Knowledge Graph Question Answering (KGQA) plays an important role in modern information service systems. However, due to the challenges in constructing Knowledge Graphs (KGs) and their rapid update pace, KGs often remain incomplete. Previous works on multi-hop KGQA on incomplete KGs tend to explore potential relations between topic entities and answers, so they are constrained by the limited set of entities in KGs. To address the issue, we propose a method that executes multi-hop KGQA over private-public KGs to expand the size of the available entity set while being privacy-preserving to the private KGs and the user query. To be specific, by embedding entities and relations in both private and public KGs and using the triple scoring function as a distance metric, we reduce multi-hop KGQA into the iterative information retrieval task and formulate the iterative inference module for this task. Extensive experiments demonstrate that our method achieves the absolute average accuracy increase of 15.4% on WebQuestionsSP and 24.4% on SimpleQuestions over state-of-the-art methods under various private-public settings while achieving no privacy leakage of user queries.

The widespread adoption of laser powder bed fusion (LPBF) additive manufacturing is hampered by process unreliability problems. Modeling the melt pool behavior in LPBF is crucial to develop process control methods. While data-driven models linking melt pool dynamics to specific process parameters have shown appreciable advancements, existing models often oversimplify these relationships as deterministic, failing to account for the inherent instability of LPBF processes. Such simplifications can lead to overconfident and unreliable predictions, potentially resulting in erroneous process decisions. To address this critical issue, we propose a probabilistic data-driven approach to melt pool modeling that incorporates process noise and uncertainty. Our framework formulates a problem that includes distribution approximation and uncertainty quantification. Specifically, the Gaussian distribution with higher order priors, aided with variational inference and importance sampling, is used to approximate the probability distribution of melt pool characteristics. The uncertainty inherent in both LPBF process data and the modeling approach itself are then decomposed and approximated by using Monte Carlo sampling. The melt pool model is improved further by using a novel grid-based representation for the neighborhood of a fusion point, and a neural network architecture designed for effective feature fusion. This approach not only refines the accuracy of the model but also quantifies the uncertainty of the predictions, thereby enabling more informed decision-making with reduced risk. Two potential applications, including LPBF process planning and anomaly detection, are discussed. The implementation of our model is available athttps://github.com/qihangGH/probabilistic_melt_pool_model. Note to Practitioners—Modeling the melt pool behavior in laser powder bed fusion (LPBF) processes is pivotal for enhancing its quality control. However, a problem is that most existing data-driven melt pool models learn melt pool behavior with a deterministic function, which predicts the same outputs if its inputs are the same. This deviates from the reality and neglects the uncertainty in LPBF processes. As a consequence, the quality control methods based on such melt pool models lack required reliability. In response to these challenges, this work proposes to model melt pool behavior by using probability distributions with deep learning techniques, which can quantify the uncertainty in both LPBF process data and data-driven models. Aided with an elegantly designed representation for the neighborhood of a fusion point as model input, and a neural network architecture that fuses multi-modal data, the proposed model achieves accurate melt pool size prediction results. More importantly, this work quantifies and decomposes the prediction uncertainty. By accounting for noise and parameter variations, the probabilistic modeling models developed herein offer a more robust foundation for LPBF quality control than the existing ones. They can be readily applied by practitioners to perform improved process planning, defect prognosis, and real-time anomaly detection tasks.

System testing mainly focuses on finding bugs before online deployment. However, the system’s complexity increases due to a surge in the number of users, which makes scheduling the test case generation time more difficult, impeding testing fidelity and automatic testing. In this paper, we propose a data-driven framework for scheduling the test cases and improving testing fidelity. The developed framework involves two stages: 1) mining the running event interval characteristics from logs obtained from the actual environment in the first stage, and 2) utilizing those characteristics to develop a model for the test case generation time schedule in the second stage. First, we select the running event interval, which is the interval between two adjacent running events processed by the system, as the feature for analysis. Then, we analyze the interval’s distribution characteristics, observing that it adheres to the power-law distribution from multiple aspects. Second, based on the distribution characteristics obtained, we develop a composite event chain with associated relationships (CECAR) model to schedule test case generation time. Finally, we conduct a series of experiments using logs obtained from eight regions. The results show that, compared to baseline methods, the CECAR model reduces average errors in the generated quantity, power exponent, and specific granularity intervals by approximately 1% to 9% across multiple layers, demonstrating the effectiveness of the proposed method. We also made our dataset and code publicly available athttps://github.com/yizhenli-xjtu/CECAR. Note to Practitioners—System testing is a promising way to eliminate bugs and improve system stability before online deployment. If the testing method does not align with the system’s running characteristics in the actual environment, the testing fidelity and efficiency may be greatly reduced. Scheduling the test case generation time is one of the fundamental ways to address this challenge. The CECAR model developed in this paper can reproduce temporal characteristics and incorporate the concurrent relationships between different events, in turn, achieving the goal of fidelity improvement. Furthermore, based on the CECAR model, we can simulate arbitrary scenarios by combining different types of devices, which can greatly improve the testing’s flexibility and practicality.

