Manufacturing Systems Research Articles

Machine Learning-based Dispatching for a Wet Clean Station in Semiconductor Manufacturing

The concept of cyber manufacturing has become a critical element in semiconductor fabrication environments, where automation and systemization are integral, for addressing the growing complexity of processes and facilitating predictive capabilities through data integration. This study deals with the dispatching problem to minimize makespan at a wet clean station in semiconductor fabrication using artificial intelligence-enabled manufacturing control techniques. The wet clean station is comprised of sequential chemical and rinsing baths for cleaning wafer lots and multiple robot arms for lot handling. In the station, wafer lots are sequentially immersed in several baths for cleaning to eliminate residual contaminants and stains that cause defects on wafer surfaces. The station can process various types of products, and the specific order of immersion differs depending on the product type. Unlike typical dispatching problems, the information required for dispatching, such as processing times and sequences inside the station, is not available. The only available data are historical logs that record when each lot enters and leaves the station. However, even when cleaning the same product type, the duration that lots spend in the station may vary based on the combination of product types being cleaned simultaneously and the settings of the station. Thus, using the time records, this study proposes a dispatching method based on machine learning models (multiple linear regression, deep neural network, and convolutional neural network). The proposed algorithms were evaluated and verified by comparing them with CPLEX solving a mixed integer programming and dispatching methods used in a semiconductor fab in Korea. Through this experiment, we observed that the proposed models can provide dispatching solutions that are practical and effective in a rapidly changing production setting. These models have the potential to enhance the capacity of a wet clean station and will contribute to artificial intelligence-based manufacturing system control.

Hybrid manufacturing thermal environment cycle and industrial economic transformation based on artificial intelligence

With the continuous development of global manufacturing, hybrid manufacturing has become an important way to improve production efficiency and respond to market demand. However, traditional manufacturing enterprises face many challenges in thermal environment management and energy consumption, and urgently need to achieve transformation and upgrading through artificial intelligence technology. This article aims to explore the thermal environment cycle of hybrid manufacturing based on artificial intelligence and its impact on industrial economic transformation, in order to provide theoretical support and practical guidance for manufacturing enterprises in intelligent manufacturing and energy management. We have researched and designed an intelligent manufacturing system, clarified the design principles and requirements of the system, constructed the system architecture, and designed the functional modules in detail. By collecting and processing data, using intelligent scheduling prediction technology to simulate the thermal environment of the manufacturing workshop, and then simulate the energy consumption situation. Finally, a manufacturing workshop scheduling model was constructed, process optimization strategies were proposed, and the transformation path of industrial economy was explored. Research has shown that through the implementation of intelligent manufacturing systems, the energy consumption of manufacturing workshops is significantly reduced, the thermal environment is effectively improved, and the application of process optimization and scheduling models greatly improves production efficiency. The exploration of the path of industrial economic transformation provides new ideas for the sustainable development of enterprises in the new situation.

A self-imitation learning approach for scheduling evaporation and encapsulation stages of OLED display manufacturing systems

In modern organic light-emitting diode (OLED) manufacturing systems, scheduling is a key decision-making problem to improve productivity. In particular, the scheduling of evaporation and encapsulation stages has been confronted with complicated constraints such as job-splitting property, preventive maintenance, machine eligibility, family setups, and heterogeneous release time of jobs. To efficiently solve such complicated scheduling problems, reinforcement learning (RL) has drawn increasing attention as an alternative in recent years. Unfortunately, the performance of the RL-based scheduling methods might not be satisfactory since unexpected correlations between actions are caused by machine eligibility restrictions, making it more challenging to address the credit assignment problem. To minimize the total tardiness, this article proposes a self-imitation learning-based scheduling method in which an agent utilizes past good experiences to exploit efficient exploration. Furthermore, a novel return design is introduced to overcome the credit assignment problem by considering machine eligibility restrictions. To prove the effectiveness and efficiency of the proposed method, numerical experiments are carried out by using the datasets that simulated the real-world OLED display manufacturing systems. Experiment results demonstrate that the proposed method outperforms other baselines, including rule-based and meta-heuristics, as well as the other DRL-based method in terms of the total tardiness while reducing computation time compared to meta-heuristics.

