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This paper addresses the state estimation of timed discrete event systems, where the occurrence of an event is associated with a time delay and the initial state of the system belongs to a set of possible initial states. A discrete event system is modeled with a bounded timed labeled Petri net whose unobservable subnet is acyclic, and the behavior of the system under a global time clock can be observed. The procedure for state estimations of partially observed Petri nets is refined by the introduction of time in discrete event systems. We apply the symbolic techniques to the representation of states and transition relations of bounded timed labeled Petri nets. Then we propose an online algorithm for the computation of state estimations generated by timed observations. [ABSTRACT FROM AUTHOR]

It is theoretically and practically significant to synthesize a maximally permissive (optimal) controller to prevent deadlocks in an automated manufacturing system (AMS). With an AMS being modeled with Petri nets, by the existing methods, integer linear programming (ILP) problems are usually formulated and solved to obtain optimal policies by forbidding illegal markings at the same time no legal marking is excluded. Without an efficient technique for solving an ILP, such a method is usually computationally prohibitive. A resource-oriented Petri net (ROPN) is employed to model a class of AMS for resolving the deadlock control problem with maximal permissiveness in this paper. Efficient methods are developed to figure out the key structures in an ROPN model for deadlock prevention. Based on the structural properties of ROPN models, this work explores several types of illegal markings that can be prohibited optimally by structural analysis. For these markings, a deadlock prevention policy can be derived in an algebraic way without solving a notorious ILP problem. For the other markings, linear programming (LP), instead of ILP, approaches are developed to forbid them optimally. Thus, a maximally permissive controller can be developed while the computational cost is reduced greatly. The proposed methods are verified by typical examples in the literature. • Resource-oriented Petri nets are used to model systems for deadlock prevention. • Various types of illegal markings that can be simply prevented are structurally identified. • It finds that most of illegal markings can be prevented by using polynomial algorithms. • The other illegal markings can be prevented by solving a linear programming. [ABSTRACT FROM AUTHOR]

For sustainable development, a refinery is required to save energy as much as possible so as to reduce the emission of greenhouse gas. In crude oil operations, oil transportation from storage tanks to charging tanks via a pipeline consumes a large portion of energy. It is vitally important to minimize energy consumption for this process. Since the oil flow resistance is proportional to the square of oil flow rate, the relation between energy efficiency and flow rate is nonlinear, which makes the problem complicated. This work addresses this important issue by formulating a linear programming model for the considered problem such that it can be efficiently solved. A real-world industrial case study is used to demonstrate the applications and significance of the proposed method. [ABSTRACT FROM AUTHOR]

• It addresses a class of two-stage flow shops with no-interruption requirement. • With feasibility conditions derived, an adaptive genetic algorithm is constructed. • The probability of crossover and mutation operations are adaptively adjusted. • A local search method is introduced to further improve the performance. • Extensive experiments demonstrate the efficiency and effectiveness. It is a trend for a production system to produce multiple product types with small batches. Thus, there are frequent switches between batches. Due to the different product types in different batches, setups may be necessary for a batch changeover. Based on practical applications, we investigate the problem of scheduling a class of two-stage hybrid flow shops, where there is significant setup time at the first stage, while the second stage should operate continuously without interruption. To solve this problem, after deriving the existence conditions of a feasible schedule, an adaptive genetic algorithm is designed to dynamically adjust the probabilities of crossover and mutation operations according to the population diversity. In this way, the whole solution space could be fully explored so as to get an optimal or near optimal solution for large-size practical problems. Also, based on the characteristics of solutions, a local search method is presented to further improve the solution accuracy. Experiments are done to test the efficiency and effectiveness of the proposed method. Results show that the algorithm can find good solutions with a gap of 1% with the lower bound within three minutes, i.e. , it is efficient and effective. [ABSTRACT FROM AUTHOR]

