Modelling and solving profit-oriented U-shaped partial disassembly line balancing problem

Disassembly lines are utilized frequently to disassemble the end-of-life products completely or partially to retain the valuable components for remanufacturing or recycling. This research introduces and solves the profit-oriented U-shaped partial disassembly line balancing problem (PUPDLBP) for the first time. A 0–1 integer linear programming model is formulated to tackle the PUPDLBP with AND/OR precedence, which is capable of solving the small-size instances optimally. As the considered problem is NP-hard, a novel discrete cuckoo search (DCS) algorithm is implemented and improved to solve the considered problem. The proposed DCS employs a two-phase decoding procedure to handle the precedence constraint, and new population update and new method to select and replace the abandoned individuals to achieve the proper balance between exploitation and exploration. Case studies demonstrate that the U-shaped line might obtain the larger total profit than a straight line. The comparative study shows that the improvements enhance the performance of DCS by a significant margin. The proposed algorithm outperforms CPLEX solver when solving large-sized instances and produce competing performance in comparison with 11 other algorithms

Checkpointing schemes for grid workflow systems

One of the major challenges in wide use of Grid workflow systems is fault tolerance and avoidance. Checkpointing schemes provide a way of fault detection and recovery. In our research, we focus on performance optimization of checkpointing schemes and DVS (Dynamic Voltage Scaling) for Grid workflow systems. We propose offline checkpointing schemes with DVS and online adaptive checkpointing schemes that dynamically adjust the checkpointing intervals by using store-checkpoints (SCPs) and compare-checkpoints (CCPs). When combined with DVS, offline adaptive checkpointing schemes not only are fault tolerant but also lead to reduce average execution time of tasks. These schemes can efficiently utilize comparison and storage operations and significantly improve the performance. Further, these schemes can calculate the optimal numbers of checkpoints by which minimize the mean execution time. We also expand the online adaptive checkpointing schemes from single-task execution scenarios to multi-task execution scenarios. Simulation results show these online schemes outstandingly increase the likelihood of timely task completion when faults occur

A machine learning-driven framework for the property prediction and generative design of multiple principal element alloys

Multi-principal element alloys (MPEAs), inclusive of so-called high entropy alloys (HEAs), represent an innovative class of metallic materials that reveal unique properties and potentially broad applicability. However, the compositional complexity of MPEAs presents challenges in discerning physical mechanisms that control properties, and in harnessing such mechanisms to drive the design of new alloys. An ability to design metallic alloys that possess user-defined requisite properties has emerged as a critical area of interest within the field of materials science and engineering. This research illustrates how the integration of data science, machine learning (ML), and generative design strategies can evolve alloy design to a predictive, data-oriented approach. A comprehensive workflow utilising machine learning for feature analysis, property prediction, and generative design of novel MPEA generation is proposed. This workflow facilitates the examination and comparison of the predictive capabilities of ML models in determining the mechanical properties of MPEAs, providing insights into the influence of different design parameters. Additionally, by integrating a generative adversarial model, the prediction of novel MPEAs and anticipate their mechanical behaviours is revealed

A cross-authentication model and implementation

The current status is that there is coexistence of different authentication mechanisms in heterogeneous domains. We have noticed that the there are [sic] little work has been done in cross-authentication for heterogeneous domains. In this paper, we target on this problem, and propose a cross-authentication model for heterogeneous domains on active networks. We implement our model with the method of system redundancy. We make the simulation of active networks under Windows environment. Moreover, we give out the security proof of our model. Our system implements mutual entity authentication among heterogeneous domains based on PKI and ID-PKC. The theoretical analysis and the preliminary experiments demonstrate that the proposed system possesses the properties of high security and stability

Simulation and analysis of DDos in active defense environment

Currently there is very few data that can describe the whole profile of a DDoS attack. In this paper, the active DDoS defense system deploys a number of sub-systems, such as Flexible Deterministic Packet Marking (FDPM) and Mark-Aided Distributed Filtering (MADF). In addition, two DDoS tools, TFN2K and Trinoo, are adopted and integrated into SSFNet to create virtual DDoS networks to simulate the attacks. Then, simulation experiments are used to evaluate the performance of the active DDoS defense system. At last, we set up a model to describe the interactions between DDoS attack and defense party, which allows us to have a deep insight of the interactions between the attack and defense parties. Experiment results shows that the model can precisely estimate the defense effectiveness of the system when it encounters attacks

