The integration of advanced technologies, such as Artificial Intelligence (AI), into manufacturing processes is attracting significant attention, paving the way for the development of intelligent systems that enhance efficiency and automation. This paper uses the term ”Gentelligent system” to refer to systems that incorporate inherent component information (akin to genes in bioinformatics—where manufacturing operations are likened to chromosomes in this study) and automated mechanisms. By implementing reliable fault detection methods, manufacturers can achieve several benefits, including improved product quality, increased yield, and reduced production costs. To support these objectives, we propose a hybrid framework with a dominance-based multi-objective evolutionary algorithm. This mechanism enables simultaneous optimization of feature selection and classification performance by exploring Pareto-optimal solutions in a single run. This solution helps monitor various manufacturing operations, addressing a range of conflicting objectives that need to be minimized together. Manufacturers can leverage such predictive methods and better adapt to emerging trends. To strengthen the validation of our model, we incorporate two real-world datasets from different industrial domains. The results on both datasets demonstrate the generalizability and effectiveness of our approach.

High-dimensional and incomplete (HDI) data commonly arise in many complex application scenarios, such as bioinformatics and recommender systems. Deep neural networks (DNNs) exhibit cutting-edge performance in representing HDI data due to their formidable capacity for nonlinear learning. However, previous research primarily concentrates on single-metric-focused models utilizing fixed and exclusive$L_{2}$-norm-based strategies for both loss and regularize terms. Such strategies limit the model’s ability to effectively learn from diverse and heterogeneous HDI data. Recognizing this limitation, this article presents the multimetric autoencoder (MMA) with the following twofold ideas: 1) utilizing multiple$L_{p}$-norms to create four distinct autoencoders, each defining a unique metric representation space with diverse regularize and loss characteristics; 2) integrating these four diverse autoencoders by using a customized, self-adjusting weighting strategy. This innovative approach enhances the model’s capacity to handle heterogeneous and inclusive HDI data effectively, addressing the limitations of previous studies. The theoretical analysis supports the effectiveness of the MMA in harnessing the benefits of diverse multimetric spaces. In the experiments, the MMA is evaluated on six real HDI datasets. The experimental results reveal that the MMA outperforms seven contemporary models in effectively representing HDI data.

The increasing demand for efficient last-mile delivery in smart logistics underscores the role of autonomous robots in enhancing operational efficiency and reducing costs. Traditional navigation methods, which depend on highprecision maps, are resource-intensive, while learning-based approaches often struggle with generalization in real-world scenarios. To address these challenges, this work proposes the Openstreetmap-enhanced oPen-air sEmantic Navigation (OPEN) system that combines foundation models with classic algorithms for scalable outdoor navigation. The system uses off-the-shelf OpenStreetMap (OSM) for flexible map representation, thereby eliminating the need for extensive pre-mapping efforts. It also employs Large Language Models (LLMs) to comprehend delivery instructions and Vision-Language Models (VLMs) for global localization, map updates, and house number recognition. To compensate the limitations of existing benchmarks that are inadequate for assessing last-mile delivery, this work introduces a new benchmark specifically designed for outdoor navigation in residential areas, reflecting the real-world challenges faced by autonomous delivery systems. Extensive experiments in simulated and real-world environments demonstrate the proposed system's efficacy in enhancing navigation efficiency and reliability. To facilitate further research, our code and benchmark are publicly available11https://ei-nav.github.io/OpenBench/.

Semiconductor manufacturing widely employs cluster tools that comprise three critical components: a vacuum module (VM), a loadlock module (LLM), and an equipment front-end module (EFEM). In operating such tools, LLM plays a pivotal role. Additionally, wafer fabrication in VM faces strict wafer residency time constraints, making the coordination among the modules particularly challenging. This paper addresses the cyclic scheduling problem of single-arm cluster tools with EFEM and wafer residency time constraints. It focuses on the tool efficiency in module cooperation and schedulability. We explore cooperative strategies for the robot operating within VM to access loadlocks, elucidating the influence of EFEM and LLM on VM. Based on these strategies, we establish necessary and sufficient conditions for the existence of a feasible periodic schedule, providing a foundational basis for the systematic analysis. For schedulable scenarios, we derive an efficient algorithm to identify the feasible and optimal schedule in terms of tool cycle time. The performance and efficiency of the proposed algorithm are validated through experimental verification. Note to Practitioners—In wafer fabs, single-arm cluster tools with EFEM are widely adopted. Studies that neglect the impact of EFEM on scheduling cluster tools are often inapplicable to real-world scenarios. Wafer residency time constraints are essential for maintaining wafer quality. As VM and EFEM operate in different pressure environments, the cooperation via LLM makes it challenging to meet residency time constraints. To address this problem, this paper analyzes the impact of cooperative strategies on the tool performance, and the influence of EFEM and LLM on VM as well. Under these strategies, we examine the schedulability conditions, helping engineers understand how processing parameters affect scheduling feasibility. If a feasible schedule exists, an efficient algorithm is derived to find an optimal schedule. Experimental results show the efficiency of the proposed algorithm, making it suitable for embedding into cluster tool controllers for efficient implementation. For cases that are not schedulable, with the results obtained, this work can provide guidance of how to redesign the process such that feasibility can be ensured.