Abstract: For short-term scheduling of crude oil operations, oil residency time and high fusion crude oil transportation constraints are difficult to model. With high setup cost for high fusion point oil transportation, it is desired that the volume of such oil should be transported as much as possible by a single setup. To do so and obtain a feasible schedule, charging tank cycling is an effective strategy. With a hybrid Petri net model, scheduling analysis is carried out and schedulability conditions under a charging tank cycling strategy are presented. They can be used for determining a refining schedule and checking if a target refining schedule is realizable. If so, a detailed feasible schedule can be easily obtained by creating the operation decisions one by one and put into immediate industrial use. [Copyright &y& Elsevier]

With the increasing demand for automotive glass, improving the efficiency of automotive glass manufacturing systems can make full use of production resources and reduce the waste of natural and social resources. Therefore, this work aims to provide an efficient method for a real-world two-stage hybrid flow shop scheduling problem with small batches in an automotive glass manufacturing system. For the investigated problem, there is a significant setup time at the first stage, the second stage is served by machines that should not be interrupted, and each batch has a due date. Such constraints make this scheduling problem challenging. To solve this problem, a mixed integer linear programming model is established. Also, two properties and three theorems are given to enhance the problem-solving process. Subsequently, an efficient genetic algorithm is carefully designed to solve large-scale problems by considering the properties of the system. Meanwhile, an improvement scheme is proposed to decrease the running time of the algorithm, and experimental results show that this scheme can reduce the running time by 280 s at most from the average results of different scale problems. Finally, extensive experiments are carried out and a real-world case is solved to demonstrate the efficiency and effectiveness of the designed genetic algorithm. Also, the Taguchi method is adopted to tune the parameters for the designed algorithm. [ABSTRACT FROM AUTHOR]

In this study, we investigate the multi-objective surgery scheduling and rescheduling problems with considering medical staff dissatisfaction and fuzzy surgery time. Rescheduling is activated when emergency patients arrive. First, a multi-objective mathematical model is established for maximizing the average patient satisfaction, and minimizing the fuzzy maximum completion time and total medical cost, simultaneously. Second, five meta-heuristics are employed and improved to solve the concerned problems. Five heuristic rules are developed to improve the diversity and quality of initial solutions. For improving the performance of meta-heuristics, six local search operators are designed and two Q-learning-based strategies are developed to select optimal ones intelligently. Finally, 29 instances with different scales are used to verify the performance of the proposed algorithms. Compared with the basic meta-heuristics, the average performance of the algorithms with the second Q-learning-based strategy is improved by 62.5%, 62.1%, 50%, 70.7%, and 70.7%, respectively. Through the Friedman test, the asymptotic significance values of both metrics (0.034 and 0.000) are less than 0.05, indicating that there is a significant performance gap among five algorithms with the second Q-learning-based strategy. The average rank values of the teaching-learning-based optimization with the second Q-learning strategy are 3.7069 and 2.0690 for two metrics, which are better than the compared ones. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of fault identification in a system. The system is originally modeled by a Petri net, called a nominal (fault-free) net, and faults are considered as unobservable transitions not contained in the nominal net. It is assumed that partial places of the nominal net are observable and the output of the system is defined as an observed evolution , i.e., a sequence involving transitions and markings of the observable places. When faults occur, the observed evolution cannot be generated by the nominal net. We provide an approach that identifies unobservable transitions by constructing and solving an Integer Linear Programming problem according to the observed evolution and the nominal net. A faulty net is obtained by adding the identified unobservable transitions to the nominal one such that it coincides with the observed evolution. In addition, two methods to ensure acyclicity of the identified subnet, i.e., a net that includes unobservable transitions only, are reported. [ABSTRACT FROM AUTHOR]

For sustainability, many studies have been conducted on energy saving for HVAC (heating, ventilation, and air-conditioning) systems. In a public place, different persons may have different thermal comfort requirements. Few studies are done so far for energy saving in a public place with HVAC systems that can accommodate many persons with thermal sensation differences for different persons being taken into account. In this work, during the summer from May to September in 2016, an investigation for energy saving is done in a large library room at Macau University of Science and Technology, Macau, China. In such a large room, the temperature in different areas is different and many students with different thermal sensations study in the room at the same time. Under such an environment, to save energy as well as maximize the total thermal satisfactory rate, a large number of experiments are carried out. Based on the collected data, methods are given to predict the indoor temperature for each reading area and thermal satisfactory rate under the current indoor temperature. Then, an efficient algorithm is developed to maximize the thermal satisfactory rate for the entire room by optimizing the input temperature of the ACC (air-conditioning control) system such that energy can be saved. Results show that, under the worst situation, by comparing with the manual control, the daily electricity saving is about 93 kWh and the electricity saving during the period from May to September is about 13,950 kWh. [ABSTRACT FROM AUTHOR]

