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Nowadays, high-throughput screening (HTS) systems are widely used in pharmaceutical industries and laboratories for the discovery of new drugs and biomedical substances. It is important to efficiently schedule them so as to reduce the cost. In the operation of an HTS system, a microplate may visit some resources more than once and complex time window constraints are imposed on some activities and activity sequences. Moreover, with the consistency requirement, a one-microplate cyclic schedule is necessary. Thus, its scheduling problem is very challenging. This article studies the scheduling problem of an HTS system for an enzymatic assay, a typical application of HTSs, from the perspective of control theory. The system is modeled by resource-oriented Petri nets (ROPNs). With the model, necessary and sufficient conditions under which a feasible cyclic schedule exists are established. Then, we determine how many microplates should be in the system for concurrent processing and the transition firing sequence to obtain the activity sequence for an optimal and feasible schedule. In this way, a feasible and optimal cyclic schedule can be found by very simple computations, which shows that polynomial algorithms for an optimal schedule exist, while the existing studies apply mathematical programming with exponential computation complexity. Also, its efficient implementation is given. [ABSTRACT FROM AUTHOR]

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]

A maximally permissive (or optimal) supervisory control of an automated manufacturing system (AMS) modeled by Petri nets (PNs) can be usually implemented by imposing constraints in the form of a set of linear inequalities. To find such a set of linear constraints, in the existing work, an integer linear programming (ILP) problem is generally formulated and solved for some dominant markings obtained by reachability analysis and vector covering theory, which is computationally inefficient due to the combinatorial nature of solving ILPs to decide the coefficients of optimal linear constraints. This paper addresses the deadlock prevention problem for AMSs by developing efficient methods to reduce the computational overhead through the establishment of conditions on deciding the maximal permissiveness of linear constraints imposed on a system. By taking the advantage of structural properties of a PN model under consideration, we identify the most part of minimal covered illegal markings that can be optimally controlled via policies obtained by specific linear inequality constraints. Also, algorithms are developed to implement the proposed policies. The proposed approach can verify the optimality of a linear constraint efficiently without solving ILP problems. A linear programming method is developed to deal with the markings that cannot be processed by the proposed structural analysis. It is shown that for the considered class of PNs, called system of simple sequential processes with resources (S3PR), no mixed ILP problem needs to solve and the computational burden is dramatically reduced. Two examples are employed to demonstrate the efficiency of the developed method. [ABSTRACT FROM AUTHOR]

This work investigates quantitative supervisory control of discrete event systems modeled with Petri nets under the fixed‐initial‐credit energy objective. A weight function referred to as an energy function is defined on a Petri net to characterize the energy level of a transition. The proposed fixed‐initial‐credit energy problem aims to design a supervisor such that the energy level of a transition sequence in a supervised system is higher than 0 under a given initial energy level. The problem is eventually transformed into a two‐player game between a system and a supervisor; supervisor synthesis is reduced to finding a winning strategy in the two‐player game. Instead of enumerating the complete state space of the underlying Petri net, two information structures are utilized, namely the conventional basis reachability graph and the newly proposed essential marking graph, to construct two‐player games based on each of them. It is shown that a winning strategy for a supervisor decoded from the game based on the basis reachability graph of the Petri net is a solution to the problem but is in general restrictive. Further, it is shown that the set of strategies for a supervisor in the game based on the essential marking graph is consistent with that from the game based on the reachability graph of a Petri net. The two developed approaches do not require an exhaustive exploration of the state space of a plant, thus achieving higher efficiency. [ABSTRACT FROM AUTHOR]

Scheduling a cluster tool with wafer residency time constraints is challenging and important in wafer manufacturing. With a backward strategy, the scheduling problem of such single-robot-arm cluster tools is well-studied in the literature. It is much more challenging to schedule a more general case whose optimal scheduling strategy is not limited to the backward one. This work uses a timed Petri net (PN) to model the dynamic behavior of the system and presents a method to determine the optimal scheduling strategy for the system. Based on its PN model and the obtained strategy, it reveals that the key issue to schedule such a tool is to determine when and how long the robot should wait for. Based on this finding, this work establishes for the first time the necessary and sufficient conditions regarding the existence of an optimal and feasible one-wafer cyclic schedule for single-robot-arm cluster tools. It then formulates a computationally efficient linear program to find it if existing, and finally gives industrial examples to show the application and power of the proposed method. [ABSTRACT FROM AUTHOR]