The growing demand for efficient last-mile delivery highlights the need for autonomous robots to improve operational efficiency and reduce costs. Traditional navigation methods rely on high-precision maps, which are expensive to produce and maintain, while learning-based approaches often struggle to adapt to diverse real-world environments. To address these challenges, this paper presents OpenStreetMap-enhanced oPen-air sEmantic Navigation (OPEN), a system that combines foundation models with classic navigation algorithms to enable scalable outdoor navigation. By leveraging off-the-shelf OpenStreetMap (OSM), OPEN eliminates the need for extensive pre-mapping and provides a lightweight, readily available map representation. The system uses Large Language Models (LLMs) to interpret delivery instructions and Vision Language Models (VLMs) for global localization without relying on GPS, real-time map updates, and entrance recognition, ensuring robust navigation in complex environments. To further enhance adaptability, OPEN incorporates a local replanning method that dynamically adjusts waypoints in response to environmental changes and OSM inaccuracies. Since existing benchmarks do not adequately reflect the challenges of last-mile delivery, this work introduces a new benchmark designed for residential navigation. Experiments conducted in both simulated and real-world settings demonstrate that OPEN improves navigation accuracy, efficiency, and reliability, outperforming existing semantic navigation methods. To facilitate further research, the code and benchmark are publicly available.

This work focuses on the scheduling of a photolithography area with multiple machine groups and each one consists of a predetermined number of photolithography machines (PMs). PMs belonging to the same machine group should have identical processing capacities. Additionally, all PMs are designated with downward processing compatibility. This means that the wafers requiring relatively low pattern precision can be processed by the PMs used to deal with high pattern precision. After executing a photolithography process, a circuit pattern is transferred from an auxiliary resource called a reticle onto the wafer surface. Moreover, when processing wafers with different reticle and processing environment requirements, the machine setup is necessary. With those complex processing requirements, the objective is to minimize the difference between the longest and shortest working time of PMs so as to balance the workloads among all PMs. To do so, a mixed-integer linear programming model is built and then solved by using CPLEX for the small-sized problem. For medium-and large-sized problems, a designed estimation of distribution algorithm integrating a Kmeans clustering is constructed to improve the productivity of the photolithography area. Comparison results show that the proposed method outperforms the compared algorithms regardless of problem sizes.

In role-based collaboration (RBC) methodology, eliminating agent conflicts during the role assignment process is crucial for establishing a sustainable cooperative system. However, when agent resources are scarce, assignment strategies aimed at eliminating agent conflicts become infeasible. Consequently, there is a need to select the optimal assignment with a minimal number of agent conflicts, which is essentially a nonlinear bilevel optimization problem. To tackle this issue, we first design the group role assignment with minimized agent conflicts (GRAMAC) model to formalize this problem. It converts this problem into an extended integer linear programming (x-ILP) one and finds the optimal solution. Then, we prove that solving the GRAMAC model is an$\mathscr {NP} - \mathrm {complete}$task. Moreover, we identify the sufficient and necessary condition under which the GRAMAC model has the optimal conflict-free solution. Finally, extensive experiments demonstrate that, compared to existing strategies, our proposed method reduces the number of agent conflicts by an average of approximately 30% while ensuring the group performance of the collaborative system.

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.

Cluster tools are the key equipment in semiconductor manufacturing . For high-end chip manufacturing processes, to ensure the wafer quality , periodical cleaning operations for process chambers in such tools are required for eliminating the residual gas and chemicals. In operating a tool, the wafer processing time in a process chamber may vary within a range. It is meaningful to predict the cycle time of cluster tools with chamber cleaning operations and processing time variation. By doing so, automation material handling system can be told when a lot is completed by a cluster tool such that it can assign an overhead hoist transporter to transport this lot at the right time. This plays an important role in improving the productivity of a whole semiconductor manufacturing system . This work conducts the cycle time analysis of cluster tools with chamber cleaning operations and processing time variation. Specifically, it proposes a novel method to calculate the average cycle time of a single-arm cluster tool under which an optimal schedule can be obtained. Then, for a dual-arm cluster tool, an efficient algorithm is developed to approximate the average system cycle time under which an optimal schedule can be obtained. Experiments show that the gap between the average system cycle time obtained by simulations and the average system cycle time obtained by the proposed method is no more than 0.1% which demonstrate the effectiveness of the proposed method.

Quasi group role assignment (QGRA) presents a novel social computing model designed to address the burgeoning domain of self-service spatiotemporal crowdsourcing (SSC), specifically for tackling the photographing to make money problem (PMMP). Nevertheless, the application of QGRA in practical scenarios encounters a significant bottleneck. QGRA provides optimal assignment strategies under conditions where both the number of crowdsourced tasks and workers remain stable. However, real-world crowdsourcing applications may necessitate the phased integration of new tasks. With the rapid increase in the number of tasks, a set of residual tasks inevitably exists that are difficult to complete. To maximize the completion of crowdsourced tasks, workers may be assigned low-yield or even unprofitable tasks. Given the reluctance of crowdsourcing workers to be overstretched for these tasks, along with the inherent characteristics of self-service crowdsourcing tasks, this can lead to the failure of the assignment scheme. To tackle the identified challenges, this article proposes the QGRA with agent satisfaction (QGRAAS) method. Initially, it sheds light on a creative satisfaction filtering algorithm (SFA), which is engineered to perform optimal task assignments while actively optimizing the profitability of crowdsourcing workers. This approach ensures the satisfaction of workers, thereby fostering their loyalty to the platform. Concurrently, in response to the phased changes in the crowdsourcing environment, this article incorporates the concept of bonus incentives. This aids decision-makers in achieving a tradeoff between the operational costs and task completion rates. The robustness and practicality of the proposed solutions are confirmed through simulation experiments.

Role negotiation (RN) is situated at the initial stage of the role-based collaboration (RBC) methodology and is independent of the subsequent agent evaluation and role assignment (RA) processes. RN is to determine the roles and the resource requirements for each role. In existing RBC-related research, RN is assumed to be static. This means that the roles and the resource requirements for each role are predetermined by decision-makers. However, the resources allocated to each role can vary. At this time, iterative RN outcomes will have different RA results. There may not be a direct dominant relationship between different RA outcomes, especially when solving group role assignment (GRA) with multiple objectives (GRA++) problems, which makes it even more complex. To address these concerns, we introduce the original bilevel GRA++ (BGRA++) model. Specifically, at the lower level of BGRA++, a strategy is designed for quantifying iterative RNs. For the upper level, we introduce the novel GRA-NSGA-II algorithm for the RA process. Finally, we introduce the concept of decision tolerance to assist decision-makers in selecting the optimal solution from the multiple RNs. Last, simulation experiments are conducted to verify the robustness and practicability of the proposed method. Comparisons and discussions show that the proposed solution is highly competitive for solving the GRA++ problem with iterative RN.

