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The purpose of this paper is to provide the method and system to conduct online measurement and the characterization of temperature during printed circuit board (PCB) routing process as well as the optimization of router design based on the investigation of routing temperature.

The background of this research is introduced first. Then the method to measure the routing temperature on-line by using an infrared camera is presented. The routing process is characterized by investigating the routing temperature. Tool design optimization is conducted based on the temperature in processing PCB with aluminum substrate. Finally the concluding remarks of this research are presented.

The routing temperature can be accurately measured by an infrared camera. Routing temperature is sensitive to properties of PCB, types of router and routing parameters. Very high temperature is experienced if non-appropriate routers are used to process board with aluminum substrate. It is demonstrated by the experiments that two fluted tool, three fluted tool and coated tool with three flutes are suitable for aluminum substrate processing by considering the low temperature and the nice surface finish.

The paper highlights the key points to measure the routing temperature on-line by an infrared camera and characterize the routing process and optimize the tool design by investigating the measured temperature as well.

Order picking, the activity by which a number of goods are retrieved from a warehousing system to satisfy a number of customer orders, is an essential link in the supply chain and is the major cost component of warehousing. The critical issue is to simultaneously reduce the cost and increase the speed of the order picking activity. The main objectives of this paper are: evaluate various routing heuristics and an optimal routine in a volume‐based and random storage environment; compare the performance of volume‐based storage to random storage; and examine the impact of travel speed and picking rates on routing and storage policy performance. The experimental results show the solution gap between routing heuristics and optimal routing is highly dependent on the travel speed and picking rate, the storage policy, and the size of the pick list. In addition, volume‐based storage produced significant savings over random storage, but again these savings are dependent on the travel speed and picking rate.

The purpose of this paper is to develop a new rectilinear branch pipe‐routing algorithm for automatic generation of rectilinear branch pipe routes in constrained spaces of aero‐engines.

Rectilinear branch pipe routing that connects multiple terminals in a constrained space with obstacles can be formulated as a rectilinear Steiner minimum tree with obstacles (RSMTO) problem while meeting certain engineering rules, which has been proved to be an NP‐hard and discrete problem. This paper presents a discrete particle swarm optimization (PSO) algorithm for rectilinear branch pipe routing (DPSO‐RBPRA) problems, which adopts an attraction operator and an energy function to plan the shortest collision‐free connecting networks in a discrete graph space. Moreover, this paper integrates several existing techniques to evaluate particles for the RSMTO problem in discrete Manhattan spaces. Further, the DPSO‐RBPRA is extended to surface cases to adapt to requirements of routing pipes on the surfaces of aero‐engines.

Pipe routing numeral computations show that, DPSO‐RBPRA finds satisfactory connecting networks while considering several engineering rules, which demonstrates the effectiveness of the proposed method.

This paper applies the Steiner tree theory and develops a DPSO algorithm to plan the aero‐engine rectilinear branch pipe‐routing layouts.

A lot of recent research has been focused on developing network mobility management to support the movement of a mobile network consisting of several mobile nodes. The IETF NEMO working group proposed the NEMO basic support protocol that defines a methodology for supporting such network mobility using bi‐directional tunneling between the home agent and the mobile router. However, this protocol has been found to suffer from the so‐called ‘dog‐leg problem’, and despite alternative research efforts to solve this problem, there are still limitations in the efficiency of real time data transmission and intra‐domain communication. Accordingly, the current paper proposes a new route optimization methodology that uses unidirectional tunneling and a tree‐based intra‐domain routing mechanism. As such, the proposed scheme can provide faster signaling and data transmission and be easily extended to support micro‐mobility without any additional extensions. The performance of the proposed scheme is also evaluated to demonstrate its efficiency.

Recently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental friendliness. Considering the limited battery capacity of electric vehicles, it is vital to optimize battery charging during the distribution process.

This study establishes an electric vehicle routing model for cold chain logistics with charging stations, which will integrate multiple distribution centers to achieve sustainable logistics. The suggested optimization model aimed at minimizing the overall cost of cold chain logistics, which incorporates fixed, damage, refrigeration, penalty, queuing, energy and carbon emission costs. In addition, the proposed model takes into accounts factors such as time-varying speed, time-varying electricity price, energy consumption and queuing at the charging station. In the proposed model, a hybrid crow search algorithm (CSA), which combines opposition-based learning (OBL) and taboo search (TS), is developed for optimization purposes. To evaluate the model, algorithms and model experiments are conducted based on a real case in Chongqing, China.