Wafer revisiting and residency time constraints complicate the scheduling problem of cluster tools in semiconductor manufacturing. Random disturbance to the activity time in operating a tool further complicates such a scheduling problem. To solve this challenging problem, this paper proposes a robust real-time schedule which consists of a real-time controller (RTC) and an off-line schedule. The former is developed to offset the activity time disturbance such that the wafer sojourn time fluctuation in a process module is minimized. With the RTC, to find the off-line schedule, necessary and sufficient schedulability conditions under which a feasible schedule exists are derived and these conditions can be easily checked. Then, the off-line schedule can be efficiently found by the proposed algorithms based on nondisturbed activity time if a feasible schedule exists. With the obtained real-time schedule, it is shown that the productivity of the system is maximized. Finally, examples are used to illustrate the proposed approach. [ABSTRACT FROM AUTHOR]

Supervised cross-modal hashing has attracted many researchers. In these studies, they seek a common semantic space or directly regress the zero-one label information into the Hamming space. Although they achieve many achievements, they neglect some issues: 1) some methods of the classification task are not suitable for retrieval tasks, since they are lack of learning personalized features of sample; 2) the outcomes of hash retrieval are related to both the length and encoding method of hash codes. Because a sample possess more personalized features than label semantics, in this paper, we propose a novel supervised cross-modal hashing collaboration learning method called discrete Cross-modal Hashing with Relaxation and Label Semantic Guidance (CHRLSG). First, we introduce two relaxation variables as latent spaces. One is used to extract text features and label semantic information collaboratively, and the other is used to extract image features and label semantics collaboratively. Second, the more accurate hash codes are generated from latent spaces, since CHRLSG learns collaboratively feature semantics and label semantics by using labels as the domination and features as the auxiliary. Third, we utilize labels to strengthen the similar relationship of inter-modal samples via keeping the pairwise closeness. Label semantics are made full use of to avoid classification error. Fourth, we introduce class weight to further increase the discrimination of samples that belong to different classes in intra-modal and keep the similarity of samples unchanged. Therefore, CHRLSG model preserves not only the relationship between samples, but also maintains the consistency of label semantic during collaboration optimization. Experimental results of three common benchmark datasets demonstrate that the proposed model is superior to the existing advanced methods. [ABSTRACT FROM AUTHOR]

In semiconductor manufacturing, with the shrinking down of wafer circuit widths, a strict quality control is required for wafer fabrication processes, resulting in that after a wafer being processed and removed from a chamber, a cleaning operation that takes significant time is performed for eliminating the chemical residual. Such a cleaning operation makes a traditional backward strategy for single-arm cluster tools inefficient. By the existing studies, it is shown that the productivity can be improved if some numbers of chambers at a step are kept empty. With this idea, an extended backward strategy is proposed by deciding the optimal number of empty chambers. Based on such a strategy, this paper studies the challenging problem for scheduling a single-arm cluster tool with both chamber cleaning operations and wafer residency time constraint for the first time. By building a timed Petri net model for the system, two linear programs are proposed to determine the minimal cycle time and test the existence of a feasible schedule and find it if existing. At last, two industrial examples are used to demonstrate the obtained results. [ABSTRACT FROM AUTHOR]

A robotic cluster tool involves many activities whose time is subject to some disturbance, thus leading to the activity time variation. It results in wafer sojourn time fluctuation in a process module, which may in turn violate wafer residency time constraints. Some wafer fabrication requires a revisiting process. With wafer revisiting, the effect of activity time variation on wafer sojourn time fluctuation is so complicated that no analysis was reported to the best knowledge of the authors. It is vitally important to accurately analyze it. To do so, this paper adopts a Petri net model to describe the dynamical behavior of cluster tools. With this model, a real-time control policy is proposed to offset the effect of the activity time variation on wafer sojourn time fluctuation as much as possible. Then, the wafer sojourn time delay is analyzed and algorithms are developed to calculate its exact upper bound. With the proposed method, one can check if a given schedule is feasible under bounded activity time variation. Some practical examples are given to show the application of the proposed approach. [ABSTRACT FROM PUBLISHER]

A hybrid multirobot cluster tool is composed of both single and dual-arm robotic cluster tools. Since the behavior of different individual tools is different, it is very challenging to coordinate their activities in such a tool and to schedule it optimally. To find a one-wafer cyclic schedule to reach the shortest cycle time for a treelike hybrid multirobot cluster tool whose bottleneck tool is process-bound, this paper extends resource-oriented Petri nets to model it such that a schedule can be parameterized by its robots’ waiting time. Based on the model, this paper then establishes the conditions under which there is a one-wafer cyclic schedule such that the shortest cycle time can be obtained. An efficient algorithm is also given to test the existence of such a schedule and to find it if existing. At last, examples are used to illustrate the proposed approaches. [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]

