framework provides a comparative study for optimal feeder routing problem and HV substation placement from the cost and resilience viewpoints. Once, the network is planned based on cost minimization, and then the proposed resilience index is calculated for the planned network. Finally, the network is planned based on resilience enhancement and afterward the planned network's cost is calculated. In case of resilient-based planning, the studied area is divided into small sites with different wind speed to evaluate the geospatial characteristics of hurricane and a fragility index is calculated for each distribution network component located at each sites. Both cost- and resilient- based networks are planned using deterministic and probabilistic approaches. In deterministic approach, network planning is done according to the worst prediction, the highest wind speed. But in probabilistic one, some scenarios with specified probability are defined for wind speed. Results show that, fragility index is high when considering worst prediction, so this less probable scenario needs higher investments. In this regards, scenario based planning, can lead to the best compromised solution considering cost and resilience which the obtained results validate the accuracy of the modellings and efficiency of the proposed method.

Logistics Route Optimization Based on Improved Particle Swarm Optimization

An improved particle swarm optimization (PSO) algorithm is presented by dynamically adjusting the inertia weight in the iterative process of PSO, and it is used to solve the problem of logistics route optimization. This algorithm can not only improve the convergence speed, but also avoid falling into local optimum. In the process of improving the standard algorithm, two methods are proposed to adjust the inertia weight value according to the number of iterations. One is piecewise linear decreasing, another is linear decreasing. The results show that linear decline is better than piecewise linear decline to achieve the purpose of optimization, which is more conducive to accelerate the convergence rate and enhance the ability of optimization. Through the simulation experiment of the specific vehicle routing optimization problem, the results show that after the improvement, the optimization performance is enhanced, the optimization speed is accelerated, and the complexity is not increased, which greatly improves the performance of the original algorithm.

Improved Wolf Pack Algorithm for Solving a Class of Multi-Objective Route Optimization Problem

This paper considers a class of Multi-objective path planning and solving algorithms. The choice of step size is a very important factor that affects the iterative algorithm. This paper improves the classical Wolf Pack Algorithm (WPA) by designing an adaptive step size. This new improved wolf pack algorithm (IWPA) improves the classical wolf colony algorithm by designing an adaptive step size. It effectively solves the problem that the WPA may fall into a endless loop. The simulation results by MATLAB show that the IWPA has a good effectiveness than the WPA to solve multi-objective route optimization.

Principled network extraction from images.

Images of natural systems may represent patterns of network-like structure, which could reveal important information about the topological properties of the underlying subject. However, the image itself does not automatically provide a formal definition of a network in terms of sets of nodes and edges. Instead, this information should be suitably extracted from the raw image data. Motivated by this, we present a principled model to extract network topologies from images that is scalable and efficient. We map this goal into solving a routing optimization problem where the solution is a network that minimizes an energy function which can be interpreted in terms of an operational and infrastructural cost. Our method relies on recent results from optimal transport theory and is a principled alternative to standard image-processing techniques that are based on heuristics. We test our model on real images of the retinal vascular system, slime mould and river networks and compare with routines combining image-processing techniques. Results are tested in terms of a similarity measure related to the amount of information preserved in the extraction. We find that our model finds networks from retina vascular network images that are more similar to hand-labelled ones, while also giving high performance in extracting networks from images of rivers and slime mould for which there is no ground truth available. While there is no unique method that fits all the images the best, our approach performs consistently across datasets, its algorithmic implementation is efficient and can be fully automatized to be run on several datasets with little supervision.

Approximate Solution by Ant Colony Programming to Symmetrical Drop Suspended Equilibrium Equation

The paper presents the Ant Colony Programming (ACP) method as a solver for Differential Equations of a suspended symmetric drop. The Ant Colony method developed by Gambardella Dorigo in 1997 has been applied in Routing, Scheduling, Constraint Satisfaction, Graph Coloring, and such other optimization problems. The Differential Equation Solution can be expressed as an expression containing Terminals and Functions and both are represented as a graph with nodes and edges. In this graph, each node (city) represents a function or terminal, the information flow represents pheromone quantity disposed by ants while walking. An initial quantity of pheromone is disposed on links between nodes. After that and until termination criteria, ants construct solutions (paths) from the source to the destination by visiting nodes, and deposing (updating) pheromones quantity at links of the path. This is one of the advantages of Ant Colony Programming, i.e. the diversification by local pheromone update. The select step is fundamental, too. In this step, the ant reflects the performance of the link in the cost path according to fitness value. The simulation experiments show that Ant Colony Programming method can obtain very good results in finding solution.