Manufacturing systems consume a tremendous amount of energy and contribute about a quarter of greenhouse gas emissions. To achieve the sustainable production, it is vital to reduce the total energy consumption and improve the energy efficiency of manufacturing systems, especially energy-intensive manufacturing systems. In this paper, the energy consumption optimization problem for a two-machine geometric line is investigated. Specifically, it is formulated as a nonlinear programming which minimizes the energy consumption of the system while maintaining a required production rate. For this nonlinear programming with complex constraints, two optimality equations are explored and their mathematical properties are analyzed. Based on these properties, an effective and computationally efficient algorithm is developed to solve the optimal solution of the energy consumption optimization problem. In addition, the sensitivity of the optimal solution with respect to system parameters is analyzed. Finally, several extensions of the problem with an alternative objective and more practical considerations, are addressed as well.Note to Practitioners—For energy-intensive manufacturing systems, reducing the energy consumption and improving the energy efficiency are of both economic and ecological significance. In the literature, almost all existing related researches assume that machines obey the Bernoulli reliability model, which is not applicable for modeling some production lines. In this paper, we extend the problem formulation and solution methodology for the two-machine line with Bernoulli reliability model to that with geometric model, which has wider applications in practical systems. However, the extension is non-trivial since it is much more complicated to solve the energy consumption optimization problem for geometric lines and more insights on the results are gained. Although the line studied is short, as a step stone, this research will be extended to long geometric lines and production lines with more practical, e.g., exponential and non-exponential, reliability models.

This article studies how to solve the energy management (EM) problem while balancing economy and security. Firstly, a defense scheme is developed to relieve the negative effects of denial-of-service (DoS) attacks. Secondly, a secure predefined-time distributed EM algorithm via the time-base generator (TBG) is devised which can be suitable for directed graphs, and sensitive information is decomposed into two dynamic variables for key information privacy preservation. Thirdly, a dynamic event-triggered (DET) mechanism is proposed to economize communication resources. By constructing the Lyapunov energy function, it is proven that the convergence time of the devised distributed algorithm can be predefined by users independent of initial conditions. Zeno behavior is proven to be avoided, which exhibits the effectiveness of the DET approach. At last, the simulations are given to show the validity and advancement of the designed secure distributed algorithm.Note to Practitioners—With the deep integration of the cyber layer and physical layer in microgrids, the microgrid system is facing the threat of attacks. This article considers a typical cyber attack, a DoS attack, and the privacy-preserving problem. For solving the EM problem considering cyber security, a DoS attack defense scheme and a novel state decomposition mechanism have been proposed. After a DoS attack occurs, the microgrid system has an urgent need for EM fast recovery, while frequent information exchange can lead to unnecessary communication bandwidth occupation. Hence, a predefined-time distributed optimization approach based on the DET method is devised to achieve fast EM after DoS attacks and relieve the communication burden. The article adopts the TBG function to design dynamic gain which allows practitioners to flexibly adjust the convergence speed of the EM algorithm in practical applications.

The initial alignment is a critical stage for a strapdown inertial navigation system (SINS) and global positioning system (GPS) integrated navigation system. Currently, two major factors degrade the performance of SINS/GPS in-motion initial alignment, i.e., outliers in GPS measurements and cumulative low-accuracy inertial measurement unit (IMU) bias errors. This article considers both factors and proposes GISEIA-EMM: a high-accuracy GPS-inertial state estimator for in-motion alignment based on extended magnitude matching (EMM) method. First, we use the full integral method and non-interpolation procedure to construct the vector observation, which reduces the number of outliers and improves the accuracy of outlier detection. Second, we use an error-state extended Kalman filter (ESEKF), based on an augmented state-space model where the reference vector is regarded as a state, to suppress cumulative IMU bias errors, which improves the alignment accuracy. Third, we propose an EMM method, with the non-drifted expected normalized magnitude error, to detect and eliminate outliers in GPS measurements, which makes the alignment process stable. Simulation and field test results demonstrate that GISEIA-EMM can effectively address the negative impact of the two factors.