Scheduling a multicluster tool with wafer residency time constraints is highly challenging yet important in ensuring high productivity of wafer fabrication. This paper presents a method to find an optimal one-wafer cyclic schedule for it. A Petri net is developed to model the dynamic behavior of the tool. By this model, a schedule of the system is analytically expressed as a function of robots’ waiting time. Based on this model, this paper presents the necessary and sufficient conditions under which a feasible one-wafer cyclic schedule exists. Then, it gives efficient algorithms to find such a schedule that is optimal. These algorithms require determining the robots’ waiting time via simple calculation and thus are efficient. Examples are given to show the application and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In semiconductor manufacturing, there are wafer fabrication processes with wafer revisiting. Some of them must meet wafer residency time constraints. Taking atomic layer deposition (ALD) as a typical wafer revisiting process, this paper studies the challenging scheduling problem of single-arm cluster tools for the ALD process with wafer residency time constraints. It is found that there are only several scheduling strategies that are applicable to this problem and one needs to apply each of them to decide whether a feasible schedule can be found or not. This work, for each applicable strategy, performs the schedulability analysis and derives the schedulability conditions for such tools for the first time. It proposes scheduling algorithms to obtain an optimal schedule efficiently if such conditions are met. It finally gives illustrative examples to show the application of the proposed concepts and approach. [ABSTRACT FROM PUBLISHER]

With the interaction of discrete-event and continuous processes, it is challenging to schedule crude oil operations in a refinery. This paper studies the optimization problem of finding a detailed schedule to realize a given refining schedule. This is a multiobjective optimization problem with a combinatorial nature. Since the original problem cannot be directly solved by using heuristics and meta-heuristics, the problem is transformed into an assignment problem of charging tanks and distillers. Based on such a transformation, by analyzing the properties of the problem, this paper develops a chromosome that can describe a feasible schedule such that meta-heuristics can be applied. Then, it innovatively adopts an improved nondominated sorting genetic algorithm to solve the problem for the first time. An industrial case study is used to test the proposed solution method. The results show that the method makes a significant performance improvement and is applicable to real-life refinery scheduling problems. [ABSTRACT FROM PUBLISHER]

This work studies the scheduling problem of a single-arm multicluster tool with a linear topology and process-bound bottleneck individual tool. The objective is to find a one-wafer cyclic schedule such that the lower bound of cycle time is reached by optimally configuring spaces in buffering modules that link individual cluster tools. A Petri net (PN) model is developed to describe the dynamic behavior of the system by extending resource-oriented PNs such that a schedule can be parameterized by robots’ waiting time. Based on this model, conditions are presented under which a one-wafer cyclic schedule with the lower bound of cycle time can be found. With the derived conditions, an algorithm is developed to find such a schedule and optimally configure buffer spaces. The algorithm requires only simple calculation to set the robots’ waiting time and buffer size. Illustrative examples are presented to demonstrate the proposed method. [ABSTRACT FROM PUBLISHER]

As a type of process plant, a refinery is characterized by the interaction of discrete events and continuous processes. To schedule crude oil operations in a refinery, it is necessary to define and schedule the jobs simultaneously such that heuristics and meta-heuristics cannot be directly applied. It is very challenging to schedule crude oil operations. To solve this problem, it is decomposed into two subproblems hierarchically. At the upper level, a refining schedule is found, while at the lower level a detailed schedule is obtained to realize the refining schedule. Given a refining schedule at the upper level, this paper studies the detailed scheduling problem at the lower level. Based on a control-theoretic perspective, the problem is innovatively transformed to a problem of assigning charging tanks to distillers such that meta-heuristic methods can be applied. Then, a genetic algorithm (GA) approach is developed to solve the problem. In realizing the proposed GA, based on a set of existence conditions of a feasible schedule, methods are presented to guarantee that each chromosome corresponds to a feasible schedule. An industrial case study is used to show the application of the proposed method. It shows that the method works well and is applicable to real-life problems. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [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]