A Dynamically Weighted Directed Network (DWDN) fundamentally illustrates the complex interactions among massive nodes from a big-data-oriented application, like the dynamic interactions among numerous terminals in a metropolitan network management system (MNMS). A High-Dimensional and Incomplete (HDI) tensor is able to flexibly quantize it, where lots of entries are missing primarily due to the impossibility in discovering the full interactions among numerous nodes. Such an HDI tensor can be effectively represented by a Latent Factorization of Tensors (LFT) model for extracting useful knowledge like potential links from it, while existing LFT models commonly adopt general regularization schemes without considering an HDI tensor's imbalanced known data, which impairs their generality. To address this issue, this paper develops an Fine-grained Regularized Nonnegative Latent factorization of tensors (FRNL) model based on two-fold ideas: a) innovatively proposing an Swish-p-based and fine-grained regularization scheme where the regularization effects acting on individual latent feature is proportional to its related instance count for precisely representing the imbalanced distribution of an HDI tensor's known data; b) implementing the self-adaptation of the model hyper-parameters via a fuzzy controller to achieve high practicability. The convergence ability of FRNL is justified theoretically. Experimental studies on eight DWDNs emerging from a real MNMS illustrate that compared with state-of-the-art LFT models, the proposed FRNL model obtains significantly higher learning accuracy and computational efficiency in representing a DWDN.

In semiconductor manufacturing, cluster tools tend to integrate a vacuum module (VM), a loadlock module (LLM), and an equipment front-end module (EFEM). While scheduling techniques exist for cluster tools without EFEM, the collaboration among these modules introduces additional challenges for tools with EFEM. In such tools, LLM acting as a shared module plays a crucial role in operating a cluster tool. Moreover, modern fabs have adopted the practice of chamber cleaning after processing each wafer to eliminate chemical residue that may remain within chambers. This article addresses a cyclic scheduling problem of a single-arm cluster tool with EFEM, while considering chamber cleaning requirements. We propose a conflict-free loadlock (LL) state transformation sequence of One-in and One-out LLs to describe the transformations resulted from LL operations. We then present cooperative strategies for robots to access LLM during the state transformation. Based on these strategies, we derive closed-form algorithms to find feasible and optimal schedules in terms of tool cycle time, allowing for an analysis of the best-performing strategy combinations. The effectiveness of the proposed algorithm is illustrated by experimental results.

Mass individualized prototyping (MIP) is a kind of advanced and high-value-added manufacturing service. In the MIP context, the service providers usually receive massive individualized prototyping orders, and they should keep a stable state in the presence of continuous significant stresses or disruptions to maximize profit. This article proposed a graph convolutional neural network-based deep reinforcement learning (GCNN-DRL) method to achieve the resilient production control of MIP (RPC-MIP). The proposed method combines the excellent feature extraction ability of graph convolutional neural networks with the autonomous decision-making ability of deep reinforcement learning. First, a three-dimensional disjunctive graph is defined to model the RPC-MIP, and two dimensionality-reduction rules are proposed to reduce the dimensionality of the disjunctive graph. By extracting the features of the reduced-dimensional disjunctive graph through a graph isomorphic network, the convergence of the model is improved. Second, a two-stage control decision strategy is proposed in the DRL process to avoid poor solution quality in the large-scale searching space of the RPC-MIP. As a result, the high generalization capability and efficiency of the proposed GCNN-DRL method are obtained, which is verified by experiments. It could withstand system performance in the presence of continuous significant stresses of workpiece replenishment and also make fast rearrangement of dispatching decisions to achieve rapid recovery after disruptions happen in different production scenarios and system scales, thereby improving the system’s resilience.

Nowadays, biochemical and biomedical laboratories, and pharmaceutical industries widely adopt high throughput screening (HTS) systems for the development of new drugs and detection of new viruses. Different from a flow shop in manufacturing systems, in operating an HTS system, microplates are often required to be processed multiple times by some devices, resulting in complex microplate flows. Such flows make the system deadlock-prone and very difficult to deal with. Also, consistency is essential for its operation, requiring a one-microplate cyclic schedule for its operation. Generally, the scheduling problem of such systems is NP-hard due to their combinatorial nature. Thus, scheduling an HTS system is challenging. Antibody detection is an important complement to the nucleic acid test for response to the coronavirus disease pandemic and is adopted in many laboratories, which can be implemented by the enzyme-linked immunoassay (ELISA) via an HTS system. This article focuses on the problem of scheduling an HTS for ELISA and explores the possibility of a polynomial algorithm for finding an optimal one-microplate cyclic schedule. To do so, we model the system via a type of Petri nets and develop an optimal deadlock avoidance policy. As a result, the optimal activity sequence can be simply obtained. With the obtained sequence, the scheduling problem is converted to a continuous optimization problem that can be solved by determining the robot waiting time at the devices. Then, a one-microplate cyclic schedule can be efficiently found by solving a small-sized linear programming (LP) problem. Moreover, it is shown that, for many cases, one can quickly get an optimal solution by simply setting the robot waiting time without solving an LP problem. Consequently, we successfully present a polynomial algorithm to get an optimal cyclic schedule and show that the addressed problem is polynomial-time solvable. A practical example is used to show its applications.