Model watermarking secures ownership verification and copyright protection of deep neural networks. In the black-box scenario, watermarking schemes commonly rely on injecting triggers and requiring the model's training data to maintain its performance. However, such knowledge might be unavailable in commercial settings as model transactions or copyright transfers. To tackle this challenge, we propose a novel data-free black-box watermarking scheme. Our approach modifies data-free adversarial distillation to efficiently obtain a generator that produces samples serving as a substitute for the training data so the watermark can achieve high fidelity without referring to the training data.

Additive manufacturing (AM) can build up complex parts in a layer-by-layer manner, which is a kind of novel and flexible production technology. The special manufacturing capability of AM shows great application potential in various fields. However, an open-loop control method cannot guarantee the reliability and repeatability of an AM process. Defects often occur to deteriorate product quality and lead to material and time waste, which hinders the development of AM industry. In this regard, a lot of efforts have been made to make an AM process more controllable. This work proposes an AM control framework that divides the related studies into three feedback loops, including the in-situ monitoring of process defects, fault diagnosis of 3-D printers, and closed-loop control of an AM process. These three loops constitute the inspection and control of AM from the machine level to product level. Specifically, the measurement requirements for monitoring techniques, defect detection, fault diagnosis, and closed-loop control are summarized. The challenges and future trends in realizing a more reliable and repeatable AM process are discussed. Note to Practitioners—This survey is motivated by urgent need to solve product quality problems in additive manufacturing (AM) caused by open-loop control. Three feedback loops can be established to solve them. The first one is defect detection that inspects part quality during fabrication. The second one is the fault diagnosis of a 3-D printer that monitors the health and operation conditions of its actuators. The last one is closed-loop control that improves AM process reliability and repeatability by regulating process variables in real time. These three loops are all based on the feedback signals of in-situ monitoring systems. This paper reviews the related studies and provides guidance for establishing the monitoring systems, performing defect detection and fault diagnosis, and designing closed-loop control systems, which helps realize more reliable and repeatable AM.

This paper studies the edge computing system (ECS) in which caching the frequently reusable service (FRS) at the edge server (ES) is an effective way to reduce delays. The larger cache space available in the ES (we call it ES cache space) might buffer the larger scale of FRS, and subsequently decrease the delays while bringing higher cache storage costs. Meanwhile, the distribution of FRS is not always known in advance. Therefore, how much ES cache space should be supplied to make the optimal tradeoff between the delays and cache storage costs arises as an interesting issue in practice. To address this issue, this paper first formulates the problem of determining the amount of ES cache space supply as a constrained Markov decision process (CMDP), then adopts the Zipf’s distribution to estimate the probability distribution of FRS, and finally proposes an effective cache space control algorithm (CSCA) guiding the ES to determine the amount of ES cache space supply to minimize the cache storage costs while maintaining the delays at the acceptable level. Theoretical analysis, simulations and field experiments document and illustrate its performance. Note to Practitioners—This paper addresses the interesting trade-off between the delays and cache storage costs for the edge computing system that operates with limited cache storage budgets while must satisfy the required real-time performances. It helps to improve the operation efficiency of the systems with edge computing setting in the area of Internet of Things (IoT) or Cyber-Physical Systems (CPS) that employ the edge server to cache the frequently reusable services, arriving at the minimization of the accumulative cache storage costs while maintaining the accumulative delays at the acceptable level. Theoretical analysis, simulation and field experimental investigations jointly show that the solution proposed here outperforms existing solutions.