With a combinatorial nature and hybrid characteristics, it is very challenging to schedule crude oil operations in a refinery and, up to now, there is no software tool for practical use in this field. To solve this problem, based on the two-level architecture in our previous work, for cases where crude oil mixing is required, this paper investigates the refining scheduling problem at the upper level. Based on the schedulability conditions, a three-stage approach is proposed. First, a linear programming model is developed to maximize productivity. Second, another linear programming model is built and solved to optimally obtain the mixed crude oil types and their amounts, and assign them into the distillers. Finally, this paper simply sequences and merges the oil parcels obtained at the second stage to form a desired schedule. Because the proposed approach mainly solves two linear programming problems, it is very efficient. An industrial case study is presented to illustrate the proposed approach. [ABSTRACT FROM PUBLISHER]

Some wafer fabrication processes require a wafer to visit some processing modules in a cluster tool multiple times, leading to a wafer revisiting process. They may pose wafer residency time constraints, i.e., a wafer can stay in a module for a limited time after it is processed. Although techniques exist for scheduling cluster tools with either wafer residency time constraints or wafer revisiting, it is much more challenging to schedule tools with both of them. Considering that atomic layer deposition is a typical wafer revisiting process, this paper intends to schedule a dual-arm cluster tool dealing with it. Based on the analysis of such a tool’s properties, a novel scheduling strategy called modified 1-wafer cyclic scheduling is derived. With this strategy, necessary and sufficient schedulability conditions are presented. If schedulable, highly efficient scheduling algorithms are developed to obtain a feasible and optimal schedule together with a way to implement the obtained one. Illustrative examples are given to show the application of the proposed approach. [ABSTRACT FROM PUBLISHER]

This paper studies the challenging problem of scheduling single-arm multi-cluster tools with wafer residency time constraints. They have a linear topology and their bottleneck tool is process-bound. This work aims to find an optimal one-wafer cyclic schedule. With the Petri net model developed in our previous work and the minimal cycle time for a multicluster tool without wafer residency time constraints, it derives the necessary and sufficient schedulability conditions for multicluster tools with wafer residency time constraints for the first time. Then, it gives an algorithm to find an optimal one-wafer cyclic schedule if schedulable. This is done by simply setting the robots’ waiting time for each tool. Thus, it is very computationally efficient and applicable to practical problems. An example is presented to illustrate the proposed method. [ABSTRACT FROM PUBLISHER]

A multi-cluster tool is composed of a number of individual cluster tools linked by buffering modules (BMs). The capacity of a BM can be one or two. Aiming at finding an optimal one-wafer cyclic schedule, this paper explores the effect of two-space BMs on the performance of a multi-cluster tool. A Petri net (PN) model is developed to model it by extending resource-oriented PNs. The dynamic behavior of robot waiting and tasks, process modules, and buffers is well described by the model. This paper shows that there is always a one-wafer cyclic schedule that reaches the lower bound of the cycle time of a process-bound tool. Furthermore, a closed-form algorithm is revealed to find such a schedule for the first time for such multi-cluster tools. Illustrative examples are given to show the application and power of the proposed method. [ABSTRACT FROM AUTHOR]

Due to the different behavior of single-arm and dual-arm cluster tools, it is challenging to schedule a hybrid multi-cluster tool containing both of them. This paper aims to find an optimal one-wafer cyclic schedule for such a multi-cluster tool. It is assumed that the bottleneck individual cluster tool in it is process-bound, thereby making it process-dominant. To do so, this paper models a hybrid multi-cluster tool with Petri nets. With this model, it derives the conditions under which individual cluster tools can operate in a paced way. Based on these conditions, this paper shows that for any process-dominant hybrid multi-cluster tool there is always a one-wafer cyclic schedule. Then, it develops the algorithms to find the minimal cycle time and the optimal one-wafer cyclic schedule. It is computationally efficient and easy-to-implement in practice. Examples are given to show the application and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Cyclic scheduling and operation of a residency time-constrained single-arm cluster tool with failure-prone process modules are highly challenging. In some cases, when a failure occurs, there still exists a feasible cyclic schedule for the performance-degraded tool. In other cases, such a schedule no longer exists. For the latter, it is highly desired to respond to a process module failure properly such that the tool can continue working and the wafers in the tool can be completed in a feasible way. This work is the first one to study this important issue. The idea is to apply Petri nets to describe the dynamic behavior of a single-arm cluster tool. With the developed Petri net model, this paper formulates failure response policies to control the cluster tool such that it can keep working without violating any residency time constraint. The failure response policies are implemented via efficient real-time control laws. Illustrative examples are presented to show their usage. [ABSTRACT FROM AUTHOR]