In semiconductor manufacturing, multicluster tools are widely employed for many wafer fabrication processes. With the demand for high-mix integrated circuit chips and shrinkage of circuit width, a scheme in which multiple wafer types are fabricated inside multicluster tools is adopted by wafer foundries to make more profits. Multiple wafer types, multiple robots, and wafer residency time constraints make the resulting scheduling problems challenging. This work focuses on scheduling a single-arm multicluster tool to process two wafer types concurrently subject to wafer residency time constraints in which a conventional one-wafer cycle and backward strategy are not efficient. With such properties, several necessary and sufficient conditions are presented to check the feasibility of a periodic schedule. Polynomial-time-complex algorithms are proposed to examine a tool's schedulability and coordinate multiple robots to handle wafers for schedulable scenarios. The cycle time of an obtained schedule can reach the lower bound. A practical example is used to show the effectiveness of the proposed algorithm.

With severe global competitions, how to control inventory so as to reduce the material storage space plays a pivotal role in reducing the daily cost resulted from the land rental. The goal of this article is to develop a periodic material delivering schedule to minimize the space required for inventory. We take a leading home appliance manufacturing system in China as a case problem to study the material delivery scheduling optimization problem so as to ensure the synchronization between production and material delivering. The problem can be divided into a number of subproblems with each of them served by a turnover vehicle (TV) and this article investigates one of its subproblems. We build a mixed-integer linear programming (MILP) model for the problem and use the CPLEX optimizer to solve small-size problems. With the combinatorial nature, we design a Grey Wolf Optimizer (GWO) algorithm to solve large-size problems. Extensive experiments are done, and comparisons are made with existing metaheuristics to validate the proposed algorithm. Results demonstrate the efficiency and effectiveness of the proposed method.

In the team allocation problem (TAP), eliminating team disparities aims at keeping an equilibrium of the resource or ability among teams for equality. For this concern, existing literature merely utilized the golden mean principle to eliminate team disparities from a static perspective. Few of them reasonably investigate the pros and cons of this principle from a computational perspective. Moreover, maintaining equilibrium is a dynamic process and requires dynamic adjustment, especially after considering team members’ self-efforts and adaptivity. With respect to the environments—classes, agents, roles, groups, and objects (E-CARGO) model and its role-based collaboration (RBC) methodology, this article formalizes and solves the TAP, i.e., revised group role assignment (GRA) problem, from both the individual and team’s perspective. Based on the revised GRA, this article provides novel insight into the effectiveness of dynamically maintaining equilibrium, which may help decision-makers be proactive in building more sustainable teams. Relevant large-scale simulation experiments are conducted in this article to verify the proposed method. This article reveals a social paradox: even though considering all about the team members’ self-efforts and adaptivity, equilibrium still seems inequitable. Conversely, pursuing equilibrium may bring the Matthew effect.

The World Bank estimates that the number of refugees worldwide will reach 140 million by 2050 due to global warming and local wars. Considering the rapid increase in the number of refugees, an efficient and feasible assignment method is required for refugee resettlement. This article formalizes the refugee resettlement issue using the Environments-Classes, Agents, Roles, Groups, and Objects (E-CARGO) model. A novel solution is designed for Refugee reSettling (RS) by extending the Group MultiRole Assignment (GMRA), which applies the agent stability evaluation method as a feedback mechanism while optimally resettling refugees. With this proposed solution, decision-makers can swiftly resettle refugees from multiple suffering countries while appropriately ensuring host countries’ benefit. Finally, large-scale simulation experiments based on the Python PuLP platform are carried out to demonstrate the practicability and robustness of the proposed solution. The simulation results provide a solid decision-making reference for the leaders of the world.

Group role assignment with cooperation and conflict factors (GRACCFs) is a creative social computing method for team establishment. It can maximize the new team’s performance through role assignment considering potential cooperation or conflict factors among agents. However, this method has two bottlenecks in practical applications. First, in the scenario of establishing a new team from several existing teams, collecting the pertinent cooperation or conflict information encounters challenges. Second, GRACCF merely takes the CCFs as a part of the objective function for team performance, but this will underestimate the CCFs’ impacts on the sustainable development of the team. This article tackles these issues by extending GRACCF from a new viewpoint. It first designs a KD45 logic algorithm based on the KD45 logic system, which can discover the implicit cognitive CCFs through logical inferences with closure calculations. Then, it proposes an original team evaluation method that can help decision-makers determine the weights of team performance and CCFs’ impacts based on their demands. Large-scale simulation experiments indicate that the proposed solution is practicable and robust. The proposed method provides a solid decision-making reference for administrators when establishing a sustainable team.

Nowadays, cluster tools are extensively used for many wafer manufacturing processes, such as coating, lithograph, developing, etching, deposition, and testing. Traditional process modules in cluster tools can execute a single operation only. With the rapid development of equipment design, multifunctional process modules (MPMs) are equipped to serve for processing multiple operations together just like a single operation. With different wafer processing parameters, MPMs may be set for processing multiple operations together or processing just a single operation to form different schedules so as to maximize the productivity. Thus, it is highly desired to find an efficient scheduling method to quickly adapt to wafer processing parameter changes for productivity maximization by taking the advantages of MPMs. To tackle this issue, a deadlock-free Petri net (PN) model is developed to describe the behavior of a single-arm cluster tool. Based on the evolving mechanism of the PN model, two algorithms are developed to calculate the makespan for completing a given number of wafers. Then, an adaptive scheduling method is presented to set the functions of MPMs to minimize the makespan. Finally, experimental results show the efficiency and effectiveness of the proposed method.