The robust unit commitment (UC) is of paramount importance for achieving reliable operations considering the uncertainty of renewable realizations. The typical affine decision rule method and the robust feasible region method may achieve uneconomic dispatches as the dispatch decisions just rely on the current-stage information. Through approximating the future cost-to-go functions, the dual dynamic programming based methods have been shown adaptive to the multistage robust optimization problems, while suffering from high computational complexity. Thus, we propose the robust approximate dynamic programming (RADP) method to promote the computational speed and the economic performance for large-scale robust UC problems. RADP initializes the candidate points for guaranteeing the feasibility of upper bounding the value functions, solves the alternating calculation based bilinear programming to obtain the worst cases, and combines the primal and dual updates for the two-phase robust UC decision-making problem to achieve fast convergence. The finite termination guarantee of the RADP method is verified by the analyses for the multistage robust optimization problems with achieving suboptimal solutions. Numerical tests on 118-bus and 2383-bus transmission systems have demonstrated that RADP can approach the suboptimal economic performance at significantly improved computational efficiency.Note to Practitioners—This paper was motivated by solving the large-scale robust UC problem embedded with the multistage economic dispatch with improved computational and economical performance. Compared to the existing methods, this work suggests a RADP-based approach, which is inspired by the robust dual dynamic programming (RDDP) scheme. The proposed RADP owns lower computational complexity when compared with RDDP. As the problem in this work is formulated in a general form, the proposed RADP can be applied to other robust optimization problems such as the inventory management problem with uncertain demands. To apply the method to large-scale decision-making problems, one needs to solve linear programming problems to obtain the finite upper/lower bounds first, and then initialize the upper-bound points based on the feasible region limits of the decision variables. Conduct the forward pass to generate the candidate points and the backward pass to refine the cost-to-go functions. If the application problems have discrete decision variables as in the two-phase UC, one can solve the nonanticipativity constrained problem to obtain discrete solutions before doing the RADP scheme. The analyses and numerical experiments suggest that this approach can achieve suboptimal solutions. In future research, we will address the accelerated multistage robust decision-making with achieving optimal solutions.

The normal distributions transform (NDT) is an effective paradigm for point set registration. This method was initially designed for pair-wise registration and suffers from the accumulated error problem when directly applied to multi-view registration. Under the framework of point-to-cluster correspondence, this paper proposes a novel multi-view registration method named 3D multi-view registration based on the normal distributions transform (3DMNDT), which integrates the k-means clustering and Lie algebra optimizer to achieve multi-view registration. More specifically, the multi-view registration is cast into the maximum likelihood estimation problem. Firstly, k-means clustering is utilized to divide all data points into different clusters, where one normal distribution is computed to locally model the probability of measuring a data point in each cluster. Subsequently, the multi-view registration problem is formulated by the NDT-based likelihood function. To maximize this likelihood function, the Lie algebra optimizer is introduced and developed to optimize each rigid transformation sequentially. 3DMNDT implements data point clustering, NDT computing, and rigid transformation optimization alternately until the desired registration results are obtained. Experimental results tested on benchmark data sets illustrate that 3DMNDT can achieve state-of-the-art performance for multi-view registration. Note to Practitioners—This paper is motivated by solving the problem of registering multiple point sets. The normal distributions transform (NDT) is a well-known pair-wise registration method widely applied in the robotic domain. This paper extends the original NDT and proposes a novel registration method to simultaneously align more than two point sets. The multi-view registration is cast into the maximum likelihood estimation problem. Subsequently, the k-means clustering and Lie algebra optimizer are integrated to estimate registration parameters. Experimental results demonstrate its superior performance on the accuracy, efficiency, and robustness for multi-view registration of point sets.