In a multi-cluster tool, there may be both single and dual-arm cluster tools. Such a multi-cluster tool is called hybrid multi-cluster tool. To operate such a multi-cluster tool, one needs to coordinate different types of robots for accessing the shared buffering modules. Aiming at finding a one-wafer periodic schedule such that the lower bound of cycle time can be reached, this paper conducts a study on scheduling a hybrid multi-cluster tool with its bottleneck tool being process-bound. The tool is modeled by a kind of timed Petri net model. With this model, the scheduling problem is reduced to determining the robots' waiting time. Then, the conditions under which a one-wafer periodic schedule exists such that the lower bound of cycle time can be reached are presented. Based on them, a closed-form algorithm is given to check whether such a one-wafer periodic schedule exists. If so, it is found via simple calculation. Examples are given to show the application of the proposed method. [ABSTRACT FROM PUBLISHER]

In operating a multi-cluster tool, it needs to coordinate the activities of multiple robots. Thus, it is very challenging to schedule it. This paper conducts a study on one-wafer cyclic scheduling for multi-cluster tools whose bottleneck cluster tool is process-bound. The system is modeled by a Petri net. With this model, conditions under which a one-wafer cyclic schedule exists are developed. Based on them, it is shown that, for any multi-cluster tool whose bottleneck cluster tool is process-bound, there is always a one-wafer cyclic schedule. Then, a method is presented to find the minimal cycle time and the optimal one-wafer cyclic schedule. It is computationally efficient. Illustrative examples are used to show the applications and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

For some wafer fabrication processes, such as an atomic layer deposition (ALD) process, the wafers need to visit some process modules for a number of times. Using the existing swap-based strategy scheduling method in such systems leads to a 3-wafer cyclic schedule. Unfortunately, it is not optimal in the sense of cycle time. Aiming at searching for a better schedule, this paper models the system by a timed Petri net. With this model, the properties of the 3-wafer schedule are analyzed. Then, based on the analysis, it is found that, to improve the performance, it is necessary to reduce the number of wafers completed in a cycle. Hence, a 1-wafer schedule is developed by using a new swap-based strategy. By using the Petri net model, its cycle time is analyzed and shown to be optimal. Also, an effective method is presented to implement it. Illustrative examples are given to verify the research results obtained in this paper. [ABSTRACT FROM PUBLISHER]

Short-term scheduling for crude oil operations is a combinatorial problem and involves extreme detail. Thus, it is very complicated and, up to now, there is no efficient technique and software tool for it. To search for efficient techniques, a two-layer hierarchical solution is proposed for it. At the upper level, one finds a realizable refining schedule to optimize some objectives. At the lower level, a detailed schedule is obtained to realize it. A methodology has been presented to solve the lower level problem from a control perspective by the authors of this paper. In this paper, the upper level problem for finding optimal refining schedules is addressed, and a novel method is proposed based on the results obtained at the lower level. This method solves a linear programming problem to determine the maximal production rate and a transportation problem to optimally assign crude oil types and volume to the distillers. This way, the method is computationally very efficient. An industrial case study is presented to show the application of the proposed method. [ABSTRACT FROM PUBLISHER]

With wafer residency time constraints, it is crucial to schedule a cluster tool in semiconductor fabrication such that the wafer sojourn time in a processing module is in a given range. However, because of the activity time variation in wafer fabrication by cluster tools, a feasible schedule obtained under the assumption of deterministic activity times may become infeasible. To solve this problem, it is critically important to reveal the wafer sojourn time fluctuations with bounded activity time variation. This paper targets at single-arm cluster tools. They are modeled by a Petri net to describe the fabrication processes. Based on the net, a real-time control policy is proposed such that its use offsets the effect of the activity time variation as much as possible. Then, the wafer sojourn time delay is analyzed and analytical expressions are given to calculate the upper bound. With the proposed method, we can check if a given schedule is feasible under bounded activity time variation. Examples are given to show the applications of the research results. [ABSTRACT FROM PUBLISHER]

Due to the proliferation of contemporary computer-integrated systems and communication networks, there is more concern than ever regarding privacy, given the potential for sensitive data exploitation. A recent cyber-security research trend is to focus on security principles and develop the foundations for designing safety-critical systems. In this work, we investigated the problem of verifying current-state opacity in discrete event systems using labeled Petri nets. A system is current-state opaque provided that the current-state estimate cannot be revealed as a subset of secret states. We introduced a new sub-model of the system, named an observer net. The observer net have the same structure as the plant, but it is distinguished by the use of colored markers as well as simultaneous and recursive transition enabling and firing, which offer an efficient state estimation. We considered two settings of the proposed approach: an on-line setting, in which a current-state opacity algorithm is proposed. The algorithm waits for the occurrence of an observable event and determines if the current observation of a plant reveals the secret behaviour, as well as, an off-line setting, where the verification problem is solved based on a state estimator called a colored estimator. In this context, necessary and sufficient conditions for verifying opacity are developed with illustrative examples to demonstrate the presented approach. [ABSTRACT FROM AUTHOR]

