Multi-objective Route Planning Research Articles

A hierarchical model with hexagon grids for multi-objective route planning in large-scale off-road environments

Off-road route planning plays a vital role across various domains, including civil transportation, earthquake relief and civil production. Off-road route planning requires the consideration of a diverse range of environmental constraints, which is a considerable challenge compared with conventional route planning along existing networks. We aimed to present a multi-objective route planning method for off-road environments. Specifically, we designed a heuristic function incorporating multiple characteristics to generate reliable route planning solutions based on Pareto optimality. We also constructed a hierarchical hexagonal grid model by merging similar grid cells and reconstructing adjacency relationships with other grid cells to achieve high efficiency. The proposed method was compared with conventional route planning methods using single- and multi-objective approaches in traditional and hierarchical hexagonal grid models. The proposed method can effectively plan off-road routes, improve optimal route distance and trafficability, and provide more efficient route solutions. It reduced the planning time by 97.1% compared with multi-objective route planning based on a traditional hexagonal grid model.

Pareto Improver: Learning Improvement Heuristics for Multi-Objective Route Planning

Pareto Improver: Learning Improvement Heuristics for Multi-Objective Route Planning

Reducing Waiting Times at Charging Stations With Adaptive Electric Vehicle Route Planning

Electric vehicles are becoming more popular all over the world. With increasing battery capacities and a growing fast-charging infrastructure, they are becoming suitable for long distance travel. However, queues at charging stations could lead to long waiting times, making efficient route planning even more important. In general, optimal multi-objective route planning is extremely computationally expensive. We propose an adaptive charging and routing strategy, which considers driving, waiting, and charging time. For this, we developed a multi-criterion shortest-path search algorithm using contraction hierarchies. To further reduce the computational effort, we precompute shortest-path trees between the known locations of the charging stations. We propose a central charging station database (CSDB) that helps estimating waiting times at charging stations ahead of time. This enables our adaptive charging and routing strategy to reduce these waiting times. In an extensive set of simulation experiments, we demonstrate the advantages of our concept, which reduces average waiting times at charging stations by up to 97 %. Even if only a subset of the cars uses the CSDB approach, we can substantially reduce waiting times and thereby the total travel time of electric vehicles.

Route planning for electric vehicles

This work addresses the multi-objective route planning and charging problem for Battery Electric Vehicles (BEVs). The proposed solution is based on the NSGA-II algorithm to derive the Pareto-optimal set of eco-routes with the minimum travel and charging time, distance, energy consumption and charging costs while complying with the constraints related to the battery State of Charge (SoC) and the status of charging stations. Influencing factors, such as, vehicle and battery parameters, weather conditions, driver behaviour, road grade, Time Of Use (TOU) electricity cost, and Charging Stations (CS) status and type, are taken into account. The final results of the proposed method are compared with the well-established simulator SUMO.

[formula omitted]: A multi-objective route planning and charging slot reservation approach for electric vehicles considering state of traffic and charging station

Electric vehicles (EVs) are environment friendly and are expected to find tremendous growth in the near future. However, the short voyaging reach and the lack of charging lattice have impeded the large-scale application of EVs. A choice of a particular route for EV is largely dependent on the availability of charging stations on that route as well as the ease of charging facility offered by the charging station (including state of the charging station, user convenience, etc.). Hence, it is vital to delve into the challenge of locating a charging station that offers the utmost user convenience in charging EVs. This paper attempts to present a solution to this very problem. The proposed approach is an improvised distributed system, namely, S2RC (Smart Search of Route and Charging) which plans an energy efficient EV route considering EV’s state-of-charge (SoC) level, traffic conditions, the frequency of charging stations and the state of charging station (the charging resources of the charging station along with their occupancy levels, etc.). This distributed architecture employs the proposed agile charging slot reservation approach for the EVs that wish to get recharged at a particular charging station. Besides, to delineate the interactive mechanism of EVs recharging, three non-identical objective functions are formulated to minimize the overall energy consumption of EVs, waiting time at charging station, and total charging expenditure. After performing the extensive simulations on the weighted directed real transportation graph of Chandigarh, India, the proposed S2RC system recommends a charging station in accordance to the preferences given by the EV user. Case studies verify the significance and validity of the proposed model in terms of energy efficiency and user convenience.

Multi-Objective Route Planning for Aircraft Taxiing Under Different Traffic Conflict Types

Aircraft taxiing in large airports is often delayed by traffic conflicts. The increased fuel consumption and pollutant emission lower the efficiency of airport surface operation and bring potential safety hazards. Previous studies on route planning for surface taxiing seldom involve refined delay analysis under different traffic conflict types and discussions on route planning at airports with various environmental parameters and under diverse aircraft types. In this study, dynamic models of crossing, head-on, and trailing conflicts during aircraft taxiing were constructed, and delays of these three conflict types were determined. The shortest route set of restricted routing was determined by the Yen algorithm according to the topological network, conflict-induced delay, and spatial distributions of taxiing at airports. Considering the three optimization objects, the aircraft route planning was optimized and determined from the shortest route set according to the differences in the environmental parameters and aircraft types. Experiment results show that the proposed method achieves 9.6–12.1% higher route planning precision in all traffic periods compared with previous route planning with considerations to taxiing conflict. This method also provides support to the dynamic decision of airport operation and control departments according to the environment and performance of aircraft.