Sharing private parking spaces during their idle time periods has shown great potential for addressing urban traffic congestion and illegitimate parking problems in smart cities. In this article, aiming to address the online parking-space sharing issue while ensuring the privacy of customer parking destination locations, we propose a novel destination privacy-preserving online parking sharing (DPOPS) incentive scheme. In particular, the online parking-space sharing problem is formalized as a social welfare maximization problem in a two-sided market, where parking-space providers (PSPs) and customers are regarded as sellers and buyers. Then, novel threshold value-based rules are designed to determine winners, payments, and reimbursement. Finally, winners are matched by solving a mixed-integer nonlinear programming problem, aiming to minimize the distance between customer’s destination and allocated parking space. In addition, the location privacy of the customers’ destinations is protected by the Laplace mechanism. We prove that DPOPS achieves several economically effective properties and approximate differential privacy. We analyze the upper bound of the efficiency loss of our scheme. Extensive evaluation results demonstrate that our scheme can not only achieve good performance regarding social welfare, PSP satisfaction ratio, privacy preservation, and computation overhead but also leads to shorter travel distances for customers comparing to the baseline scheme.Note to Practitioners—In this article, we address the online parking-space sharing issue with considering the parking-space providers (PSPs) and customers’ individual utility while preserving the location privacy of customers’ destinations. Most of the previous works focused on designing a centralized mechanism for allocating parking spaces without considering the protection of the customers’ location privacy. In particular, we propose an online parking-space sharing scheme called DPOPS, including a novel threshold value-based winner determination rule and a parking-space allocation rule. The proposed scheme DPOPS allows the PSPs and customers submit their bids and asks according to their own willingness and is able to improve the utilization of private parking spaces during their idle time periods. Moreover, the location privacy of customers’ destinations is protected by the Laplace mechanism. The experiments demonstrate that the proposed approach outperforms the exponential-based scheme in terms of PSP satisfaction ratio and the travel distance for parking-space customer. The proposed scheme is helpful in managing the vacant parking space in a competitive market and can be readily implemented in the real-world online parking-space sharing systems.

Production systems include both discrete part and batch operations, where an individual part is manufactured in a discrete operation, and a group of parts are processed simultaneously, i.e., in a batch, on one machine for a batch operation. Many manufacturing industries, such as battery, aircraft, and automotive, consist of mixed batch and discrete part operations, referred to as batch-discrete lines. Although such operations are widely encountered, analytical studies of these systems are limited in current literature. In this paper, a systems approach is presented to model and analyze batch-discrete lines. First, a Bernoulli machine reliability model for a two-machine batch-discrete system is introduced. Using a virtual buffer to represent the batch processing feature, performance evaluation formulae are derived and system properties are investigated. Using them, improvement analyses and bottleneck identification are presented. Then, the model is extended to systems with a quality inspection device under different control policies. To illustrate the applicability of the model, a case study in a composite part production process is described. Such a work delivers a quantitative tool for production engineers and managers to design, analyze, and improve batch-discrete manufacturing systems.Note to Practitioners—Many manufacturing systems in aircraft, automotive, battery, medical device, and defense industries include both batch operation and discrete part processing machines. In a batch operation, multiple parts are manufactured simultaneously on a batch machine, while a single part is made in a discrete part machine. Production lines with mixed batch and discrete part operations are named as batch-discrete systems. Analysis and improvement of such systems are critical to ensure high productivity and quality. However, accurate modeling and analysis of batch-discrete systems are lacking in current literature. To bridge this gap, a novel methodology is presented in this paper. Using a Bernoulli reliability machine model with a virtual buffer concept, performance measures are derived for batch-discrete two-machine lines, as well as the reversed discrete-batch lines. Then system properties, such as monotonicity, interchangeability, and reversibility, are investigated, followed by improvement analysis under constraints and bottleneck analysis. By extending to systems with quality inspections, two quality control policies, to scrap either the current batch or the whole inventory after detecting a degraded part, are studied. In addition, a case study of heating (batch) and trimming (discrete) operations in composite panel production lines is presented, and improvement strategies are investigated, to illustrate how to apply the model and analysis in practice.

Machines consume intensive energy in some production systems. Although substantial efforts have been devoted to performance analysis, continuous improvement, and design of production systems, the research on reducing the energy consumption in these systems is limited. In this article, the problem of minimizing the energy consumed by machines in the two-machine Bernoulli serial line, which has been formulated as nonlinear programming with production rate constraint, is investigated. Specifically, structural characteristics and optimality conditions of the problem are analyzed, and two nonlinear algebraic optimality equations are established. To solve the optimality equations, their properties are explored, and an effective algorithm based on the binary search method is developed. Furthermore, the sensitivity of the optimal solution with respect to system parameters is quantitatively analyzed. Based on the sensitivity analysis, some useful insights on the optimal objective value are extracted.Note to Practitioners—In energy-intensive manufacturing enterprises, reducing the energy consumption in their production systems is urgent and challenging. In this article, an energy consumption optimization problem in a simple model of production systems (i.e., in the two-machine Bernoulli line) is investigated. Machine efficiencies are optimized so that the total energy consumption of machines is minimized, while a required production rate is ensured. The reported results enable a novel managerial paradigm for production systems, especially for energy-intensive systems. Although the model of the two-machine Bernoulli line is simple, as a cornerstone, this research will be extended, in the future, to long Bernoulli lines and more practical production systems, e.g., lines with geometric, exponential, and non-exponential machine reliability models.

