Guanlong Deng

As an extension of the no‐wait job shop scheduling problem, the distributed no‐wait job shop scheduling problem (DNWJSP) combining distributed scheduling with no‐wait constraint exists commonly in real‐world manufacturing. In this study, we formulate a mixed‐integer linear programming (MILP) model for the problem and propose a Q‐learning‐based multi‐phase grey wolf optimization (QMGWO) algorithm. First, the algorithm consists of two phases: the hunting phase and the local search phase. In the hunting phase, the information from three best solutions in the population is used to determine the search mode and reallocate some jobs for the current solution. In the local search phase, a local search is designed and performed on the solutions obtained from the hunting phase. Then, to prevent the algorithm from falling into local optimum, we design six local search strategies based on the key factory. Furthermore, to enhance the flexibility and efficiency of the algorithm, we propose a Q‐learning method to dynamically select an appropriate local search strategy. Finally, the experimental results and statistical analysis based on benchmark instances demonstrate that the QMGWO algorithm has a significant advantage over several other high‐performing algorithms. In addition, we validate the optimal solutions for all small instances by applying the CPLEX solver to the MILP model.

Wireless Sensor Networks (WSNs) are used for data acquisition and transmission in unmanned surface vessels (USVs). However, the openness of wireless networks makes USVs highly susceptible to false data injection (FDI) attacks during data transmission, which affects the sensors’ ability to receive real data and leads to decision-making errors in the control center. In this paper, a novel dynamic data encryption method is proposed whereby data is encrypted prior to transmission and the key is dynamically updated using historical system data, with a view to increasing the difficulty of attackers cracking the ciphertext. At the same time, a dynamic relationship is established between ciphertext, key, and auxiliary encrypted ciphertext, and an attack detection scheme based on dynamic encryption is designed to realize instant detection and localization of FDI attacks. Secondly, the H∞ fusion filter is designed to filter the external interference noise, and the real information is estimated or restored by the weighted fusion algorithm. Ultimately, the validity of the proposed scheme is confirmed through simulation experiments.

The no-wait job shop scheduling problem (NWJSP) plays a crucial role in industrial production and is an NP-hard problem. We propose a hybrid discrete artificial bee colony (HDABC) algorithm for solving the NWJSP with the total tardiness criterion. In the proposed algorithm, we first design multiple discrete operations which combine with the basic framework of the artificial bee colony algorithm. Furthermore, we propose a new selection method that allows onlooker bee to select better food sources. To determine the start times of the jobs, we introduce and adapt the left timetabling method for the tardiness objective under consideration. Experimental results show that the HDABC algorithm has better search capability than two well-performing ABC algorithms as well as an iterated greedy algorithm in solving the NWJSP with the total tardiness criterion.