In today’s semiconductor manufacturing industry, wafer foundries often face the challenge of producing a variety of integrated circuit chip products using a single manufacturing line. To address this, multicluster tools have become a popular choice for processing multiple wafer types simultaneously. Operating such tools involves coordinating the robots in adjacent individual tools to transport multitype wafers through a shared buffer. This study aims to develop a scheduling method for the concurrent fabrication processes of two wafer types, performed by a multicluster tool with wafer residency time constraints. The proposed approach presents a two-backward sequence, based on a backward strategy of a single wafer type, to convert a one-wafer cyclic schedule into a one-wafer-per-type cyclic schedule while revealing its temporal properties. To ensure a smooth operation of a single-arm multicluster tool system and synchronize multiple robots, several necessary and sufficient conditions are derived for the first time. Two efficient algorithms are then proposed to determine the feasibility of a periodic schedule and obtain a schedule that achieves the lower-bound cycle time under a two-backward strategy, maximizing the productivity of such a multicluster tool. Finally, numerical simulations and two practical examples are presented to demonstrate the applications and performance of the proposed approach.

Spatiotemporal analysis ranges from simple univariate descriptive statistics to more complex multivariate analyses. Such an analysis can be used to explore spatial and temporal patterns in different domains, i.e., spatial and temporal information of subscribers in Internet of Things networks. Most spatial and temporal analysis techniques are based on conventional quantitative and traditional data mining approaches, such as the$k$-means algorithm. Clustering approaches based on artificial neural networks can be more efficient since they can reveal nonlinear patterns. Hence, in this work, we tailor an AI-based spatiotemporal unsupervised model such that the underlying pattern structure of a mobile phone network can be revealed, relative similarity among interactions extracted, and the associated patterns analyzed. The proposed approach is based on an optimized self-organizing feature map. It deals with high-dimensionality concerns and preserves inherent data structures. By identifying the spatial and temporal associations, decision makers can explore dominant interactions that can be used for resource optimization in network planning, content distribution, and urban planning.

Cluster tools play a significant role in the entire process of wafer fabrication. As the width of circuits in semiconductor chips shrinks down to less than 10nm, strict operational constraints are imposed on the operations of cluster tools in order to ensure the quality of processed wafers. Particularly, wafer residency time constraints and chamber cleaning requirements are commonly seen in etching, chemical vapor deposition, coating processes, etc. They make the scheduling problem of cluster tools more challenging. This work aims to provide a solution for dual-arm cluster tools with wafer residency time constraints and chamber cleaning requirements. To do so, it proposes a novel virtual wafer-based scheduling method. By this method, under a steady state, a PM processes either a real or virtual wafer at a time. When a PM processes a virtual one, its chamber can perform a cleaning operation. In this way, we can meet not only the strict residency time constraints for real wafers, but also innovatively meet chamber cleaning requirements. Based on such a novel scheduling method, an efficient binary integer programming model is established to optimize the throughput of cluster tools. Finally, experiments are performed to show the efficiency and effectiveness of the proposed method.Note to Practitioners—To ensure wafer quality in semiconductor manufacturing, engineers have to impose wafer residency time constraints and chamber cleaning requirements on the operations of cluster tools. In order to tackle their scheduling problem with these constraints, this work proposes a novel method based on the use of virtual wafers. Under a one-cyclic schedule obtained for time-constrained cluster tools without chamber cleaning requirements, virtual wafers are loaded into the tool such that when a PM processes a virtual wafer, a chamber cleaning operation can be performed in practice. The key to solve this scheduling problem is to find a wafer loading sequence with the highest performance in terms of cycle time. To do so, this work establishes an efficient binary integer programming model to search for such a solution. Since the obtained solution is a periodical wafer loading sequence based on a one-wafer cyclic schedule, it can be easily implemented. Therefore, this work has a high practical value to numerous semiconductor manufacturers.

Self-service spatiotemporal crowdsourcing (SSC), a booming variant of spatiotemporal crowdsourcing (SC), emerges because of the vigorous development of the mobile Internet. Unlike the conventional SCs, the particularity of self-service in SSC may lead to unfinished tasks at the end of the entire assignment process, making a one-time assignment scheme ineffective. SSC is essentially an adaptive collaboration (AC) problem that requires a dynamic assignment strategy for a higher task completion rate. This article tackles this issue by establishing a quasi group role assignment (QGRA) based on a typical SSC scenario, that is, the photographing to make money problem (PMMP). First, it sheds light on a novel role awareness method, which can effectively divide tasks to accelerate the solution while, to some extent, raising the task completion rate. Second, it specifies an agent satisfaction evaluation (ASE) method to quantify the relationship between task completion rate and workers’ satisfaction. This method aims at considerably ameliorating task completion rate. Last, it extends QGRA with a new AC algorithm, which can achieve AC of the workers while accomplishing the crowdsourcing task. Moreover, utilizing the ASE method can help decision-makers balance the task completion rate and the workers’ satisfaction. Large-scale simulation experiments based on the real crowdsourced datasets exemplify the robustness and practicability of the proposed solutions. This article contributes a new version of the group role assignment (GRA) model, that is, quasi GRA (QGRA), a creative formalization to solve the AC problem.

For route choice during an outside emergency evacuation, evacuees make different route choice decisions due to their preference diversity. Furthermore, uncertainties of human behavior are inevitable, leading to a high complexity of macroscopic traffic flows and great impact on the evacuation efficiency. In this work, a simulation-based evacuation traffic planning is presented for homogenizing the network traffic flows. In the simulation, the evacuees are divided into two types: panicky and rational ones, and their route choice behavior is captured by two distinct models, respectively. The proportion of panicky evacuees is dynamically changed based on the traffic information level (TIL) and traffic states of downstream links at a decision point. Type-II fuzzy logic system (T2FLS) is used to model the uncertainties of evacuees’ subjective perception on route costs. Comprehensive numerical experiments are done to analyze the impact of TIL, behavior diversity, and the heterogeneity of macroscopic traffic flows on evacuation efficiency. Results show that the proposed evacuation traffic planning is beneficial for improving the evacuation efficiency, alleviating the proportion of panicky evacuees, and homogenizing traffic flows in a large-scale network.