The penetration of plug-in electric vehicles (PEVs) has greatly increased over the past few years. By using vehicle-to-grid (V2G) technology, PEVs can be used as “mobile batteries” in a microgrid. Here, we aim to coordinate the V2G dispatch with traditional energy management in a corporate energy system (CES). To do so, a two-stage robust optimization (RO) model is built with respect to uncertainties in the CES, e.g., photovoltaic (PV) power. Particularly, relationships between the working time schedule and PEVs are investigated and analyzed for the first time, and a novel PEV aggregator model, i.e., shift-working V2G, is presented. The shift-working V2G model provides beneficial characteristics, like weakened randomness and stable storage capacity. A quantitative method to evaluate the V2G capacity is then presented. An analytical solution methodology is also proposed, which can equivalently convert the robust “min-max-min” model to a single-level mixed-integer linear programming (MILP) model. Case studies are conducted for an iron and steel company in Shanghai, China, with almost 40 000 PEVs. The results show that V2G integration can significantly improve the load-tracking ability of CES and help reduce the energy cost, although the V2G cost is considered. The computational efficiency is also improved compared with the existing methods. Note to Practitioners-This article is motivated by the problem of using the battery storage capacities of plug-in electric vehicles (PEVs) in a corporate energy system (CES) such as an iron and steel plant, aiming at minimizing the energy cost. In existing studies, behaviors of PEVs (e.g., arriving or leaving time) have usually been elusive since they are directly decided by drivers, and thus, it is difficult to determine how much storage capacity PEVs can provide. However, in a CES where a shift-work regulation is implemented, employees should arrive or leave punctually during each shift, and the same is true of their PEVs. Based on this fact, PEVs within one shift could show weakened randomness and stable storage capacities. In this article, the influences of the shift-work regulation on the PEVs are fully analyzed, and the shift-working V2G provides an easy way to integrate PEVs into the CES. In practice, the shift-working V2G model can be applied to any energy system that also implements a shift-work regulation, such as a fire station.

In energy-intensive production systems, machines consume a huge amount of energy during the production process. Since the high energy consumption is prominent in production systems, reducing the energy consumption and improving the energy efficiency are of great significance. In this article, the problem of minimizing the total energy consumption in the two-machine Bernoulli line with general lower and upper bounds of machine efficiencies is investigated. Specifically, first, it is formulated as a nonlinear constrained programming. Then, the structure of its feasible region and properties of its objective function are analyzed. Based on these explorations, its optimal solution is constructed from the solution of a relaxation problem (i.e., the problem without general bounds on machine efficiencies), which has been analyzed and solved in the literature. Finally, the results obtained are extended to the problem of minimizing the energy consumption per job to improve the energy efficiency of the two-machine Bernoulli line. The sensitivity analysis of the optimal objective value with respect to the required production rate is carried out for both the total energy consumption and the energy consumption per job optimization models. Note to Practitioners-As is well known, reducing the energy consumption and improving the energy efficiency in energy-intensive production systems are of great significance. In practical production systems, due to physical limitations, machine efficiencies are usually confined to a subset of (0, 1]. Although for the case without machine-efficiency restrictions (i.e., the efficiencies can be selected in (0, 1]), the problems of reducing the energy consumption and improving the energy efficiency have been investigated for some production systems, the machine-efficiency-constrained problems, which are more practical and challenging, have not been examined yet. In this article, two problems, which minimize the total energy consumption and the energy consumption per job in the two-machine Bernoulli line, respectively, are formulated and solved. In the future, this research will be extended to long Bernoulli lines and systems with geometric, exponential, and nonexponential machine reliability models, and the results obtained will be implemented in practical production systems.