An Optimized and Efficient Routing Protocol Application for IoV

IoV is the latest application of VANET and is the alliance of Internet and IoT. With the rapid progress in technology, people are searching for a traffic environment where they would have maximum collaboration with their surroundings which comprise other vehicles. It has become a necessity to find such a traffic environment where we have less traffic congestion, minimum chances of a vehicular collision, minimum communication delay, fewer communication errors, and a greater message delivery ratio. For this purpose, a vehicular ad hoc network (VANET) was devised where vehicles were communicating with each other in an infrastructureless environment. In VANET, vehicles communicate in an ad hoc manner and communicate with each other to deliver messages, for infotainment purposes or for warning other vehicles about emergency scenarios. Unmanned aerial vehicle- (UAV-) assisted VANET is one of the emerging fields nowadays. For VANET’s routing efficiency, several routing protocols are being used like optimized link state routing (OLSR) protocol, ad hoc on-demand distance vector (AODV) routing protocol, and destination-sequenced distance vector (DSDV) protocol. To meet the need of the upcoming era of artificial intelligence, researchers are working to improve the route optimization problems in VANETs by employing UAVs. The proposed system is based on a model of VANET involving interaction with aerial nodes (UAVs) for efficient data delivery and better performance. Comparisons of traditional routing protocols with UAV-based protocols have been made in the scenario of vehicle-to-vehicle (V2V) communication. Later on, communication of vehicles via aerial nodes has been studied for the same purpose. The results have been generated through various simulations. After performing extensive simulations by varying different parameters over grid sizes of 300 × 1500 m to 300 × 6000 m, it is evident that although the traditional DSDV routing protocol performs 14% better than drone-assisted destination-sequenced distance vector (DA-DSDV) when we have number of sinks equal to 25, the performance of drone-assisted optimized link state routing (DA-OLSR) protocol is 0.5% better than that of traditional OLSR, whereas drone-assisted ad hoc on-demand distance vector (DA-AODV) performs 22% better than traditional AODV. Moreover, if we increase the number of sinks up to 50, it can be clearly seen that the DA-AODV outperforms the rest of the routing protocols by up to 60% (either traditional routing protocol or drone-assisted routing protocol). In addition, for parameters like MAC/PHY overhead and packet delivery ratio, the performance of our proposed drone-assisted variants of protocols is also better than that of the traditional routing protocols. These results show that our proposed strategy performs better than the traditional VANET protocols and plays important role in minimizing the MAC/PHY and enhancing the average throughput along with average packet delivery ratio.

Landing route planning method for micro drones based on hybrid optimization algorithm

Aiming at the obstacle avoidance trajectory planning problem in the landing process of the micro drone, this paper proposes a swarm optimization algorithm that combines the dragonfly optimization method and the differential evolution method. The orthogonal learning mechanism is adopted to realize the adaptive switch between the two algorithms. In the process of landing route planning, the planning plane is first obtained by making the gliding plane tangent to the obstacle. In the planning plane, the projection of obstacle is transformed into multiple unreachable line segments. By designing an optimization model, the 3D landing route planning problem is transformed into a 2D obstacle avoidance route optimization problem. Taking the shortest route as the optimization objective, the penalty factor is introduced into the cost function to avoid the intersection of the landing route and obstacle. During the optimization process, through the orthogonal learning of the intermediate iterative results, the hybrid algorithm can adaptively select the next iterative algorithm, so it can give full play to the respective advantages of the two algorithms. The optimization results show that, compared with the single optimization algorithm, the hybrid optimization algorithm proposed in this paper can better solve the problem of landing route planning for micro-small UAVs.

Improving efficiency of network resources in elastic optical transport network by using in-network cache functions

We developed a network architecture called information-centric elastic optical transport network (i-CEON) that enables efficient content delivery. Increasing the volume of video traffic consumes network resources, so it is necessary to establish optical transport network architectures suitable for content delivery. For this purpose, we extend existing information-centric networking to be applicable to elastic optical networks. The main focus of this paper is presenting the method in i-CEON of optimizing optical-path routing by effectively using an in-network cache function to efficiently accommodate content traffic. This reduces redundant use of network resources. We formulate the joint optimization problem of elastic optical-path routing and content-replication placement as a mixed integer linear programming problem and also develop a heuristic algorithm. We conducted intensive numerical experiments to demonstrate the effectiveness of our method and confirmed that it reduced network cost by 21% and server load by 32%.