The recent extension of a macroscopic fundamental diagram (MFD) into a bi-modal MFD (or 3D-MFD) provides the relationship among the total network circulating flows and the accumulations of private vehicles and public buses. 3D-MFD reveals the contribution of large occupancy vehicles such as buses in improving urban transportation efficiency. A lot of bi-modal traffic management techniques are introduced based on 3D-MFD to improve the urban traffic efficiency without using detailed origin-destination (OD) information. However, similar to MFD, 3D-MFD is also highly affected by the heterogeneity of a road network. In order to form 3D-MFDs with low scatter to be utilized for further bi-modal traffic management, this paper proposes a partition method to cluster road links into several homogeneous regions for a bi-modal urban network. It is comprised of three layers named as initial partition, merging, and boundary adjusting. At the initial partition layer, Seeded Region Growing (SRG) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are integrated to obtain a number of subregions. A modified Genetic Algorithm (GA) is developed to merge the subregions into larger regions at the merging layer. Then, boundary adjusting is performed by changing the region to which a boundary is clustered to optimize the result. Multi-sensor data collected from Shenzhen in China are utilized to verify the effectiveness of the proposed partition method.

Transportation services play an increasingly significant role for people’s daily lives and bring a lot of benefits to individuals and economic development. The randomness and volatility of traffic flows, however, constrains the effective provision of transportation services to a certain extent. Precise traffic flow forecasting becomes the key and primary task to realize the stability of intelligent transport systems and ensure efficient scheduling of traffic. This paper investigates the application of an ensemble approach based on deep belief networks for short-term traffic flow forecasting. Traffic flow data, collected from the real world, is decomposed into several Intrinsic Mode Functions (IMFs) and a residue with EEMD (Ensemble Empirical Mode Decomposition). Then, for each component, the essential feature subset is extracted by the mRMR (minimum Redundancy Maximum Relevance Feature Selection) method considering weather conditions and day properties. Furthermore, each component is trained by DBN (Deep belief networks) and their forecasting results are summed up as the output of the ensemble model at last. Results indicate that the proposed approach achieves significant performance improvement over the single DBN and other selected methods.

When earthquakes occur, solar-powered unmanned aerial vehicles (SUAVs), deployed as communication relay points, can construct a signal relay network to assist the ground mobile communication vehicles in resuming communication. Considering the urgency of disaster relief, a practical, accurate, and robust modeling method for multiple SUAVs deployments is vital. For this concern, this article first formalizes the deployment problem of multiple solar-powered UAVs in communication recovery by extending Group MultiRole Assignment (GMRA) (UGRA). In the second step, the success in this assignment process depends on the choice of the agent evaluation method. The evaluation benchmark in UGRA is SUAV path planning in a complex environment with uncertain subpaths and accumulative attitude errors. In response to this issue, we propose two innovative algorithms: 1) dynamic curve path-planning algorithm (DCPPA) and 2) greedy curved straight path-planning algorithm (GCSPPA). Moreover, with the time requirement in mind, one sufficient condition and one necessary condition are established to help the DCPPA achieve fast convergence. With these two novel agent evaluation algorithms, UGRA can rapidly deploy multiple SUAVs to establish a collaborative relay network within an acceptable time. Finally, simulation experiments at different scales are carried out to demonstrate the accuracy and effectiveness of the proposed solution.

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.

Nowadays, wafer fabrication in semiconductor manufacturing is highly dependent on cluster tools. A cluster tool is equipped with several process modules (PMs) and a wafer handling robot. When the tool is operating, generally each PM is processing a wafer, and the robot is responsible for delivering the wafers from one PM to another. Thus, when a wafer is completed in a PM, the robot may be busy for performing other tasks such that it cannot immediately unload the completed wafer in the PM, resulting in that the wafer has to stay there for some extra time. The processing time of a wafer together with its delay time for waiting for the robot’s arrival for unloading is defined as wafer residency time in a PM. However, a long wafer delay time may deteriorate its quality. Therefore, it is highly desired and important to reduce the wafer delay time at each step as much as possible. This work aims to tackle this important issue for single-arm cluster tools (SACTs). Specifically, by using a Petri net model, this work analyzes the steady-state operational behavior of an SACT under the backward and earliest starting strategies. It is found that there must exist wafer delay time at the steps in the upstream of the bottleneck step, and such wafer delay time can be reduced by properly adjusting the robot waiting time. Thus, three algorithms are developed to reduce the wafer delay time at each step as much as possible by properly assigning the robot idle time. Finally, the application of the proposed method is illustrated by using examples.Note to Practitioners—In a modern semiconductor fab, there are hundreds of cluster tools for wafer fabrication. To ensure wafer quality, it is important to reduce the wafer delay time in PMs of cluster tools after a wafer is processed since the high temperature, chemical gas, and particles in the PMs may damage the wafer. To do so, this work proposes three algorithms with polynomial complexity to assign the robot idle time as robot waiting time such that the wafer delay time in PMs can be reduced as much as possible. Furthermore, the obtained schedule by these algorithms is optimal in terms of the cycle time. Besides, the developed algorithms can be easily embedded into the controller of cluster tools by facility engineers. Therefore, this work has a practical value.

Last-mile logistics is a crucial phase of online commodity trades. In last-mile logistics, one of the critical problems is to reasonably assign couriers to distribute the products in time in order to ensure the quality of service, especially for fresh produce. The last-mile assignment problem (LMAP) for fresh produce poses a challenge on traditional logistics since fresh produce is difficult to preserve. This article formalizes the LMAP for fresh produce via the group role assignment framework and proposes a role awareness method by using adaptive clustering in spatiotemporal crowdsourcing based on task granularity. The formalization of LMAP makes it easy to find a solution using the IBM ILOG CPLEX optimization package (CPLEX). The proposed method allows one to take the time and space factor into consideration, helps spatiotemporal crowdsourcing assign couriers for efficient delivering daily orders, and improves the quality of service in last-mile logistics. It is verified by simulation experiments. The experimental results demonstrate the practicability of the proposed solutions in this article.