As the main force of energy consumption, machines consume a huge amount of energy in some production systems. To operate the systems in an energy-efficient way, the efficiencies of the machines should be elaborately optimized so that the total energy consumption in the production lines is minimized while maintaining the required production rate. For this purpose, this article investigates the energy consumption optimization problem in Bernoulli serial lines having more than two machines. Specifically, this problem is first formulated as a nonlinear programming with a production rate constraint; due to difficulties in solving this constrained nonlinear programming, structural characteristics of the problem and properties of the objective function, which are inspired by the results and insights obtained in two-machine lines, are analyzed. Finally, based on the structural characteristics and properties explored, a solution methodology is developed to recursively solve the unique optimal solution of the energy consumption optimization problem. Extensive numerical experiments show that the solution provided by this method, which has the reversibility property and is better than the one provided by an existing method in the literature, is numerically optimal (note that the theoretically optimal solution is extremely hard to obtain if not totally impossible). Note to Practitioners-Machines consume a huge amount of energy in energy-intensive (i.e., high energy-consuming) production systems in, e.g., automobile, semiconductor, and steel companies. Due to economic, social, and environmental concerns, it is a high priority to consider ways to reduce the energy consumption in these production systems. In this article, the problem of minimizing the total energy consumption in Bernoulli serial lines while maintaining the required production rate is investigated. Based on the aggregation method developed for performance analysis of long lines and the optimization results obtained for two-machine lines, the energy consumption optimization problem for long Bernoulli serial lines is elaborately analyzed and a method is proposed to recursively solve its unique optimal solution (i.e., the optimal machine efficiencies). From the point of view of system operation, this research, which makes the system operate in the most energy-efficient manner, offers a new managerial paradigm for the production systems.

This article focuses on the problem of deadlock for sequential automated manufacturing systems (AMSs) that allow for the general resource allocation and flexible routings. A class of Petri nets, systems of sequential systems with shared resources (S4PR), are used to model these considered AMSs. Our previous work has showed that deadlocks in S4PR are characterized by saturated perfect activity-circuit (PA-circuit). In this article, we divide all saturable PA-circuits into two categories: 1) dependent and 2) independent. An algorithm is proposed to compute all independent saturable PA-circuits. We prove that by adding a monitor for each independent PA-circuit to ensure that it is not saturated, all dependent PA-circuits cannot be saturated either and deadlocks in S4PR are successfully prevented. The presented method simplifies the structure of the deadlock controller without imposing tight constraints on the system. Finally, the proposed controller is illustrated by some examples.

Many optimization problems for heating, ventilation, and air conditioning (HVAC) control systems usually refer to multiple timescales. This paper studies a two-timescale decision problem for indoor temperature regulation by HVAC with the objective to improve user's comfort under limited energy consumption. A slow timescale is divided into several fast timescales, so the two timescales have inherent association. Using states of fast timescale to represent the state of its slow timescale properly is challenging, thereby the weighted mean type is proposed in this paper. We find that the realizability and consistency in two-timescale cannot be guaranteed in existing empirical models, which are the bases for physical application. Therefore, this paper proposes a method that building the fast timescale model first and then inducing the slow timescale model from the fast timescale model. In addition, such a problem in the high-order system is more complex, whose solution is also discussed in this paper. The results of case studies show that the induced model can guarantee the consistency and realizability and meet the user desired temperature for comfort. This paper was motivated by the multi-timescale problem that making a decision of controlling the indoor temperature for the user's comfort with energy constraint. Existing approaches to build a two-timescale model by experience would cause the inconsistent deviation. If the HVAC works in a closed environment system like a space station, besides crew comfort, the temperature for experiments in the space station should be accurate. Therefore, the deviation from the actual temperature and the desired temperature will cause adverse effects. In this paper, we mathematically derive the thermal state transfer equations and analyze some problems. Then, this paper suggests a new approach to build the two-timescale model for various conditions, in which the models of two timescales are consistent with each other. Preliminary experiments suggest that this approach is feasible and effective. In future research, we will jointly make optimization and decision of multiple appliances in home energy management (HEM) system.