A novel methodology based on long short-term memory stacked autoencoders for unsupervised detection of abnormal working conditions in semiconductor manufacturing systems

A novel methodology based on long short-term memory stacked autoencoders for unsupervised detection of abnormal working conditions in semiconductor manufacturing systems

A learning-based memetic algorithm for energy-efficient distributed flow-shop scheduling with preventive maintenance

In manufacturing systems, implementing preventive maintenance (PM) is essential for ensuring sustainable production since the inevitable wear and tear of machines can significantly affect production efficiency. In today’s decentralized economy, distributed shop scheduling has emerged within the framework of distributed manufacturing to reduce costs, enhance efficiency, and strengthen competitiveness. Thus, this paper proposes a learning-based memetic algorithm (LMA) for addressing the energy-efficient distributed flow-shop scheduling problem with preventive maintenance (EDFSP-PM) to minimize both makespan and total energy consumption simultaneously. First, a mathematical model is formulated and encoding and decoding methods are developed to map solutions to schedules with consideration of PM operations. Second, two heuristics are employed to generate high-quality solutions and various problem-specific operators are designed for different sub-problems and objectives. Third, a hierarchical learning mechanism is proposed via employing multi-layer Q-learning to select appropriate operators for solutions with diverse characteristics. Fourth, a feedback learning mechanism with solution pool is devised to reintegrate solutions from the pool into the search process to enhance search efficiency. Finally, numerical experiments are conducted to verify the effectiveness of the designed mechanisms. The comparative results demonstrate superior performance of the proposed LMA in terms of convergence and diversity.

Towards resilience in Industry 5.0: application of system dynamics in matrix-structured manufacturing system

In the face of increasing global disruptions, resilient manufacturing systems are critical to ensuring operational stability, a concept central to the Industry 5.0 vision. The Matrix-Structured Manufacturing System (MMS) is a key example of such resilience, designed to maintain functionality and recover quickly under major disruptions. This paper introduces a novel System Dynamics (SD)-based modelling and simulation framework to quantitively assess the performance indicators and resilience of MMS. By simulating key disruption scenarios, including partial equipment failures and order fluctuations with resource constraints, this study evaluates the system’s response and recovery capabilities using a Comprehensive Resilience Index (CRI). Unlike conventional approaches, the proposed SD model captures the dynamic interactions across various production configurations and simulates the operational logic governing them, which can also be applied to other production systems. The simulation results demonstrate the potential of SD to support decision-making by providing insights into the effectiveness of reconfiguration strategies and resource management. This work aims at offering robust methods for quantitative resilience assessment and strategic planning towards the practical implementation of resilient manufacturing in Industry 5.0 envisions.

Blockchain‐based smart and secure manufacturing systems

AbstractIn the current data sharing environment of smart manufacturing systems, there is a lack of a transparent, open, and fair interaction environment, and there exists a serious imbalance in data sharing and transmission, which leads to data trust issues. To address this challenge, we propose a fine‐grained access control method for IoT data sharing based on blockchain technology (the Bayesian information criterion [BIC] scheme for IoT data sharing). This approach includes ex‐ante supervision and authentication of data sources, fine‐grained access control during data usage, and ex‐post tracking of malicious users. Firstly, a system model is built, and symmetric encryption is used to encrypt the data in order to improve computational efficiency. Subsequently, scheme design and algorithm design are carried out to encrypt the symmetric key using ciphertext‐policy attribute‐based encryption to achieve one‐to‐many data sharing, which greatly reduces the number of repeated encryptions and computational overheads. The data owner uploads the encrypted data to the cloud server, and the data user must hold the token generated by the blockchain to interact with the cloud and obtain the corresponding information. The results show that the BIC scheme not only ensures the legitimacy and security of data access but also locks up and traces malicious users who may have compromised their private keys, preventing illegal data sharing and key misuse; it offers significant advantages in terms of storage efficiency and time consumption, and also facilitates a flexible and secure data sharing process.

Joint optimization of preventive maintenance and product quality improvement policies for deteriorating manufacturing systems with quality-reliability dependency

Machine reliability and product quality are usually investigated independently in most traditional models. However, in the actual manufacturing process, machine reliability and product quality are commonly interdependent due to strong coupling between the machine and the product. In this paper, three joint optimization models for preventive maintenance (PM) and product quality improvement are developed for deteriorating manufacturing systems considering quality-reliability dependency. First, the degradation process of quality-related components (QRCs) is depicted by Gamma process, and product quality deviation is obtained by considering the quantitative relationship between the degradation of QRCs and product quality. Second, joint optimization policy of age-based maintenance and product quality improvement is proposed for deteriorating manufacturing systems with quality-reliability dependency. Third, above maintenance policy is extended to manufacturing systems processing jobs with random processing time, and then maintenance first (MF) and maintenance last (ML) policies are developed, respectively. Furthermore, we compare all of the maintenance models to determine which policy should be selected for manufacturing systems under specific conditions, and all theoretical discussions in this paper are analytically made. Finally, the effectiveness of the proposed joint policies is verified by a case study on a cylindrical gear housing machining process.