Given a discrete event system, it is said to be opaque if an intruder who can partially observe system behavior cannot infer that the system is necessarily in a predefined secret. A discrete event system is current-state opaque if an intruder is never able to verify whether the current state of the system is within the secret states. This article addresses the verification of current-state opacity of a discrete event system modeled with Petri nets, where the secret is defined as a set of states. The existing methods cannot deal with large-sized systems due to the curse of dimensionality. Multi-valued decision diagrams (MDDs) are widely accepted as an efficient and compact data structure for representing the extremely large reachability sets of Petri nets. We show that MDDs can be used in Petri nets with partial observable transitions and propose an approach for the verification of current-state opacity for bounded Petri nets. It is shown by experimental studies that the symbolic approach is practically more efficient than traditional techniques such as basis reachability graphs (BRGs). [ABSTRACT FROM AUTHOR]

Deadlocks are a rather undesirable situation in a highly automated flexible manufacturing system. Their occurrences often deteriorate the utilization of resources and may lead to catastrophic results in safety-critical systems. Graph theory, automata, and Petri nets are three important mathematical tools to handle deadlock problems in resource allocation systems. Particularly, Petri nets are considered as a popular formalism because of their inherent characteristics. They received much attention over the past decades to deal with deadlock problems, leading to a variety of deadlock-control policies. This study surveys the state-of-the-art deadlock-control strategies for automated manufacturing systems by reviewing the principles and techniques that are involved in preventing, avoiding, and detecting deadlocks. The focus is deadlock prevention due to its large and continuing stream of efforts. A control strategy is evaluated in terms of computational complexity, behavioral permissiveness, and structural complexity of its deadlock-free supervisor. This study provides readers with a conglomeration of the updated results in this area and facilitates engineers in finding a suitable approach for their industrial scenarios. Future research directions are finally discussed. [ABSTRACT FROM PUBLISHER]

With wafer residency time constraint of cluster tools in semiconductor manufacturing, activity time variation can make an originally feasible schedule infeasible. Thus, it is difficult to schedule them and schedulability is a vitally important issue. With bounded activity time variation considered, this paper addresses their real-time scheduling issues and conducts their schedulability analysis. A Petri net (PN) model and a control policy are presented. Based on them, this paper derives closed-form schedulability conditions. If schedulable, an algorithm is developed to obtain an offline periodic schedule. This schedule together with the control policy forms a real-time schedule. It is optimal in terms of cycle time and can be analytically computed, which represents significant advance in this area. [ABSTRACT FROM PUBLISHER]

For short-term scheduling of crude oil operations in refinery, often crude oil residency time constraint and charging-tank-switch-overlap constraint are ignored by mathematical programming models to make the problem solvable. Thus, a schedule obtained by such mathematical programming models is infeasible and cannot be deployed. To solve this problem, this work studies the short-term scheduling problem of crude oil operations in a control theory perspective. The system is modeled by a hybrid Petri net and a short-term schedule is seen as a series of control commands. With this model, schedulability analysis is carried out and schedulability conditions are presented. These conditions can be used as constraints for finding a realizable and optimal refining schedule. Moreover, based on the proposed approach, a detailed schedule can be easily obtained given a realizable refining schedule. In this way, the complexity for the short-term scheduling problem of crude oil operations in refinery is greatly reduced and effective techniques and tools for practical applications can be obtained. [ABSTRACT FROM AUTHOR]

For some wafer fabrication processes in cluster tools, e.g., atomic layer deposition (ALD), wafer revisiting is required. Typically, in such processes, wafers need to visit two consecutive processing steps several times. Such a revisiting process can be denoted as (mi, mi + 1)^h, where i means the ith-step and mi and mi + 1 mean the corresponding quantity of the processing modules in i and (i+1)th steps, and h the number of visiting times. This paper conducts a study for scheduling single-arm cluster tools with such a wafer revisiting process. The system is modeled by Petri nets (PNs) to guarantee the feasibility of robot activities. Based on the model, a deadlock avoidance policy is presented. With the control policy, cycle time analysis for the revisiting process is made. With the fact that wafer processing times are much longer than robot movement times in cluster tools, it is shown that, when mi = mi + 1 = 1, i.e., each step has only one processing module, the optimal one-wafer cyclic schedule is deterministic and unique, and the minimal cycle time can be calculated by an analytical expression. It is also shown that, when mi = 1 and mi + 1 = 2 or mi= 2 and mi + 1 = 1, the optimal one-wafer cyclic schedule can be obtained by finding h deterministic schedules and the one with the least cycle time. A novel analytical method is finally presented to schedule the overall system containing such reentrant wafer flow. This represents a significant advance in single-arm cluster equipment automation. [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]