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.

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.

In wafer manufacturing, extensive research on the operations of cluster tools under the steady state has been reported. However, with the shrinking down of wafer lot size, such tools are frequently required to switch from handling one lot of wafers to another, resulting in more transient processes, including start-up and close-down ones. Also, wafer residency time constraint is critical for many wafer fabrication processes. To cope with the transient scheduling problem of time-constrained single-arm cluster tools with parallel chambers, based on a generalized backward strategy, this paper first builds timed Petri net models for these two transient processes. Then, two linear programs are derived for the first time to search a feasible schedule with a minimal makespan. Two industrial examples are given to demonstrate the effectiveness of the obtained results at last.

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 singlerobot-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 singlerobot-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.

As a kind of robotic cells, cluster tools are widely used for semiconductor wafer fabrication processes since they provide a reconfigurable and efficient environment. With recent advances in new semiconductor materials, the circuit line width has continuously being shrunk down, which brings new challenges for manufacturers. They require that a wafer should be moved away from a processing chamber as soon as possible after its processing is finished. To ensure high-quality integrated circuits in a wafer, its post-processing residency time must be minimized. It is also highly desirable to maximize the throughput of robotic cluster tools. This article aims at scheduling such tools with multiple objectives subject to wafer residency time constraints. To do so, new algorithms are proposed to calculate robot waiting time delicately upon the analysis of particular events of robot waiting for dual-blade robotic tools and optimally schedule such tools. The numerical results of industrial examples show that the proposed algorithms can provide an effective method to find schedules for dual-blade cluster tools such that multiple objectives are optimized.

A public room in buildings could hold a number of persons who may prefer dissimilar thermal environment. Different areas in such rooms may have different temperatures. Facility layout in such a room has effect on the distribution of the people in the room, thus it may affect its thermal environment. 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. This work aims at optimally operating an ACC system and designing the facility layout so as to maximize the total thermal satisfaction rate (TSR) as well as energy saving. To do so, with a large library room at Macau University of Science and Technology (MUST) as a case scenario, an investigation with a large number of experiments is 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. Then, we propose a particle swarm optimization (PSO) algorithm such that an optimal or near-optimal solution can be efficiently found. By numerical results, it is observed that the proposed method can significantly improve the total TSR and save energy.

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.

Cluster tools are automated robotic manufacturing systems containing multiple computer-controlled process modules. They have been increasingly used for wafer fabrication. This paper reviews the modeling and scheduling methods for cluster tools with both nonrevisiting and revisiting processes. For nonrevisiting processes, we focus on the modeling and scheduling problems of cluster tools with different constraints. Then, their solution methods are reviewed and compared. For revisiting processes, this paper first discusses the scheduling problem of some general manufacturing systems with revisiting. Then, the modeling and scheduling methodologies used to solve the scheduling problems of cluster tools with revisiting processes are reviewed. Future research directions and conclusions are finally discussed. Note to Practitioners-Semiconductor manufacturing systems are among the most advanced and complicated manufacturing systems. Their key equipment is highly automated robot-based cluster tools. With wafer residency time constraints, wafer revisiting, activity time variation, chamber cleaning requirements, and failure-prone process modules (PMs), it is very challenging to schedule and control them. This paper surveys their modeling and scheduling methods. Scheduling them requires one to schedule their robot tasks and processing activities simultaneously. Owing to wafer residency time constraints and the lack of buffers among PMs, it is difficult to conduct their optimal scheduling. This paper presents a thorough review of the state-ofthe-art research results about modeling and optimal scheduling of clusters tools and indicates the future research directions.

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.

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.

In wafer fabrication, a robotic cluster tool is required to be closed down in order for engineers to perform its on-demand and preventive maintenance and switch between different wafer lots. They often deal with a close-down process subject to wafer residency time constraints, i.e., a wafer must exit from a processing chamber before its quality degradation within a certain time limit. To obtain higher yield, it is very important to optimize a close-down process for a cluster tool. Yet the existing literature pays no or little attention to this issue. By focusing on a time-constrained single-arm cluster tool, this paper intends: 1) to build its Petri net model to analyze its schedulability and 2) to develop computationally efficient algorithms to find an optimal and feasible schedule for its closing-down process under different workloads at its steps. Industrial examples are used to illustrate the application of the proposed method.

Semiconductor manufacturing industry has adopted multi-cluster tools as wafer fabrication equipment that is extraordinarily pricey but highly attractive owing to their higher productivity than single cluster tools can achieve. A challenging issue is how to schedule these tools. It is especially difficult to schedule their frequently occurring close-down processes subject to wafer residency constraints. Such processes appear frequently as caused by wafer lot switches and preventive and emergency maintenances. They are dynamical and non-cyclic. We analyze the synchronization conditions for multiple robots to perform concurrent activities. Upon these conditions, for the situations that an optimal schedule can be found in the steady state, a linear program model is proposed to find a feasible and optimal schedule for close-down processes. An example shows the application and efficiency of our proposed method.

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.

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.

High-mix and low-volume wafer fabrication leads to more and more lot switches in cluster tools. Practitioners must thus deal with more transient processes during such switches, including start-up and close-down. To obtain higher throughput, it is critical to shorten these processes. Much effort has been put into the steady state modeling and scheduling of cluster tools and some for start up processes. However, no attention is paid to a close-down process for single-arm cluster tools with wafer residency constraints. This work aims to do so by 1) developing a Petri net model to analyze their properties and 2) proposing Petri net-based methods to solve their close-down optimal scheduling problems under different workloads among their process steps. An industrial example is given to illustrate their application.