Online in situ detection of deposited height deviation during additive manufacturing

Deposited height deviation (DHD) of printed layers is a common surface defect that restricts vertical printing accuracy during the additive manufacturing process. The accumulation of DHD layer by layer inevitably leads to the failure of subsequent additive manufacturing tasks. Therefore, accurate online measurement of DHD is crucial. This study proposed a novel amplification computer-vision measurement (ACVM) method that effectively utilizes both melt pool images and temperature information, achieving a DHD detection sensitivity of approximately 9.96 μm. Theoretical connections between image features and DHD, as well as the theoretical associations between instantaneous temperature characteristic and DHD, have been systematically deduced. Based on these two theoretical relationships, DHD can be accurately and synchronously detected directly through the positions of image features and temperature. A single-camera dual-channel multi-signal detection (SDMD) system was developed and implemented within a laser-engineered net shaping (LENS) additive manufacturing system. Subsequently, an online measuring and verification experiment was designed to assess the height deviation of thin-walled structural parts. The experimental results demonstrated that the ACVM method provided an early response to DHD. The method exhibits significant technical application value in quality control in future.

Observing and Quantifying Fracture Dynamics in Solid-State Battery Alloy Anodes with Operando Synchrotron X-Ray Tomography

Characterizing buried interfaces in solid-state batteries is critical for understanding and enabling this next-generation energy storage technology [1]. X-ray computed tomography (XCT) is a powerful characterization tool owing to its high spatial resolution (<1 µm), non-destructive nature, access to buried interfaces, and three-dimensional imaging capabilities [2]. Additionally, synchrotron-enabled XCT can collect high resolution, three-dimensional datasets with appropriate temporal resolutions to observe the evolution of buried solid-state interfaces under operando conditions [2]. These qualities have been important for revealing the dynamic evolution of excess-lithium metal anodes and anode-less lithium solid-state batteries using synchrotron XCT [3-8].Synchrotron XCT investigations into the behavior of lithium metal anodes in solid-state batteries have been vital for the research community’s goal of understanding and mitigating degradation of these systems. However, viable lithium anode solid-state batteries are yet to be realized [1]. As a solid-state battery anode, lithium metal is expensive, highly reactive, and prone to dendritic short-circuiting, making it difficult to incorporate into manufacturing systems and unreliable as an anode [9]. Owing to this, alternative solid-state battery anode materials have been proposed. Chief among these are alloy anode materials, such as silicon, tin, and aluminum, which electrochemically alloy and de-alloy with lithium during charge and discharge of the battery, respectively [10]. These materials experience difficulties in conventional liquid electrolyte lithium-ion batteries due to their large volumetric change during electrochemical cycling that constantly exposes new surface area to the highly reactive liquid electrolyte; this consumes the anode material and quickly degrades capacity [11]. With a solid-state electrolyte, however, this effect is mitigated because the solid electrolyte does not flow to contact new surfaces, resulting in less SEI formation [12]. Despite recent performance advancements with alloy anode materials in solid-state batteries [12,13], there is not yet a fundamental understanding of their dynamic evolution and electro-chemo-mechanical behavior in these systems. Most efforts to characterize this behavior have been conducted with ex situ or postmortem imaging techniques that can artificially damage the anode and its interface with the solid electrolyte [12,13].Here, we use operando synchrotron XCT to non-destructively investigate the morphological evolution of silicon-based anodes in solid-state batteries during electrochemical cycling. Silicon was selected as the alloy anode material owing to its large specific capacity (3579 mAh g-1) and resulting massive volumetric evolution during cycling (>300%) [10]. Utilizing the excellent spatial and temporal resolution of synchrotron XCT, we reveal the dynamic microstructural evolution of silicon-based electrodes during discharge and charge. We observe the formation of cracks within the silicon anode, as well as identifying key fracture mechanisms that govern impedance evolution. Our results provide new understanding of degradation in silicon anode solid-state batteries and give insight into materials design strategies for mitigating degradation. These strategies are expected to improve the performance of silicon anode solid-state batteries by reducing impedance and increasing capacity retention. References J. Janek and W. G. Zeier, Nat Energy, 8, 230–240 (2023).P. J. Withers et al., Nat Rev Methods Primers, 1, 1–21 (2021).F. Shen, M. B. Dixit, X. Xiao, and K. B. Hatzell, ACS Energy Letters, 3, 1056–1061 (2018).Z. Ning et al., Nature Materials, 20, 1121–1129 (2021).J. A. Lewis et al., Nature Materials, 20, 503–510 (2021).J. A. Lewis et al., Advanced Energy Materials, 13, 2204186 (2023).Z. Ning et al., Nature, 618, 287–293 (2023).S. E. Sandoval et al., Joule, 7, 2054–2073 (2023).K. B. Hatzell et al., ACS Energy Letters, 5, 922–934 (2020).J. A. Lewis, K. A. Cavallaro, Y. Liu, and M. T. McDowell, Joule, 6, 1418–1430 (2022).L. Y. Beaulieu, K. W. Eberman, R. L. Turner, L. J. Krause, and J. R. Dahn, Electrochem. Solid-State Lett., 4, A137–A140 (2001).D. H. S. Tan et al., Science, 373, 1494–1499 (2021).Y. Liu et al., Nat Commun, 14, 3975 (2023).

