Route Planning Method Research Articles

A Method for Evacuation Route Planning in Disaster Situations

In this paper, the design and analysis of evacuation routes in transportation networks in cases of natural disasters is examined. A method for evacuation route planning in disaster situations is suggested. This method applies iteratively a heuristic algorithm to define two independent paths from the disaster area to each shelter for vehicle flow allocation in evacuation planning, considering both travelling time and capacity of the transportation network as parameter for analysis. Routes to different shelters cannot present intersection points either in order to allow continuous traffic flow and reduce potential accidents. An evacuation planning exercise is also presented.

Automotive Route Planning Method for Minimizing the Radar Detection of an Armed Helicopter

Radar systems are used by air defense forces to detect and track aircraft. Route planning attempts to enhance safety by minimizing the exposure of the aircraft to the threat air defense systems while achieving the mission objectives. Based on Voronoi diagram theory, a flight route optimization method is introduced to insure the armed helicopter to penetrate the emery’s radar defense system at the lowest danger. The factors, such as radar cross section and distance between helicopter and radar, that affect the radar detection are firstly analyzed. Then Voronoi diagram and its application for route planning to minimize the detection probability of an armed helicopter are described introduced in details. Dijkstra algorithm is employed to solve the optimization problem and to obtain the initial flight path. The simulation of artificial potential field dynamic is performed to transform the initial path to the smooth flight route. Example analysis of a hypothesis armed helicopter shows that the developed method is helpful for the on-the-spot planning that combines aircraft flight performance and intelligence information regarding the estimated enemy weapon locations to develop routes that avoid or mask the enemy sensor and weapon envelopes.

Comparison Process Long Execution Between PQ Algorthm And New Fuzzy Logic Algorithm For VOIP

The transmission of voice over IP networks can generate network congestion due to weak supervision of the traffic incoming packet, queuing and scheduling. This congestion negatively affects the Quality of Service (QoS) such as delay, packet drop and packet loss. Packet delay effects will affect the other QoS such as: unstable voice packet delivery, packet jitter, packet loss and echo. Priority Queuing (PQ) algorithm is a more popular technique used in the VoIP network to reduce delays. In operation, the PQ is to use the method of sorting algorithms, search and route planning to classify packets on the router. Thus, this packet classifying method can result in repetition of the process. And this recursive loop leads to the next queue starved. In this paper, to solving problems, there are three phases namely queuing phase, classifying phase and scheduling phase. The PQ algorithm technique is based on the priority. It will be applied to the fuzzy inference system to classify the queuing incoming packet (voice, video and text); that can reduce recursive loop and starvation. After the incoming packet is classified, the packet will be sent to the packet buffering. In addition, to justify the research objective of the PQ improved algorithm will be compared against the algorithm existing PQ, which is found in the literature using metrics such as delay, packets drop and packet losses. This paper described about different execution long process in Priority (PQ) and our algorithm. Our Algorithm is to simplify process execution Algorithm that can cause starvation occurs in PQ algorithm.

An interval-parameter mixed integer multi-objective programming for environment-oriented evacuation management

Large crowds are increasingly common at political, social, economic, cultural and sports events in urban areas. This has led to attention on the management of evacuations under such situations. In this study, we optimise an approximation method for vehicle allocation and route planning in case of an evacuation. This method, based on an interval-parameter multi-objective optimisation model, has potential for use in a flexible decision support system for evacuation management. The modeling solutions are obtained by sequentially solving two sub-models corresponding to lower- and upper-bounds for the desired objective function value. The interval solutions are feasible and stable in the given decision space, and this may reduce the negative effects of uncertainty, thereby improving decision makers’ estimates under different conditions. The resulting model can be used for a systematic analysis of the complex relationships among evacuation time, cost and environmental considerations. The results of a case study used to validate the proposed model show that the model does generate useful solutions for planning evacuation management and practices. Furthermore, these results are useful for evacuation planners, not only in making vehicle allocation decisions but also for providing insight into the tradeoffs among evacuation time, environmental considerations and economic objectives.

An inexact optimization model for evacuation planning

Purpose – The purpose of this paper is to develop an interval method for vehicle allocation and route planning in case of an evacuation.Design/methodology/approach – First, the evacuation route planning system is described and the notations are defined. An inexact programming model is proposed. The goal of the model is to achieve optimal planning of vehicles allocation with a minimized system time under the condition of inexact information. The constraints of the model include four types: number of vehicles constraint, passengers balance constraints, maximum capacity of links constraints and no negative constraints. The model is solved through the decomposition of the inexact model. A hypothetical case is developed to illustrate the proposed model.Findings – The paper finds that the interval solutions are feasible and stable for evacuation model in the given decision space, and this may reduce the negative effects of uncertainty, thereby improving evacuation managers' estimates under different conditions....