A swarm intelligence graph-based pathfinding algorithm (SIGPA) for multi-objective route planning

Personalized tourist route planning (TRP) and navigation are online or real-time applications whose mathematical modeling leads to complex optimization problems. These problems are usually formulated with mathematical programming and can be described as NP hard problems. Moreover, the state-of-the-art (SOA) path search algorithms do not perform efficiently in solving multi-objective optimization (MO) problems making them inappropriate for real-time processing. To address the above limitations and the need for online processing, a swarm intelligence graph-based pathfinding algorithm (SIGPA) for MO route planning was developed. SIGPA generates a population whose individuals move in a greedy approach based on A∗ algorithm to search the solution space from different directions. It can be used to find an optimal path for every graph-based problem under various objectives. To test SIGPA, a generic MOTRP formulation is proposed. A generic TRP formulation remains a challenge since it has not been studied thoroughly in the literature. To this end, a novel mixed binary quadratic programming model is proposed for generating personalized TRP based on multi-objective criteria and user preferences, supporting, also, electric vehicles or sensitive social groups in outdoor cultural environments. The model targets to optimize the route under various factors that the user can choose, such as travelled distance, smoothness of route without multiple deviations, safety and cultural interest. The proposed model was compared to five SOA models for addressing TRP problems in 120 various scenarios solved with CPLEX solver and SIGPA. SIGPA was also tested in real scenarios with A* algorithm. The results proved the effectiveness of our model in terms of optimality but also the efficiency of SIGPA in terms of computing time. The convergence and the fitness landscape analysis showed that SIGPA achieved quality solutions with stable convergence.

Multi-objective Route Planning of Museum Guide based on an Improved NSGA-II Algorithm

Museums have become a hot spot of tourism. However, the uneven distribution of passenger flow makes it difficult for tourists to find out the shortest and most comfortable tour route. The multi-objective route planning strategy is proposed for the design of museum tour route. This paper presents an improved version of the NSGA-II algorithm, named adaptive 2-opt_integerated non-dominated sorting genetic (AONSGA) algorithm. Based on NSGA-II algorithm, the adaptive probability and 2-opt local search strategy is introduced. Then the computation results on benchmark multi-objective problems show that the AONSGA algorithm has better convergence and diversity performance than the NSGA-II. Whereafter, taking the Palace Museum as an example, a map is established and AONSGA is applied to carry out multi-objective guide route planning. Finally, according to different requirements of tourists, three kinds of specific schemes of the guide route in the Palace Museum are recommended.

Multi-objective Route Planning for Underwater Cleaning Robot in Water Reservoir Tank

Underwater tank cleaning using robotic method is very crucial due to the concern on the diver’s safety in undisrupted water supply operation. A Remotely Operated Underwater Vehicles (ROV) used in the tank cleaning operation however, suffers from a high operational cost due to the lack of systematic operator guidance in robot maneuvering. This paper presents a multi-objective approach in designing a Decision Support System (DSS) for underwater cleaning robot. To explore all feasible path, the path alternatives for every cleaning point in the tank is found using Probabilistic Roadmap (PRM). Then, an optimized sequential route are identified using Non-Dominated Sorting Genetic Algorithm using Reference Point Based (NSGA-III). Several objectives such as path length and routing angle are considered to be optimized, while ensuring constraints such as similar deployment point, maximum daily time limit and cable entanglement. To measure the quality of the proposed solution, comparisons have been done based on performance of NSGA-III with Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) by considering the C-Metric value, execution time and estimated cleaning duration. In addition, comparisons with conventional path by human operator is also conducted to validate the significance of DSS application in underwater tank cleaning. Results have shown that NSGA-III has superiorities with an improvement of 11.11% in cleaning time as compared to NSGA-II and 5.12% improvement compared to MOPSO.

A multi-objective coil route planning system for the steelmaking industry based on evolutionary algorithms

A multi-objective coil route planning system for the steelmaking industry based on evolutionary algorithms

Parallel Hyper-Heuristic Algorithm for Multi-Objective Route Planning in a Smart City

Most of the commercial navigation products provide route planning service for users. However, they only consider a single metric such as distance, time, or other costs, while ignoring a critical criterion: safety. In a smart city, people may prefer to find a safe walking route to avoid the potential crime risk as well as obtain a short distance. This problem can be specified as a multi-objective optimization problem (MOOP). Many methods were proposed in the past to solve the multi-objective route planning, the multi-objective evolutionary approach (MOEA) is considered as the most popular one. However, MOEA is non-optimized when used in a large-scale road network and becomes computationally expensive when handling a large population size. In this paper, we propose a multi-objective hyper-heuristic (MOHH) framework for walking route planning in a smart city. In the search framework, we design a set of low level heuristics to generate new routes. Moreover, we adopt reinforcement learning mechanism to select good low-level heuristics to accelerate searching speed. We further improve the reinforcement learning-based multi-objective hyper-heuristic (RL-MOHH) algorithm and implement a parallel version (RL-PMOHH) on general purpose graphic process unit. Extensive experiments are conducted on the safety-index map constructed from the historical urban data of the New York city. Comprehensive experimental results show that the proposed RL-PMOHH is almost 173, 5.3, and 3.1 times faster than the exact multi-objective optimization algorithm, the RL-MOHH algorithm, and the parallel NSGA-II algorithm, respectively. Moreover, both RL-MOHH and RL-PMOHH can obtain more than 80% Pareto optimal solutions in a large-scale road network.