(Invited) Physics Informed Data Driven Manufacturing for Li-ion Batteries

The road to carbon neutrality requires a massive scale up of battery manufacturing on a rapid timescale. Li ion batteries are complex systems with many materials and interfaces, and the challenge of controlling microcale material properties of millions of units per year demands a physics and a data driven approach to manufacturing. Conversely, the Toyota Production System (TPS) for manufacturing is a deeply human-led process, thus, we have a need to integrate a variety of models with human expertise and experience. We will present our manufacturing optimization experience and approach with safety and cost as target metrics. Economics incorporates both cost of production (including yield and throughput) as well as future performance (reliability). We will present several case studies of the complexities of implementing ML for complex manufacturing problems, with emphasis on the role of inspection and safety. Finally, we will place advanced manufacturing in the context of the entire battery life cycle from materials to use, and draw on these case studies to present a vision of both data- and human-driven process improvements for battery manufacturing. Key area will focus on defect detection, root cause analysis using production data, and rapid adoption of new chemistries and cell infrastructure.

Additive manufacturing in construction using unmanned aerial vehicle: Design, implementation, and material properties

Currently, 3D printing concrete method is mostly being developed based on on-site construction. This study addresses the inherent limitations of conventional on-site 3D printing in construction, particularly the challenges posed by constrained working environments and equipment restrictions. To overcome these obstacles, the research introduces a novel architectural additive manufacturing process utilizing unmanned aerial vehicle (UAV), enabling off-site 3D printing of complex geometric structures. The primary aim of the study is to develop UAV-based equipment capable of autonomous 3D printing and real-time structural assessment, thereby enhancing the flexibility and adaptability of construction technologies. The methodology involves the design, development, and integration of a UAV-enabled additive manufacturing system, incorporating specialized components, including a cementitious material optimized for aerial deposition. Experimental testing focused on evaluating printability, structural integrity, and material performance, with compressive strength tests yielding values up to 39 MPa at 60 days, meeting general engineering standards. The findings of this study confirm the feasibility of combining UAV technology with 3D printing to improve operational flexibility and accessibility, particularly in remote or difficult-to-reach locations where conventional construction methods are impractical. This research makes a significant contribution by addressing the limitations of traditional 3D printing techniques and presenting a new avenue for construction automation. The innovative UAV-based system offers potential applications in diverse fields, including disaster response, space exploration, and infrastructure development in remote or hazardous environments. As such, this study provides a foundational basis for future advancements in UAV-driven construction technologies and opens up new possibilities for autonomous construction solutions.

Discharge Characteristics of High-Loading Coated Cathode Active Materials for High-Capacity High-Efficiency Secondary Batteries Implementation