This paper addresses the problem of real-time deadlock-free scheduling for a semiconductor track system. The system is required to process wafers continuously, cassette by cassette. The process is not necessarily a repeated one. In addition, the system is deadlock-prone and its modules are failure-prone. Thus, real-time scheduling approaches are required to achieve high-performance. The problem can be solved in a hierarchical way. A deadlock avoidance policy is developed for the system as a lower-layer controller. With the support of the deadlock avoidance policy, heuristic rules are proposed to schedule the system in real-time. An effective modeling tool, colored–timed resource-oriented Petri net, is presented. It is shown that with this model we can schedule a system to achieve satisfactory results in real-time. This method is tolerant to module failures. [ABSTRACT FROM AUTHOR]

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]

A kind of facilities for wafer fabrication, cluster tools (CTs) need to close down to an idle state from time to time because of periodical maintenance and switches from one type of lots to another, which is called a normal close-down process (NCDP). It is crucial to optimize such a transient process since it tends to occur more and more frequently due to customization. Also, process modules (PMs) in CTs are known to be failure-prone. Once a PM failure occurs, a tool needs to close down to an idle state as well, which is different from NCDP and is called a failure close-down process (FCDP). With wafer residency time constraints (WRTCs) being imposed, close-down process optimization for such a tool is challenging, since one needs to not only finish this process as soon as possible but also meet WRTCs during this transient process. In order to tackle this problem, this article first introduces steady state scheduling problems. Then, with a presented backward robot task sequence, a linear programming model is first proposed to optimize NCDP. To deal with the PM failures, efficient PM failure response policies are formulated for the cases in which a PM fails. Then, four linear programs are proposed to optimize an FCDP. Finally, industrial case studies are given to show the usefulness of the proposed approaches. [ABSTRACT FROM AUTHOR]

In semiconductor manufacturing, a process module (PM) failure in cluster tools (CTs) happens from time to time. To effectively operate a CT, such a failure should be handled in a proper and timely manner. This issue becomes much more complicated because wafer residency time constraints (WRTCs) must be met to ensure the quality for some wafer fabrication processes. With such constraints, if a tool is operated under a periodic schedule and a PM fails, it is desired that the tool can still operate under a periodic schedule if it is possible. Nevertheless, the periodic schedule after a PM failure must be different from that before its failure since in this case the tool is degraded. Thus, there must be a transient process between them. It is a great challenge to operate a tool such that it can go through such a transient process with WRTCs being always satisfied. This paper aims to solve this problem by proposing PM failure response policies which can successfully transfer a CT to the feasible schedule after failure from the one before a failure. Then, efficient algorithms are developed to improve these response policies. The proposed policies are composed of simple control laws such that they can be realized in real time and online. Illustrative examples are presented to show their applications. [ABSTRACT FROM AUTHOR]

Dealloying is a powerful technology to fabricate nanoporous materials with tunable structures and compositions for battery applications. Meanwhile, electrochemical dealloying is an intrinsic process for metal anodes that exhibits fundamental correlations with electrode morphologies and structures. In this work, Li‐Ag composites are fabricated as a case study to understand the spontaneous structural evolution and the in situ formation of nanoporosity during a reversible lithiation/delithiation process. The rationally designed nanoporous AgLi (NPAgLi) framework with limited Li capacity (10 mAh cm−2) enables a dendrite‐free anode with marginal volume variation upon long‐term cycling, which can be attributed to the spatially confined reaction pattern along with efficient Li alloying/dealloying. Furthermore, full cell tests reveal the NPAgLi anode remains stable under practical conditions such as lean electrolyte (15 µL), large areal capacity (1.6 mAh cm−2), and high‐loading cathode (12 mg cm−2). This work provides new perspectives on the in situ structural evolution of Li‐rich alloy electrodes and the results are expected to contribute to the development of alkali metal anodes. [ABSTRACT FROM AUTHOR]