Heuristic-Based Dynamic Route Planning Method for a Homogeneous Multi-robot Team

Multi-robot systems have recently received a great deal of attention due to the ability to perform an assigned task in a more reliable, faster and cheaper way beyond what is possible with a single robot. However, they may have some drawbacks, such as obstruction among robots during a task. Non-intersecting tours are preferable for the robots to prevent obstruction. Moreover, after an initial plan, the environment (open roads may be closed) and/or task requests (active/deactive) may change dynamically, which may require a fast tour construction for the members of the mobile robot group. In this study, a novel heuristic method is proposed to construct non-intersecting tours for the members of a mobile robot group in dynamic and/or partially unknown environments considering the energy capacities of the robots. It is an aggregate algorithm consisting of the Savings algorithm and the Sweep algorithm, and is applicable to the problems that are modeled using complete, sparse, directed or undirected networks. Simulations are offered to show the effectiveness of the proposed algorithm.

Order-batching methods for an order-picking warehouse with two cross aisles

Order batching is one of the methods used in warehouses to minimize the travel distance of pickers. In this paper, we focus on developing order-batching methods for an order-picking warehouse with two cross aisles and an I/O point at one of its corners. Each of these methods is made up of one seed-order selection rule and one accompanying-order selection rule. Eleven seed-order selection rules and 14 accompanying-order selection rules are studied here. These rules include those newly proposed by us and those by others. Rules proposed by others have been shown to perform well in minimizing the travel distance of pickers. They are included here for the comparison purpose. Unlike previous studies that only focus on developing aisle or location-based rules, this study also develops rules that are distance- or area-based. In addition, two different route-planning methods and two different aisle-picking-frequency distributions are considered in this paper. This study’s objective is to investigate not only the performance of seed-order selection rules and accompanying-order selection rules, but also the mutual effects between route-planning methods, aisle-picking-frequency distributions, seed-order selection rules, and accompanying-order selection rules on their performance. The result of this study shows that some of the newly proposed rules outperform those from other studies. It also shows that seed-order selection rules and accompanying-order selection rules significantly affect each other’s performance. Lastly, the performance rankings of seed-order selection rules and accompanying-order selection rules are affected by aisle-picking-frequency distributions, but not by route-planning methods.

3214 遂次変化する環境における除雪ロボットのための経路計画と誘導制御に関する研究(J25-2 メカニカルシステムとその知能化(2),J25 メカニカルシステムとその知能化)

3214 遂次変化する環境における除雪ロボットのための経路計画と誘導制御に関する研究(J25-2 メカニカルシステムとその知能化(2),J25 メカニカルシステムとその知能化)

A study on order-batching methods of order-picking in a distribution centre with two cross-aisles

One common practice in order picking is order batching, in which items of two or more orders are picked together in one picking trip. Order batching can reduce the total order-picking travel distance if orders with similar picking locations are batched together and picked in the same order-picking trip. In this paper, the performance of different order-batching methods that are made up of one seed-order selection rule and one accompanying-order selection rule is investigated. A seed-order selection rule selects the first order (i.e. the seed order) in an order batch, while an accompanying-order selection rule selects the rest of orders (i.e. the accompanying orders) to be added to the order batch. In this paper, one investigates the performance of nine seed-order selection rules and 10 accompanying-order selection rules under two different route-planning methods and two different aisle-picking-frequency distributions. The problem environment is a distribution centre's warehouse which has an I/O point at one of its corners and two cross-aisles—one front cross-aisle and one back cross-aisle. One wants to understand not only the performance of every seed-order selection rule and every accompanying-order selection rule, but also their combined performance. The effects of route-planning methods and aisle-picking-frequency distributions on the performance of seed-order selection rules and accompanying-order selection rules are also investigated. Different random problems were generated and tested for this purpose. It is hoped that the knowledge learned from this study can benefit practitioners in distribution centres with order-batching operations.