In response to the demand for secondary batteries, mass production of secondary battery materials is required, leading to the need for significantly increased production capacity and the development of high-speed production systems for electrode materials. Furthermore, in accordance with global environmental regulations and energy management regulations, reducing carbon emissions is necessary for enhancing future competitiveness through the application of eco-friendly and digital processes. Additionally, to meet the demands of commercializing high-energy-density secondary batteries and achieving carbon neutrality, it is necessary to establish advanced and efficient electrode manufacturing systems. Therefore, the design and development of processes for dispersing components that determine the physical properties of slurries, as well as mixing and coating processes for battery manufacturing, are crucial. Density control ensures accurate material composition, and control of component ratios and viscosity is essential for ensuring consistency in slurry manufacturing processes. During discharge, as the active material at the cathode undergoes reduction, the energy difference with the reference electrode gradually decreases, resulting in a decrease in cell potential. The slope change of the discharge curve is influenced by factors such as the extent of lithium ion diffusion at the surface of the active material particles, phase transitions of the active material, collapse of crystal structure, and leaching of transition metal ions comprising the active material into the electrolyte. Moreover, even at the same current density, charge and discharge curves may vary depending on factors such as particle size and size distribution of the active material, temperature, mixing conditions of active material/binder/conductive agent, electrolyte characteristics, and porous structure of the separator. In this study, the evaluation of electrode characteristics of active materials was conducted to optimize coating conditions and mixing conditions for achieving high efficiency and high loading for the implementation of high-energy-density secondary batteries. Through evaluation based on C-rate according to Loading Level, analysis of capacity implementation characteristics based on electrode density was performed, aiming to apply it to the development of carbon reduction-type high-loading, wide-electrode slurry coating equipment systems for the implementation of high-energy-density secondary batteries. Expectations include the development of carbon reduction process technologies through maximum homogeneity, enhancing production speed, and energy savings in processes.Secondary battery, Cathode active material, Slurry, Loading level, C-rateThis work was supported by the Technology Innovation Program (RS-2022-00155881, Development of high-efficiency and high-loading coating system for implementation of 350Wh/kg secondary battery) funded By the Ministry of Trade, Industry & Energy(MOTIE, Korea)

Optimizing energy efficiency in unrelated parallel machine scheduling problem through reinforcement learning

The industrial sector plays a significant role in global energy consumption and greenhouse gas emissions. To reduce this environmental impact, it's crucial to implement energy-efficient manufacturing systems that utilize sustainable materials and optimize energy usage. This can lead to benefits such as reduced carbon footprints and cost savings.In recent years, metaheuristic approaches have been focused on minimizing energy consumption within the Unrelated Parallel Machine Scheduling Problem (UPMSP). Traditional methods often overlook complex factors like release dates, due dates, and job setup times. This research introduces a novel algorithm that integrates reinforcement learning (RL) with a genetic algorithm (GA) to address this gap.The proposed RLGA algorithm, rooted in the dynamic field of evolutionary reinforcement learning, breaks down policies into smaller components to isolate essential parameters for problem-solving. Through comprehensive analysis, hyperparameters that influence optimal results are identified, facilitating automated hyperparameter selection and optimization. The expert system takes into account problem characteristics such as machine or job saturation, job overlap, and the maximum values of target variables, allowing instances to be grouped into clusters. These clusters are solved using a genetic algorithm with varying combinations of mutation and crossover hyperparameters. The most suitable approach for each cluster is determined by analyzing the results, and this configuration of hyperparameters is applied iteratively to optimize the solution search.The effectiveness of RLGA is evaluated across benchmark instances with different complexities, machine sets, jobs, and constraints. Comprehensive comparisons against existing methods highlight the superior performance and efficiency of RLGA in optimizing energy use and solution quality. Experimental results show that RLGA outperforms well-known solvers like CPO, CPLEX, OR-tools, and Gecode, making it a promising approach for optimizing energy-efficient manufacturing systems.

Rapid Prototyping of Multiscale Flexible Printed Circuit Boards With NeXus, a Robotic Additive Manufacturing System

Abstract This paper demonstrates the NeXus system, a multiscale robotic additive manufacturing platform developed at the Louisville Automation and Robotics Research Institute, as a rapid prototyping tool through additively manufacturing a multilayer flexible printed circuit board (FPC) with a printed strain sensor and soldered surface mounted devices (SMD). Manufacturing of the demonstrator requires the application and curing of multiple materials with specialized properties, tools for automated assembly, and software advances to streamline the process enabling the use of industry-standardized programs to command the NeXus system. Additive manufacturing processes supported by the NeXus include aerosol jet printing (AJP) for fine feature silver conducting lines, direct write ink-jet printing for insulating materials, and intense pulsed light (IPL) for curing materials between depositions. The NeXus system transports and manipulates parts using a six-degree-of-freedom (DOF) high-precision positioner. Solder paste deposition and pick-and-place (PnP) procedures are performed by a 4DOF Selective Compliance Articulated Robot Arm (SCARA). Conversion methods between traditional printed circuit board (PCB) design software and production-ready command scripts were developed to translate basic drawings into command scripts. Multilayer structures with AJP 50-μm wide lines, an insulating bridge with a thickness of around 100 μm, and SMDs soldered to silver AJP pads were integrated within the demonstrator. An operational amplifier and other SMDs reduce the complexity of the accompanying control circuit and amplify the sensor's response by 1830 times. The successful fabrication of the demonstrator FPC highlights the rapid prototyping ability of the NeXus system.