As the number of electric vehicles (EVs) increases rapidly, the problem of electric vehicle charging has widely become a concern. Therefore, considering the fact that charging time for one EV cannot be shortened quickly and the number of charging stations will not expand rapidly, how to schedule charging operations of electric vehicles in urban areas becomes a very important issue, since it can improve charging efficiency and relieve charging anxiety of EV users. Up to now, there is no scheduling software tool for practical use in this field. Based on the analysis of electric vehicle charging behavior characteristics, this paper investigates the EV charging problem at the scheduling level. First, a mathematical model for coordinated charging of EVs is proposed to minimize the total charging time for a given number of vehicles. Second, an earliest finish charging scheduling algorithm is presented to solve the charging problem. Then, by considering the combinatorial nature and practical applications with large number of EVs, two practical swarm-optimization-based EV charging scheduling algorithms are proposed. A real-life case study is presented to illustrate the proposed approaches. • A traffic network model with average velocity as the weight is established. • An electric vehicle charging scheduling model is developed. • An particle swarm optimization based algorithm for the charging problem. • An enhanced algorithm for the large-scale vehicle charging scheduling. [ABSTRACT FROM AUTHOR]

This paper presents an online approach to deal with the problem of state-based K -step opacity of a discrete event system modeled with labeled Petri nets. The secret states can be described by a set of either markings or generalized mutual exclusion constraints (GMECs), which is more general than the existing approaches in which the secret states are characterized by GMECs only. We formulate an integer linear programming (ILP) model to find a marking outside the set of secret states from each of the K - delayed state estimates , avoiding a complete enumeration of reachability set. Based on the ILP model, an online algorithm is developed to verify K -step opacity of an observation (i.e., an observed sequence). As an extension of K -step opacity, we define the notion of joint K -step opacity in a decentralized structure with two local sites and extend the proposed algorithm to verify the joint K -step opacity of an observation. In addition, the relationship between joint K-step opacity and K-step opacity is formally discussed. [ABSTRACT FROM AUTHOR]

The purpose of fault prediction of discrete-event systems (DESs) is to predict the occurrence of fault in advance such that some protective actions can be taken before the occurrence of the fault. The robust predictability issue under the framework of stochastic DESs (SDESs) with model uncertainty is studied. First, the notions of (ϵ , m) − robust predictability and robust predictability of SDESs are formalized. In general, a set of stochastic systems being robustly predictable can predict the occurrences of faults in the sense of probability. Then the robust predictor and robust verifier for performing the robust prediction are constructed from the given possible stochastic systems. Particularly, the necessary and sufficient conditions for (ϵ , m) − robust predictability and robust predictability of SDESs are proposed, and an approach is presented to verify the robust predictability of SDESs with polynomial-time complexity both in the state space and in the number of all possible models. [ABSTRACT FROM AUTHOR]

• Propose a stratal slicing method to reflect the actual cutter working conditions. • The stratal slicing method has stronger geological information processing ability. • It can improve the prediction accuracy and has real-time prediction capability. • The error of combined method in real-time predictions is close to that of the manual measure. • It is expected to demonstrate excellent performance in more complex geological conditions and larger shield diameters. Monitoring the wear status of cutters is important for safe and sustainable shield construction and cost management. In this paper, an innovative stratal slicing method is proposed to convert segmented and discrete uniaxial compressive strength (UCS) test data into a sequential dataset by combining it with a geological profile. The sequential dataset not only accurately represents the changing strata conditions but also differentiates the working conditions of the disc cutters in various cutterhead areas on the excavation face. Its sequence characteristics can be better combined with shield operational parameters and time-series models for real-time prediction. Furthermore, the particle swarm optimization (PSO) algorithm was improved by adding variable inertia weights and elimination mechanisms, which effectively optimised the hyperparameters of the long short-term memory (LSTM) model. The proposed method was applied to a field tunnelling case with data collected from the Guangzhou Metro Line 18 railway. The results show that the UCS data obtained using the proposed stratal slicing method can improve the prediction accuracy compared to traditional methods and models. In particular, the combined IPSO + LSTM + horizontal summation method can obtain the most accurate prediction results and has real-time prediction capability. With the proposed stratal slicing method, the IPSO + LSTM modelling approach is generally applicable to more complex ground conditions and larger shield diameters. [ABSTRACT FROM AUTHOR]