Distributed route planning for multiple mobile robots using an augmented Lagrangian decomposition and coordination technique

To enable efficient transportation in semiconductor fabrication bays, it is necessary to generate route planning of multiple automated guided vehicles (AGVs) efficiently to minimize the total transportation time without collision among AGVs. In this paper, we propose a distributed route-planning method for multiple mobile robots using an augmented Lagrangian decomposition and coordination technique. The proposed method features a characteristic that each AGV individually creates a near-optimal routing plan through repetitive data exchange among the AGVs and local optimization for each AGV. Dijkstra's algorithm is used for local optimization. The optimality of the solution generated by the proposed method is evaluated by comparing the solution with an optimal solution derived by solving integer linear programming problems. A near-optimal solution, within 3% of the average gap from the optimal solution for an example transportation system consisting of 143 nodes and 14 AGVs, can be derived in less than 5 s of computation time for 100 types of requests. The proposed method is implemented in an experimental system with three AGVs, and the routing plan is derived in the configuration space, taking the motion of the robot into account. It is experimentally demonstrated that the proposed method is effective for various problems, despite the fact that each route for an AGV is created without minimizing the entire objective function.

A DESIGN METHODOLOGY FOR CONVERTING A REGULAR WAREHOUSE INTO A ZONE-PICKING WAREHOUSE

ABSTRACT In this paper, we study the problem of converting a regular warehouse into a zone-picking warehouse. A designed methodology that divides this problem into two sub-problems is proposed. The first sub-problem is to determine the items in each zone. A clustering procedure consisting of a similarity coefficient measure and a clustering algorithm is proposed to solve this problem. Six similarity coefficients and two clustering algorithms are proposed and studied. The second sub-problem is to determine the storage locations of the items in each zone. For this problem, two storage-location assignment rules are proposed and studied. Since it is assumed that order batching is adopted in the warehouse, two order-batching methods which are different seed-order selection rules are considered in this study. Furthermore, two route-planning methods are investigated. Totally, there are 96 combinations of these methods. Each combination is tested by thirty randomly generated test problems. The testing results are collected and analyzed to understand not only each method's total order-picking travel distance performance, but also their mutual effects. Finally, we also solve the storage-location assignment problem for each test problem without dividing the warehouse into different zones, so that we can see how much improvement can be achieved by converting a regular warehouse into a zone-picking warehouse.

Dynamic Routing Decisions for Commercial Vehicle Operations in Real-Time Traffic Conditions

The application of intelligent transportation system technologies to facilitate freight mobility requires dynamic routing decisions to reduce operational costs and enhance service levels. Although most route planning or scheduling methods studied in the literature assume fixed or static travel times for the networks, an attempt is made to focus on the dynamic routing decisions in response to real-time information. A microscopic traffic simulation model coupled with a link-to-link shortest-path search with turning penalties is suggested and developed to evaluate the potential benefits of the dynamic routing decisions, which considers both updated link travel times and the incident conditions detected. The simulation results are described and discussed. Some interesting issues for future research needs are also recommended. This research will help logistics companies assess the benefits of the routing advisory systems that they use for their day-to-day operations.

An Agent-based Transportation Route Planning Method for Led Fabricating Line and Its Evaluation Using Lagrangian Relaxation.

In most of Light Emitting Diode (LED) semiconductor fabricating line, AGVs (Automated Guided Vehicles) are used to transport the products between the facilities in order to ensure dust-free operation in the transportation systems. It has been demanded to realize the route planning system which speedily derives a feasible transportation route so that each AGV does not interfere with other AGVs. In this paper, we applied an agent-based optimization method for the route planning problems with multiple AGVs in LED fabricating bay in which the total transportation time is minimized. In the agent-based method, each AGV agent independently searches its candidate of route. The imitated pheromone is effectively used to generate the route which avoids the collision among AGVs. In order to evaluate the performance of the proposed method, the lower bound of the original problem is obtained by using the Lagrangian relaxation technique. The performances of the proposed method are evaluated by using the value of duality gap for a large scale problem. The effectiveness of the agent-based optimization method is demonstrated.

複数台移動ロボットの搬送経路計画問題に対する自律分散型最適化法

複数台移動ロボットの搬送経路計画問題に対する自律分散型最適化法

Methods of Optimal Ship Routing for Weather Perturbations

The article discusses sea route planning and optimisation methods which take into account minimisation of the time needed to pass a so called time-minimum route at dynamically changing weather conditions. The above task is a kind of dynamic optimisation problem in which dynamic changes in weather conditions are defined as moving constraints. The effect of hydro-meteorological conditions on changes in the parameters of ship motion and route was determined. The obtained solutions and authors' experience gained during studying the presented methods have made the basis for developing new evolutionary algorithms which were not used so far in planning ship routes.

Simplified Public Transport Route PlanningMethod: A Study Case In Bandung (Indonesia)

Public transport is being suspected as one of the congestion's trouble makers in many cities in Indonesia. This paper will explain a method of public transport route planning by combining an 'optimisation' technique with a superimpose technique to obtain some best planned routes which will increase the efficiency and the level of service of public transport. The method has been applied in Bandung (Indonesia) and some results will be explained in a great detail.