Series machines, i.e., machines (which are usually unreliable) arranged in series with no buffering, are pervasive in production systems. In the analysis, design, and optimization of the series-machine system, the efficiency analysis is one of the most fundamental issues. There are not a lot of researches analyzing the efficiency of the series-machine system, and almost all of them assume that the system operates under type-I failure mechanisms (i.e., the breakdown of a machine could make all other series machines forced down) rather than under type-II mechanisms (i.e., the breakdown of a machine does not make any other series machines forced down). The reason that the type-I failure mechanisms are usually assumed in the literature is that the analysis of the series-machine system under type-II mechanisms is much more complex than under type-I mechanisms, although type-II mechanisms are more common in practice. To thoroughly and systematically estimate the efficiency of the series-machine system, in this paper, we propose a unified analytical approach to investigate the efficiency under both type-I and type-II failure mechanisms. Both cases of deterministic and random cycle times are considered. Different from under type-I failure mechanisms, analytical expressions of the efficiency of series-machine systems under type-II failure mechanisms are extremely hard to obtain, and thus, limit bounds of the efficiency are derived and algorithms are developed to calculate its exact value. Results show that the series-machine system under type-II failure mechanisms is more efficient than under type-I mechanisms, which, intuitively making sense, is the reason that type-II mechanisms are more common in the industry.

Production Lead Time \mbi (LT) is the average time a part spends in the system, being processed or waiting for processing. In systems with unlimited buffers, \mbi LT may be orders of magnitude larger than the total processing time, leading to serious economic and quality problems. At present, no systematic analytical methods for evaluation, analysis, and control of \mbi LT in systems with machines having up- and downtime characterized by continuous random variables are available. This paper is intended to develop such methods. Specifically, we address synchronous serial lines with exponential machines and derive formulas for \mbi LT as a function of machine parameters and raw material release rate. Using these formulas, we develop methods for open- and closed-loop raw material release, which result in the desired \mbi LT. For asynchronous exponential lines, we provide an upper bound on LT. For non-exponential lines (e.g., Weibull, gamma, and log-normal), we offer an empirical formula for \mbi LT as an affine function of the coefficient of variation. The results reported in this paper enable a new paradigm for production systems management, namely: manage a production system so that the desired \mbi LT is ensured, while the throughput is maximized. This paper analyzes production lead time in manufacturing systems with hardware-unlimited buffers. The main practical insights obtained are as follows. . The lead time as a function of the raw material release rate has a “knee-type” behavior. . Releasing raw material beyond the knee results in no noticeable increase of the throughput, but in unlimited increase of the lead time. . To maintain the lead time close to the knee (and, thus, maximize the throughput), a simple control law may be used: release raw material on, say, hourly or shift-type basis, if the total work-in-process is below a certain threshold (provided in the paper) and do not release otherwise.

Cellular lines are production systems consisting of cells comprised of machines performing similar operations. These systems are notorious for having excessively long lead time (LT) that are often an order of magnitude longer than the total processing time by all machines in the system. The main goal of this paper is to carry out an analytical investigation of this phenomenon and offer methods for its alleviation. To accomplish this, we develop a technique for performance evaluation of cellular lines with asymptotically reliable Bernoulli machines and use it for analysis and control of LT.

In systems of general assembly line with material handling, line-side buffers need to be carefully assigned to a limited number of material delivers (drivers) for part delivery to avoid production stoppage due to material shortage. Such a problem is referred to as line-side buffer assignment problem (LBAP). In this paper, we focus on fixed zoning version of LBAP. We formulate the problem, prove its NP-hardness, and propose an algorithm based on two structural characteristics of the LBAP problem-one being the analogousness between our problem and the parallel machine scheduling (PMS) problem and the other being the monotonicity of the system throughput in the course of assigning line-side buffers to drivers. The developed algorithm globally converges with probability one when there exist feasible assignments. The algorithm is tested on a real system, and the results show that it is effective for solving the LBAP problem.

Performance evaluation of complex manufacturing systems is challenging due to many factors such as system complexity, parameter uncertainties, problem size, just to name a few. In many cases when a system is too complex to model using mathematical formulas, simulation is used as an effective alternative to conduct system analysis. A manufacturing system is a good example of such cases where both system performance and system complexity are greatly impacted by material handling (MH) strategy, management, and operational control. In this paper, we study vehicle general assembly (GA) system with MH, and focus on developing an efficient simulation method for modeling and analysis where traditional simulation methods may suffer from computation intensity. Making use of the partial system decomposability, we introduce an aggregated event-scheduling simulation method with two-level framework. A dividing mechanism with boundary conditions is employed in top-level simulation to divide the global event list into small sizes. A timing-focuses strategy based on max-plus algebra is applied in bottom-level local simulation to further reduce local event lists. With this new method it is possible to mimic real production systems fast and accurately within a reasonable computational time frame. The effectiveness and efficiency of the new simulation method are validated through experimental results.