Joint Routing and Charging Problem of Multiple Electric Vehicles: A Fast Optimization Algorithm

Logistics has gained great attentions with the prosperous development of e-commerce, which is often seen as the classic optimal vehicle routing problem. Meanwhile, electric vehicle (EV) has been widely used in logistic fleet to curb the emission of green house gases in recent years. Thus, solving the optimization problem of joint routing and charging of multiple EVs is in a urgent need, whose objective function includes charging time, charging cost, EVs travel time, usage fees of EV and revenue from serving customers. This joint problem is formulated as a mixed integer programming (MIP) problem, which, however, is NP-hard due to integer restrictions and bilinear terms from the coupling between routing and charging decisions. The main contribution of this paper lies at proposing an efficient two-stage algorithm that can decompose the original MIP problem into two linear programming (LP) problems, by exploiting the exactness of LP relaxation and eliminating the coupled term. This algorithm can achieve a near-optimal solution in polynomial time. In addition, another variant algorithm is proposed based on the two-stage one, to further improve the quality of solution. Compared with the state-of-the-art algorithm, extensive simulations are implemented to validate the effectiveness of the proposed algorithm.

Vehicle Routing Problem for Collaborative Multidepot Petrol Replenishment under Emergency Conditions

In recent years, emergency events have affected urban distribution with increasing frequency. For example, the 2019 novel coronavirus has caused a considerable impact on the supply guarantee of important urban production and living materials, such as petrol and daily necessities. On this basis, this study establishes a dual-objective mixed-integer linear programming model to formulate and solve the cooperative multidepot petrol emergency distribution vehicle routing optimization problem with multicompartment vehicle sharing and time window coordination. As a method to solve the model, genetic variation of multiobjective particle swarm optimization algorithm is considered. The effectiveness of the proposed method is analyzed and verified by first using a small-scale example and then investigating a regional multidepot petrol distribution network in Chongqing, China. Cooperation between petrol depots in the distribution network, customer clustering, multicompartment vehicle sharing, time window coordination, and vehicle routing optimization under partial road blocking conditions can significantly reduce the total operation cost and shorten the total delivery time. Meanwhile, usage of distribution trucks is optimized in the distribution network, that is, usage of single- and double-compartment trucks is reduced while that of three-compartment trucks is increased. This approach provides theoretical support for relevant government departments to improve the guarantee capability of important materials in emergencies and for relevant enterprises to improve the efficiency of emergency distribution.

Reverse Logistics Vehicle Routing Optimization Problem Based on Multivehicle Recycling

In the reverse logistics recycling process, considering the diversity of recycling types, a variety of vehicle models are used to meet the reverse logistics recycling requirements. Therefore, this paper considers the fixed costs, transportation costs, carbon emission costs during driving, and time penalty costs in the reverse logistics process under multiple constraints such as multiple vehicle types and time windows in the recycling center. The recovery point is the recovery model, and an improved genetic algorithm is used to solve it. Drawing lessons from the idea of greed, the superiority of the initial population is improved; the entry matrix and the exit matrix of the iterative population are constructed, and the crossover operator is improved based on this, and the forward insertion method is introduced to design the hybrid crossover operation to speed up the population. At the same time, the improvement of the mutation operator is proposed to increase the diversity of the population. The experimental results show that the multivehicle mixed delivery mode can reduce costs more effectively than single-vehicle models, and the improved genetic algorithm has better convergence and stability.

An Research on the design and optimization of shipping routes in the Arctic

In the design and research of the Arctic route, the route design can be interfered by other factors. This paper studies the route design and optimization after considering the interference of external factors. According to the external environment factors to design the arctic route weather route, the first arctic route design model was established, using the method of dynamic programming. Therefore, it is necessary to continuously determine the shortest sailing time of the ship, namely the actual sailing speed of the ship. According to the sailing conditions of the ship in different environments, so that the highest degree of navigation safety can be achieved, and the route optimization problem is transformed into a multi-stage decision-making problem. The global optimal solution is obtained by looking for the local optimal solution of each stage. Combined with the original Arctic route model, the meteorological route is optimized. By separating the present period from the future period, the present and future benefits are considered together. By determining the stage index function and the optimal index function, the basic equation of dynamic programming is established. The Pareto front curve corresponding to the requirements of the shortest voyage time and the voyage safety is given, that is, the optimal solution function curve of the two objectives. The influence between the shortest voyage time and the voyage safety is weighed comprehensively, and the optimal solution is obtained by combining the actual situation of the ship during the voyage, that is, the route optimization is completed. Finally, the optimized Arctic route is simulated and evaluated in an electronic computer simulation system with similar environment.