• Propose a control framework for two-region bi-modal traffic networks with 3D-MFD. • Integrate perimeter control with regional bus service frequency setting. • Formulate dynamics of regional bus systems with regional bus frequencies. • Develop a modified GA for optimizing control inputs. Public buses and private vehicles are two main transportation modes for urban road traffic. It has strong flow interactions between the two traffic modes. From data analysis, the vehicle density of a traffic mode not only determine its flow, but also affect the flow of the other mode. With such mixed vehicle flows, the application of traditional traffic management that controls each vehicle flow system separately is limited. Thus, it is beneficial to control both bus and private vehicle flows in an integrated way. Under such bi-modal traffic management, the network efficiency cannot be reflected by vehicle flows since the occupancies of the traffic modes are greatly different. It leads to that the control objective should be necessarily based on passengers. To do so, we adopt bi-modal passenger-based macroscopic fundamental diagram (3D-pMFD) to capture the relations among vehicle accumulations and the total passenger flow in a network. From this aggregate perspective, a passenger-oriented traffic control strategy is proposed by integrating the perimeter control and the regional bus service frequency setting. The characteristics of both traffic modes, especially the operation strategy of buses, are taken into account in the model to describe the bi-modal traffic flow dynamics. A control framework comprised of optimization and simulation is established using model predictive control. The corresponding passenger-based optimization problem is solved by a modified genetic algorithm. With multiple data collected from the network of Shenzhen City in China, numerical experiments are designed to verify the validity and efficiency of the proposed integrated control strategy. Results show that: (1) the proposed strategy can maintain a high level of passenger flow during peak hour with a relatively high demand; (2) the interest of private vehicles is guaranteed even if buses are more recommended in the control strategy; (3) the crowding degree in a bus is reduced properly, which is able to improve the attraction of buses during peak hours; and (4) the proposed passenger-oriented objective results in a comprehensively good performance of the bi-modal network system. [ABSTRACT FROM AUTHOR]

Privacy protection has received widespread attention from the community of discrete event systems to protect the sensitive information of users or organizations from being leaked. The existing privacy protection methods cannot protect the state information of probabilistic discrete event systems via repeated observations, which represents the information pertaining to system resource configurations. This work introduces differential privacy into the framework of probabilistic labeled Petri nets to solve the problems pertaining to the initial state protection. For two initial states that are adjacent under a specified measure, a state differential privacy verification method is proposed by determining whether the probability distributions of observations generated from adjacent initial states are similar. An external attacker is unlikely to infer the initial state via repeated observations if the system satisfies state differential privacy for certain adjacent initial states. For a probabilistic labeled Petri net, which does not satisfy state differential privacy, a supervisory control method is proposed for enforcement. A maximally permissive controller can be constructed based on the control specification proposed in this paper. Experimental studies show that the method proposed in the paper can effectively protect the privacy of given adjacent initial states. • Differential privacy is introduced into DESs to protect the secret. • Define state differential privacy to analyze whether the secret is protected. • Verify adjacent initial state-based differential privacy in probabilistic DESs. • A maximally permissive controller is constructed for privacy enforcement. [ABSTRACT FROM AUTHOR]