Evaluating decision making in a multi-objective route planning-task

The present study is an initial investigation of complex decision making performance. Specifically, this work investigated how individuals make decisions involving risk and uncertainty within a spatially focused route planning task involving multiple simulated unmanned vehicles and objectives. Forty-three participants were instructed to create twenty-four route plans, half of which included a combination of risky route suggestions and enhanced icons. Given the high working memory demands of developing a route for multiple vehicles, it was expected that enhanced visualizations, which provided redundant information on target priority, uncertainty, and deadline, would reduce demands on working memory and improve performance. Additionally, it was predicted that providing risky route suggestions would negatively impact performance, yet enhanced visualizations would reduce the effect of risky route suggestions. However, findings supported neither hypothesis; there was no performance difference in providing enhanced visualizations, and risky sub-optimal route suggestions actually improved the expected value of the submitted route. These results could have occurred due to multiple factors, including the multi-objective route planning task being too complex, or the scenarios lacking sufficient variability in expected value. An additional interpretation of these findings is that humans are really poor at performing these complex multi-objective tasks, and failed to comprehend the uncertainty and risk inherent in their decisions.

Multiobjective Route Planning Model and Algorithm for Emergency Management

In order to model route planning problem for emergency logistics management taking both route timeliness and safety into account, a multiobjective mathematical model is proposed based on the theories of bounded rationality. The route safety is modeled as the product of safety through arcs included in the path. For solving this model, we convert the multiobjective optimization problem into its equivalent deterministic form. We take uncertainty of the weight coefficient for each objective function in actual multiobjective optimization into account. Finally, we develop an easy-to-implement heuristic in order to gain an efficient and feasible solution and its corresponding appropriate vector of weight coefficients quickly. Simulation results show the effectiveness and feasibility of the models and algorithms presented in this paper.

Interactive Multi-objective Route Planning for Sightseeing on Time-expanded Networks under Various Conditions

This paper proposes a versatile and interactive route planning problem for sightseeing under various conditions based on constraints of required traveling times and total satisfaction of sightseeing activities, which are time-dependent. In general, traveling times among sightseeing places and the satisfactions of activities also change dependent on various conditions such as weather, climate and season. It is important to represent a satisfying route to ensure the large satisfaction even if any assumable condition happens. In order to incorporate the above-mentioned situations, a multi-objective route planning problem is formulated as a network optimization problems based on Time-Expanded Network to represent time-dependent parameters in a static network. Furthermore, an interactive approach based on the Satisficing Trade-Off Method is introduced to transform the multi-objective into the single. In addition, the proposed problem is equivalently transformed into an extended model of network optimization problems by introducing parameters, and the interactive algorithm is developed.

An Interactive Algorithm for Multi-objective Route Planning

We address the route selection problem for Unmanned Air Vehicles (UAV) under multiple objectives. We consider a general case for this problem, where the UAV has to visit several targets and return to the base. We model this problem as a combination of two combinatorial problems. First, the path to be followed between each pair of targets should be determined. We model this as a multi-objective shortest path problem. Additionally, we need to determine the order of the targets to be visited. We model this as a multi-objective traveling salesperson problem (MOTSP). The overall problem is a combination of these two problems, which we define as a generalized MOTSP. We develop an exact interactive approach to identify the best paths and the best tour of a decision maker under a linear utility function.

Bi-level GA and GIS for Multi-objective TSP Route Planning

Route planning is usually carried out to achieve a single objective such as to minimize transport cost, distance traveled or travel time. This article explores an approach to multi-objective route planning using a genetic algorithm (GA) and geographical information system (GIS) approach. The method is applied to the case of a tourist sight-seeing itinerary, where a route is planned by a tour operator to cover a set of places of interest within a given area. The route planning takes into account four criteria including travel time, vehicle operating cost, safety and surrounding scenic view quality. The multi-objective route planning in this paper can be viewed as an extension of the traditional traveling salesman problem (TSP) since a tourist needs to pass through a number of sight points. The four criteria are quantified using the spatial analytic functions of GIS and a generalized cost for each link is calculated. As different criteria play different roles in the route selection process, and the best order of the multiple points needs to be determined, a bi-level GA has been devised. The upper level aims to determine the weights of each criterion, while the lower level attempts to determine the best order of the sights to be visited based on the new generalized cost that is derived from the weights at the upper level. Both levels collaborate during the iterations and the route with the minimal generalized cost is thus determined. The above sight-seeing route planning methodology has been examined in a region within the central area of Singapore covering 19 places of interest.