System for product flow configuration selection for reconfigurable manufacturing system

System for product flow configuration selection for reconfigurable manufacturing system

A Petri net-based approach to robust deadlock prevention in automated manufacturing systems with unreliable resources

This paper studies the robust deadlock control issues in automated manufacturing systems (AMSs) with unreliable resources. The purpose of this paper is to synthesize a robust prevention supervisor to guarantee that the controlled system can operate smoothly when unreliable resources work well, and to ensure that parts that do not require any failed resources in their remaining routes can continue to be processed even if unreliable resources malfunction. In this paper, Petri nets (PNs) are employed to model the considered AMSs, which allow multi-quantity and multi-type of resource acquisitions. First, to avoid the resource’s underflow, a set of resource constraints denoted as a set of inequalities are developed based on the nominal capacity of shared resources and the minimal resource requirements of processes. Control places (monitors) are derived along with their control variables so as to resolve the blocking states caused by resource failures. Second, to guarantee the system’s liveness, a monitor is designed for each considered siphon in the different subnets derived from a system net. As a result, a robust prevention supervisor is synthesized for AMSs with unreliable resources. The effectiveness and superiority of our method are elucidated by some analyses and discussions.

Ontology and production rules-based dynamic knowledge base construction methodology for machining process

With advancements in manufacturing, knowledge engineering has become important in supporting intelligent decision-making within manufacturing systems. However, existing process knowledge bases, integral to knowledge engineering, and essential for machining efficiency, product cost, and production cycles by integrating multi-source knowledge, are limited to generality, scalability, and adaptability to real production environments. These constraints undermine the application and reliability of process knowledge bases in decision-making. To overcome these challenges, an approach to constructing a dynamic machining process knowledge base (DMPKB) utilizing ontology and production rules is proposed. Firstly, a machining process knowledge model is developed by reorganizing concepts and relations to restructure process cases and experiences, thereby building a comprehensive knowledge base. Secondly, different update strategies are devised to fulfill the requirements of various components within the knowledge base. Finally, the effectiveness is validated by constructing a DMPKB for CNC boring machine bearing seats. Meanwhile, application verification is performed by generating process plans for a CNC boring machine bearing seat, showcasing the feasibility and utility of the developed knowledge base.

A relevant method for evaluating information management systems in manufacturing

In the context of growing digitalization and the transition to domestic information management systems in manufacturing, it becomes critically important to develop adequate methods for evaluating their effectiveness. The relevance of this study is due to the need to create effective tools for the analysis and selection of information management systems that meet the specifics of the Russian market and the conditions of import substitution. The purpose of the study is to develop and justify a method for evaluating management information systems that take into account both quantitative and qualitative indicators. Methodologically, the study includes an analysis of existing valuation methods such as economic value added and total cost of ownership, identifying their shortcomings and developing a new method based on a weighted sum of criteria. The application of the proposed method has shown that it provides a more accurate and comprehensive assessment, taking into account technical characteristics and user satisfaction, which is critical for effective intra-company and strategic planning. The results confirm the hypothesis that the integration of quantitative and quantitative indicators, taking into account the importance coefficients, increases the reliability of the assessment of the information management system. Limitations include high resource requirements and the need to involve internal and external experts, which may limit practical applications. In the future, automation of the criteria normalization process and adaptation of the method to other industries are recommended.

Scheduling AMSs with generalized Petri nets and highly informed heuristic search

The design of the heuristic function in a Petri-net(PN)-based A∗ search significantly impacts search efficiency and schedule quality for automated manufacturing systems (AMSs). In Luo et al. (2015), two admissible heuristic functions were formulated for an A∗ search based on place-timed PNs to schedule AMSs. To broaden its application scenarios and enhance search efficiency, this paper proposes a new heuristic function whose calculations take account of multiple resource acquisitions, weighted arcs, redundant resource units, and outdated resources, which are commonly encountered in practical AMSs but usually not considered. The proposed one can deal with generalized PNs, offering broader application scenarios than ordinary PNs. In addition, it is proven to be admissible and more informed than its counterparts, ensuring that the obtained schedules are optimal and making the timed PN-based A∗ search more efficient. To validate the efficacy and efficiency of the proposed method, several benchmark systems are tested.