System opacity is a widely studied security notion, implying that a secret behaviour of a given system cannot be seen or assessed by an external observer based on the system evolution. This work deals with the problem of current-state opacity formulation and verification in the context of discrete event systems modelled with partially observed Petri nets (POPNs) (i.e., Petri nets containing place sensors that measure the number of tokens in observable places and event sensors that indicate the firing of observable transitions). A Petri net system is recognized as current-state opaque if the current-state estimate is never entirely contained in the set of secret states. In this regard, we introduce the notion of discernible markings to design a reduced state estimator called a discernible reachability graph, and then come up with formal modelling of current-state opacity in POPN systems. The main idea of the proposed approach consists in proving that if a system is current-state opaque, its current-state estimate, possibly established by an intruder, contains at least one non-secret state. We exploit the mathematical feasibility to formulate this concept by defining and solving an integer linear programming problem with respect to a given secret and an observation sequence collected from sensors. In the light of the proposed modelling, necessary and sufficient conditions are proposed for opacity verification, and examples are given to expose the results. [ABSTRACT FROM AUTHOR]

In recent years, there are increasing concerns in energy savings for buildings since they are responsible for a large proportion of energy use. A public room in buildings could hold a number of persons who may prefer dissimilar thermal environment. Furthermore, different areas in such rooms may have different temperatures. Also, facility layout in such a room has effect on the distribution of the people in the room. Thus, it may affect its thermal environment and energy consumption as well. It is meaningful and challenging to effectively operate an air-conditioning control (ACC) system by taking the above mentioned factors into account such that the thermal environment is improved and energy is saved. With the lack of research reports on this issue, this work aims at optimally and dynamically controlling the set-point temperature of an ACC system and designing the facility layout so as to maximize the total thermal satisfaction rate (TSR) as well as energy savings. To do so, a non-linear mathematical programming model is proposed to optimize TSR by determining the set-point of an ACC system and the room layout. Then, a particle swarm optimization (PSO) algorithm is constructed to find an optimal or near optimal solution since it is hard to solve a non-linear mathematical programming problem in a reasonable time. Besides, for further energy saving, two more mathematical programming models are proposed to find a set-point of an ACC system under a given outside temperature and room layout determined by the PSO algorithm. Finally, by using a large library room at Macau University of Science and Technology (MUST) as a case, investigations with a large number of experiments are conducted to collect necessary data. Based on the data, regression analysis is done to predict its indoor temperatures in different areas and TSR at a given temperature. Numerical results show that, by the proposed method, it can improve the thermal satisfaction rate by about 27% and save the daily power cost by about 24.3% in comparison with the currently used manual control method. • Study is conducted on energy efficiency in large public rooms. • Methods are derived to predict temperature distribution and occupants' satisfaction rate. • PSO algorithm is developed for optimal input temperature control and facility layout. • The proposed approach can significantly reduce energy consumption. [ABSTRACT FROM AUTHOR]

• A framework of jointly exploiting the complementary multi-view information of original data representation and the underlying cross-view relations among data points is proposed. • The proposed method can partition unlabeled data with or without negative entries and handle complete as well as various incomplete multi-view scenarios with missing instances. • An iterative optimization algorithm is proposed for solving the objective function. • Extensive experiments on eight multi-view datasets demonstrate that the proposed method outperforms eight state-of-the-art methods. Multi-view data may lose some instances in real applications. Most existing methods for clustering such incomplete multi-view data still have at least one of the following limitations: 1) The common relations among data points across all views are ignored. 2) The complementary multi-view information of original data representation is not well exploited. 3) Arbitrary incomplete scenarios or data with negative entries cannot be handled. To address these limitations, in this paper, we propose a novel Consensus Learning approach to Incomplete Multi-view Clustering (CLIMC). Specifically, a low-dimensional consensus representation is introduced to exploit complementary multi-view information from the original feature representation of available instances by integrating index matrices into matrix factorization. In addition, by combining self-representation, index matrices, and consensus term, a consensus similarity graph is leveraged to explore the underlying cross-view relations among data points. Further, the key of the proposed CLIMC is that the consensus representation is correlated with the similarity graph by a graph Laplacian regularization. Consequently, the compactness of the low-dimensional representation and the accuracy of similarity degree of the graph are reciprocally promoted. Extensive experiments on several multi-view datasets demonstrate the effectiveness of CLIMC over state-of-the-arts. [ABSTRACT FROM AUTHOR]