In semiconductor manufacturing, wafer residency time constraints make the scheduling problem of cluster tools complicated. A process module (PM) in cluster tools is prone to failure. It is crucial to deal with any such failure in a proper and timely manner. If there are feasible periodic schedules in operating a cluster tool before and after a PM failure, it is desired to make it operate continuously when such a failure occurs. However, due to wafer residency time constraints, it is highly challenging to control a tool such that it can be correctly transferred from a feasible schedule before failure to another after it. To solve this problem, a Petri net model is developed to describe the dynamic behavior of a single-arm cluster tool and failure response policies are proposed. The proposed policies are formulated via simple control laws for their easy implementation. Examples are given to show them. Note to Practitioners-For single-arm cluster tools with wafer residency constraints, this work proposes the response policies when a PM fails in wafer fabrication. With a Petri net model, when there are feasible cyclic schedules for both before and after failure, policies are presented to respond to a PM failure such that the wafers in a tool can be completed in a feasible way. The policies require polynomially complex calculation and can be implemented on-line to satisfy the real-time requirements. Therefore, they are applicable to practical semiconductor manufacturing systems.

This paper addresses the scheduling problem of single-arm cluster tools with both wafer residency time constraints and activity time variation in semiconductor manufacturing. Based on a Petri net model developed in our previous work, polynomial algorithms are proposed to obtain the exact upper bound of the wafer sojourn time delay for the first time. With the obtained results, one can check the feasibility of a given schedule or find a feasible and optimal one if it exists. Illustrative examples are given to show the applications of the proposed method.

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.

A multi-cluster tool with both single and dual-arm individual cluster tools is called a hybrid multi-cluster tool. To operate it, 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 work studies its scheduling when its bottleneck individual tool is process-bound. With a timed Petri net model, the scheduling problem is reduced to determine the waiting time of robots. Then, the conditions are presented, under which a one-wafer periodic schedule exists such that the lower bound of cycle time can be reached. Based on them, an efficient algorithm is given to check whether such a one-wafer periodic schedule exists. If so, it is found via simple calculation. An example is given to show its effectiveness.

The trends of increasing wafer diameter and smaller lot sizes from 25 wafers to a few wafers have led to more transient periods in wafer fabrication, thereby requiring more research on the optimal execution of transient processes. For some wafer fabrication processes, such as atomic layer deposition (ALD), wafers need to visit some process modules for a number of times, instead of once, thus leading to a so-called revisiting process. Research on transient processes of dual-arm cluster tools with wafer revisit processes becomes urgently needed for high-performance wafer fabrication. A cluster tool has no buffer except its robot, which makes its scheduling difficult. Thus, in order to study a cluster tool with revisiting process, a simulation system is helpful. This work develops a simulation model and system for a dual-arm cluster tool with a wafer revisit by using eM-Plant as a simulation platform. The resultant simulation system can be used for analysis and optimization of transient processes. An illustrative example is given to show its applications.

It is very challenging to schedule a residency time-constrained single-arm cluster tool with failure-prone process modules. In some cases, when a failure occurs, the degraded tool is not schedulable. However, 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. Thus, in this paper, Petri net models are developed to describe the discrete-event behavior of a single-arm cluster tool. With the models, failure response policies are given to control the cluster tool such that it can keep working without violating any residency time constraints. They are implemented via efficient real-time control laws. Illustrative examples are presented to show their usage.

In its fabrication, a wafer must meet its residency time constraints, i.e., it must leave its process chamber within a certain time after its process is done. It may need to visit some processing steps for a number of times, called wafer revisiting. For the typical wafer revisiting process of atomic layer deposition (ALD), this paper studies the scheduling problem of dual-arm cluster tools with both residency time constraints and wafer revisiting. To do so, a Petri net model is developed for the system. Then, with the Petri net model and based on a one-wafer cyclic scheduling method previously developed by the authors of this paper, schedulability conditions and scheduling algorithms are derived. The schedulability can be checked by analytical expressions. If schedulable, an optimal one-wafer cyclic schedule can be found by simple calculation. Thus, the proposed approach is very efficient. In addition, with the Petri net model, a simple way is presented to implement the obtained schedule. Illustrative examples are given to show the application of the proposed approach.

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.

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.

It is very challenging to schedule a multi-cluster tool to maximize its throughput. This work studies its one-wafer optimal periodic schedule. It is found that the key to schedule it is to determine its robots waiting times. A resource-oriented Petri net model is developed for it such that the robot waiting times are well modeled. Based on the model, optimality conditions are derived and the scheduling problem is reduced to the determination of robot waiting times. By the derived conditions, an optimal one-wafer optimal periodic schedule for a multi-cluster tool can be obtained by scheduling its individual cluster tools one by one. Then, a highly efficient algorithm is proposed to compute it for an entire multi-cluster tool.

It is challenging to schedule time-constrained cluster tools subject to activity time variation. With the help of their Petri net model, a real-time control policy is used to offset the activity time variation. Based on it, the schedulability conditions and scheduling algorithms are presented for single-arm cluster tools. The schedulability conditions can be analytically checked. Algorithms are developed based on analytical expressions such that it is also computationally efficient. The schedule obtained by the scheduling algorithms together with a real-time control policy forms the real-time schedule. It is optimal in terms of cycle time.

It is very challenging to schedule cluster tools subject to wafer residency time constraints and activity time variation. This work develops a Petri net model to describe the system and proposes a two-level real-time scheduling architecture. At the lower level, a real-time control policy is used to offset the activity time variation as much as possible. At the upper level, a periodical off-line schedule is derived under the normal condition. This work presents the schedulability conditions and scheduling algorithms for an off-line schedule. The schedulability conditions can be analytically checked. If they are satisfied, an off-line schedule can be analytically found. The off-line schedule together with a real-time control policy forms the real-time schedule for the system. It is optimal in terms of cycle time minimization. Illustrative examples are given to show the application of the proposed approach.