This study focuses on the scheduling problem of heterogeneous unmanned surface vehicles (USVs) with obstacle avoidance pretreatment. The goal is to minimize the overall maximum completion time of USVs. First, we develop a mathematical model for the problem. Second, with obstacles, an A* algorithm is employed to generate a path between two points where tasks need to be performed. Third, three meta-heuristics, i.e., simulated annealing (SA), genetic algorithm (GA), and harmony search (HS), are employed and improved to solve the problems. Based on problem-specific knowledge, nine local search operators are designed to improve the performance of the proposed algorithms. In each iteration, three Q-learning strategies are used to select high-quality local search operators. We aim to improve the performance of meta-heuristics by using Q-learning-based local search operators. Finally, 13 instances with different scales are adopted to validate the effectiveness of the proposed strategies. We compare with the classical meta-heuristics and the existing meta-heuristics. The proposed meta-heuristics with Q-learning are overall better than the compared ones. The results and comparisons show that HS with the second Q-learning, HS + QL2, exhibits the strongest competitiveness (the smallest mean rank value 1.00) among 15 algorithms. [ABSTRACT FROM AUTHOR]

The exploration of advanced electrode materials with appropriate structures and compositions is the primary task for nonaqueous potassium ion batteries (PIBs). Herein, 3D nanoporous CuBi anodes are fabricated through a facile chemical dealloying process. The ligaments of this nanoporous alloy are composed of interconnected Bi serving as the active material and Cu decoration as a highly conductive matrix. Such a typical bicontinuous ligament‐channel structure is beneficial to overcome the slow K‐ion diffusion kinetics as well as electrode expansion on cycling, as evidenced by the high discharge capacity and good rate capability in PIBs. Potassium storage mechanisms in the Bi anodes are investigated through combined techniques to identify the multiple step postassiation process and the charge/discharge products at each stage. Furthermore, it is the first time to report the irreversible phase transition of KBi2→Bi upon charge, which seems associated with the ligament pulverization process. These results provide critical insights into the capacity degradation mechanisms of Bi anodes in PIBs. Also, dealloying is proved to be an efficient technology for the rational design of metal/alloy materials for high performance PIBs. [ABSTRACT FROM AUTHOR]

Unsupervised Domain adaptation (UDA) aims to transfer knowledge from the labeled source domain to the unlabeled target domain. Recently, to achieve reliable knowledge learning, progressively labeling (PL) is proposed to select reliable target samples for training. Although PL achieves fruitful results, there are two problems that limit its performance: (a) PL may neglect to filter out the uncertain samples that lie in the classification boundaries, which might select low-quality target samples for training, and lead to error accumulations; and (b) PL might overlook the consistent selection in the sample selection stage during iteration, which might result in unstable sample selection. To cope with these problems, we propose a novel method called Selected Confidence Sample Labeling (SCSL). SCSL consists of three parts: Discriminative Progressively Labeling (DPL), Consistency Strategy (CS), and Differential Learning (DL). First, DPL selects the high-confidence target samples with the maximum probability difference between the highest and the second-highest probabilities of classification. In this way, the uncertain samples are filtered out, while the qualities of the selected target samples are ensured. Second, CS includes a group of consistency strategies to make the selected high-confidence target samples near to the class-centroids. This further improves the confidence degree of the selected target samples, and ensures that the proposed model does not remove or replace the selected target samples during iteration. At last, DL is a bi-strategic training approach that applies CS and top- k fuzzy probability clustering to train the high-confidence and the remaining target samples, respectively. In doing so, all the target samples are trained simultaneously, and the generalization of the model is improved. Extensive experiments on four benchmark datasets with the comparison of several advanced algorithms demonstrate the superiority of SCSL. [ABSTRACT FROM AUTHOR]

This paper deals with the identification problem of faulty behavior in a discrete event system, assuming that the fault-free model of a system is given in terms of Petri nets, where the set of transitions is divided into two disjoint subsets: 1) observable and 2) unobservable ones. The observed system output is defined as a transition-marking sequence, i.e., each transition is followed by a marking. First, a nonlinear integer programming model that characterizes the faults modeled by fault transitions is built according to the abnormal behavior extracted from the observed sequence. Then, it is converted into an integer linear programming (ILP) problem and a faulty net that preserves the structure of the fault-free one is obtained by solving this ILP model. In addition, an algorithm is developed to ensure acyclicity of the resulting unobservable subnet whose transition set is composed of the unobservable transitions of the fault-free net and the identified fault transitions. [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]