The result of algorithm experiments illustrate that hybrid CSA is effective in terms of both solution quality and speed compared to genetic algorithm (GA) and particle swarm optimization (PSO). In addition, the model experiments highlight the benefits of joint distribution over individual distribution in reducing costs and carbon emissions.

The optimization model of cold chain logistics routes based on electric vehicles provides a reference for managers to develop distribution plans, which contributes to the development of sustainable logistics.

In prior studies, many scholars have conducted related research on the subject of cold chain logistics vehicle routing problems and electric vehicle routing problems separately, but few have merged the above two subjects. In response, this study innovatively designs an electric vehicle routing model for cold chain logistics with consideration of time-varying speeds, time-varying electricity prices, energy consumption and queues at charging stations to make it consistent with the real world.

A cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.

First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.

Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.

Message transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.

The paper aims to present a modified particle swarm optimization (MPSO) approach for automatic generation of near‐optimal pipe routes in constrained aero‐engine 3D rotational space.

Pipe assembly for aero‐engine is formulated as searching for the optimal pipe paths meeting certain objectives in a constrained 3D rotational space. The routing space is first modelled by grid discretization in the cylindrical coordinate system, and then is simplified into several 2D planes by mapping development. The objective function is formulated to minimize the pipe lengths and the number of pipe turns, to place pipes next to the inner jacket as close as possible, and also to make pipe trajectories closely follow around obstacle contours while avoiding collisions. Then, an MPSO approach, which adopts a discrete operator and a fixed‐length encoding mechanism, is developed to seek optimal solutions to the objective function. The convergence of MPSO is theoretically proved. Finally, numerical computations of pipe‐routing examples are conducted by using Matrix Laboratory and Unigraphics NX 4.0 system, which demonstrates effectiveness and efficiency of the proposed method.

Results show that MPSO can quickly find the optimal pipe routes meeting certain engineering constraints, and also manifests better computation convergences.

The application of the MPSO approach in pipe routing for aero‐engines is demonstrated. MPSO is a general modified particle swarm optimization version that it is not restricted to the pipe‐routing problems, and the routing approach can also be applied in similar path‐planning problems such as robot path‐planning and very large‐scale integration design.

The paper develops a new formulation for aero‐engine pipe‐routing problems, and presents an MPSO approach to find the optimal pipe paths.

Nodes in sensor networks do not have enough topology information to make efficient routing decisions. To relay messages through intermediate sensors, geographic routing has been proposed as such a solution. Its greedy nature, however, makes routing inefficient especially in the presence of topology voids or holes. In this paper we present GRAViTy (Geographic Routing Around Voids In any TopologY of sensor networks), a simple greedy forwarding algorithm that combines compass routing along with a mechanism that allows packets to explore the area around voids and bypass them without significant communication overhead. Using extended simulation results we show that our mechanism outperforms the right‐hand rule for bypassing voids and that the resulting paths found well approximate the corresponding shortest paths. GRAViTy uses a cross‐layered approach to improve routing paths for subsequent packets based on experience gained by former routing decisions. Furthermore, our protocol responds to topology changes, i.e. failure of nodes, and efficiently adjusts routing paths towards the destination.

This paper aims to present a comprehensive review in major research areas of unmanned air vehicles (UAVs) navigation, i.e. three degree-of-freedom (3D) path planning, routing algorithm and routing protocols. The paper is further aimed to provide a meaningful comparison among these algorithms and methods and also intend to find the best ones for a particular application.

The major UAV navigation research areas are further classified into different categories based on methods and models. Each category is discussed in detail with updated research work done in that very domain. Performance evaluation criteria are defined separately for each category. Based on these criteria and research challenges, research questions are also proposed in this work and answered in discussion according to the presented literature review.

The research has found that conventional and node-based algorithms are a popular choice for path planning. Similarly, the graph-based methods are preferred for route planning and hybrid routing protocols are proved better in providing performance. The research has also found promising areas for future research directions, i.e. critical link method for UAV path planning and queuing theory as a routing algorithm for large UAV networks.

The proposed work is a first attempt to provide a comprehensive study on all research aspects of UAV navigation. In addition, a comparison of these methods, algorithms and techniques based on standard performance criteria is also presented the very first time.