This paper presents a robust method designed to detect and track a road lane from images provided by an on-board monocular monochromatic camera. The proposed lane detection approach makes use of a deformable template model to the expected lane boundaries in the image, a maximum a posteriori formulation of the lane detection problem, and a Tabu search algorithm to maximize the posterior density. The model parameters completely determine the position of the host vehicle within the lane, its heading direction and the local structure of the lane ahead. Based on the lane detection result in the first frame of the image sequence, a particle filter, having multiple hypotheses capability and performing nonlinear filtering, is used to recursively estimate the lane shape and the vehicle position in the sequence of consecutive images. Experimental results reveal that the proposed lane detection and tracking method is robust against broken lane markings, curved lanes, shadows, strong distracting edges, and occlusions in the captured road images.

Nowadays, real-time scheduling is one of the key issues in cyber-physical system. In real production, dispatching rules are frequently used to react to disruptions. However, the man-made rules have strong problem relevance, and the quality of results depends on the problem itself. The motivation of this paper is to generate effective scheduling policies (SPs) through off-line learning and to implement the evolved SPs online for fast application. Thus, the dynamic scheduling effectiveness can be achieved, and it will save the cost of expertise and facilitate large-scale applications. Three types of hyper-heuristic methods were proposed in this paper for coevolution of the machine assignment rules and job sequencing rules to solve the multi-objective dynamic flexible job shop scheduling problem, including the multi-objective cooperative coevolution genetic programming with two sub-populations, the multi-objective genetic programming with two sub-trees, and the multi-objective genetic expression programming with two chromosomes. Both the training and testing results demonstrate that the CCGP-NSGAII method is more competitive than other evolutionary approaches. To investigate the generalization performance of the evolved SPs, the nondominated SPs were applied to both the training and testing scenarios to compare with the 320 types of man-made SPs. The results reveal that the evolved SPs can discover more useful heuristics and behave more competitive than the man-made SPs in more complex scheduling scenarios. It also demonstrates that the evolved SPs have a strong generalization performance to be reused in new unobserved scheduling scenarios. INDEX TERMS Scheduling, flexible job shop, hyper-heuristic, multi-objective, genetic programming. NOMENCLATURE MO-DFJSP multi-objective dynamic flexible job shop scheduling problem MAR machine assignment rule JSR job sequencing rule SP scheduling policy GEP genetic expression programming CCGP cooperative coevolution genetic programming with two sub-populations TTGP genetic programming with single population that a GP individual contains two sub-trees NSGAII nondominated sorting genetic algorithm II SPEA2 strength Pareto evolutionary algorithm 2

In the paper, a case study focusing on multi-objective flexible job shop scheduling problem (MO-FJSP) in an aero-engine blade manufacturing plant is presented. The problem considered in this paper involves many attributes, including working calendar, due dates, and lot size. Moreover, dynamic events occur frequently in the shop-floor, making the problem more challenging and requiring real-time responses. Therefore, the priority-based methods are more suitable than the computationally intensive search-based methods for the online scheduling. However, developing an effective heuristic for online scheduling problem is a tedious work even for domain experts. Furthermore, the domain knowledge of the practical production scheduling needs to be integrated into the algorithm to guide the search direction, accelerate the convergence of the algorithm, and improve the solution quality. To this end, three multi-agentbased hyper-heuristics (MAHH) integrated with the prior knowledge of the shop floor are proposed to evolve scheduling policies (SPs) for the online scheduling problem. To evaluate the performance of evolved SPs, a 5fold cross-validation method which is frequently used in machine learning is adopted to avoid the overfitting problem. Both the training and test results demonstrate that the bottleneck-agent-based hyper-heuristic method produces the best result among the three MAHH methods. Furthermore, both the effectiveness and the efficiency of the evolved SPs are verified by comparison with the well-known heuristics and two multiobjective particle swarm optimization (MOPSO) algorithms on the practical case. The proposed method has been embedded in the manufacturing execution system that is built on JAVA and successfully applied in several manufacturing plants. INDEX TERMSScheduling, flexible job shop, multi-agent, hyper-heuristics, genetic programming. NOMENCLATURE NSGAII Nondominated sorting genetic algorithm II. SPEA2 Strength Pareto evolutionary algorithm 2. 2/3/MPGP Multi-objective cooperative coevolution genetic programming with two/three/multiple sub-populations. The associate editor coordinating the review of this manuscript and approving it for publication was Kuo-Ching Ying. 2/3/MTGP Multi-objective genetic programming with single population that an individual contains two/three/multiple sub-trees. OMOPSO Optimized multi-objective particle swarm optimization. SMPSO Speed-constrained multi-objective particle swarm optimization. MAHH Multi-agent based hyper-heuristics.

With the continuous innovation of new-generation information technology and its accelerated integration with manufacturing industry, industrial internet platforms (IIPs) are rapidly emerging worldwide. Construction and application of IIP has become a new focus in international competition for leading enterprises, and also a new direction of industrial development for many countries worldwide. However, the development of IIP is still in the stage of exploration, and the industry sector still lacks unified understanding of the IIP. Therefore, this study firstly proposes a reference architecture of IIP to clarify its framework and core functions, so as to provide a general reference model for the industry to understand and jointly promote construction of IIP. Secondly, an assessment system is proposed to evaluate the usage of IIP. The assessment framework is composed from three domains namely the foundation, key capability, value and benefit. Finally, the practical value of the reference architecture and the assessment framework of IIP is verified by an industry practice.

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