Uncertain times and the insider perspective

This paper examines insiders' informational privilege by studying the nexus between aggregated self-reported insider trades and Economic Policy Uncertainty (EPU). We demonstrate that firm insiders act in response to the first signs of uncertainty as it appears in the media, and high-ranked managers, such as CEOs and CFOs, react more promptly than other insiders. Our findings further support the idea that insiders' indirect informational advantages allow them to interpret the significance of public information for cash flows more accurately in their own companies. Our study is the first to examine insiders' behavior using pure public information; it is also the first to exclude the influence of private information completely. We also consider various measures of EPU, including global and categorical indices representing economic, political uncertainty, while taking the financial crisis period into account.</p

Model and migrating birds optimization algorithm for two-sided assembly line worker assignment and balancing problem

Worker assignment is a relatively new problem in assembly lines that typically is encountered in situations in which the workforce is heterogeneous. The optimal assignment of a heterogeneous workforce is known as the assembly line worker assignment and balancing problem (ALWABP). This problem is different from the well-known simple assembly line balancing problem concerning the task execution times, and it varies according to the assigned worker. Minimal work has been reported in worker assignment in two-sided assembly lines. This research studies worker assignment and line balancing in two-sided assembly lines with an objective of minimizing the cycle time (TALWABP). A mixed-integer programming model is developed, and CPLEX solver is used to solve the small-size problems. An improved migrating birds optimization algorithm is employed to deal with the large-size problems due to the NP-hard nature of the problem. The proposed algorithm utilizes a restart mechanism to avoid being trapped in the local optima. The solutions obtained using the proposed algorithms are compared with well-known metaheuristic algorithms such as artificial bee colony and simulated annealing. Comparative study and statistical analysis indicate that the proposed algorithm can achieve the optimal solutions for small-size problems, and it shows superior performance over benchmark algorithms for large-size problems

On the effectiveness of flexible deterministic packet marking for DDoS defense

IP traceback is one of the defense mechanisms for Distributed Denial of Service (DDoS) attacks. However, most traceback schemes consume extensive resources such as CPU, memory, disk storage and bandwidth and require a large amount of IP packets to reconstruct sources, which makes them impractical and ineffective. In this paper, we present a new flexible IP traceback scheme called Flexible Deterministic Packet Marking (FDPM). The flexibilities of FDPM are in two ways, one is that it can adjust the length of marking field according to the network protocols deployed, thus it can work well even in an environment with different network protocols; the other is that it can adjust the marking rate according to the load of participating router, while it still can maintain the marking function. In order to verify the effectiveness of FDPM for DDoS defense in terms of marking efficiency, maximum forwarding rate, and number of packets for reconstruction, we tested FDPM by both simulation and Linux router implementation with an emphasis on the latter. The experiments demonstrate that the built-in overload prevention mechanism, flow-based marking, can isolate and mark the most possible DDoS attack packets, while keeping the load of the participating router in a reasonably low degree. The real hardware implementation confirms that this flexible capability is important when traceback mechanisms are used in a real DDoS defense scenario

Pinching hysteretic response of yielding shear panel device

The paper describes a modeling technique of the hysteretic response of yielding shear panel device (YSPD). This device is used for seismic energy dissipation in frame structures. The generalized Bouc–Wen–Baber–Noori (BWBN) hysteretic model is adopted in this work. Simulink is used to develop the BWBN model of the YSPD. The model parameters are calibrated based on experimental results conducted on the YSPD. The developed hysteretic model of the YSPD is then incorporated in state-space approach to evaluate the response of dissipative structures. Assessment of effectiveness of the YSPD in alleviating structural response and the effect of pinching on the overall response of the structure is made

Metaheuristic algorithms for balancing robotic assembly lines with sequence-dependent robot setup times

Industries are incorporating robots into assembly lines due to their greater flexibility and reduced costs. Most of the reported studies did not consider scheduling of tasks or the sequence-dependent setup times in an assembly line, which cannot be neglected in a real-world scenario. This paper presents a study on robotic assembly line balancing, with the aim of minimizing cycle time by considering sequence-dependent setup times. A mathematical model for the problem is formulated and CPLEX solver is utilized to solve small-sized problems. A recently developed metaheuristic Migrating Birds Optimization (MBO) algorithm and set of metaheuristics have been implemented to solve the problem. Three different scenarios are tested (with no setup time, and low and high setup times). The comparative experimental study demonstrates that the performance of the MBO algorithm is superior for the tested datasets. The outcomes of this study can help production managers improve their production system in order to perform the assembly tasks with high levels of efficiency and quality