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Increasing energy efficiency in manufacturing has significant environmental and cost benefits. Turning on or off a machine dynamically while considering the production rate requirements can offer substantial energy savings. In this work, we examine the optimal policies to control production and turn on and off a machine that operates in working, idle, off, and warmup modes for the case where demand inter-arrival, production, and warmup times have phase-type distributions. The optimal control problem that minimises the expected costs associated with the energy usage in different energy modes and the inventory and backlog costs is solved using a linear program associated with the underlying Markov Decision Process. We also present a matrix-geometric method to evaluate the steady-state performance of the system under a given threshold control policy. We show that when the inter-arrival time distribution is not exponential, the optimal control policy depends on both the current phase of the inter-arrival time and inventory position. The phase-dependent policy implemented by estimating the current phase based on the time elapsed since the last arrival yields a buffer- and time-based policy to control the energy mode and production. We show that policies that only use the inventory position information can be effective if the control parameters are chosen appropriately. However, the control policies that use both the inventory and time information further improve the performance. [ABSTRACT FROM AUTHOR]

The use of Additive Manufacturing (AM) technology has largely increased in the last years. Because of its large differences from conventional technologies, the use of AM in production systems might call for new strategies in production planning and control. To this aim, this paper proposes analytical models to predict aggregate performance measures such as flow time, work in process, and production throughput, for production systems characterised by Laser Powder Bed Fusion AM technology. These indicators could be used both in operations strategy development and in technology comparison. The proposed models differentiate for their detail of the analysis and the number of input parameters that need to be estimated. The results show that the level of detail of the model affects the analysis leading to quite different values of the performance measures, especially in the case of highly saturated systems. Also, a discussion about the applicability of the proposed model to other AM technologies show whether and to what extent the proposed models can be applied for modelling other AM technologies. [ABSTRACT FROM AUTHOR]

Hybrid manufacturing systems utilising raw materials and returned end-of-life products for production are studied. The systems are failure-prone and subject to inventory, market demand and return uncertainties. Thanks to growing environmental and sustainability concerns, the manufacturing sector is currently experiencing a significant growth in the popularity of reverse logistics. However, the practical implementation of production control in such systems is challenging due to return flow uncertainty and variability. To address this challenge, an estimation-based control using the Kalman filter is proposed in this research. The demand and return models employed contain random and deterministic components, with the latter being time-invariant for demand and uncertain with seasonal variations for return. The processing steps used include the estimation of inventory levels and demand and return components, return forecasting allowing cost computation over a long horizon, and the determination of the production and disposal policies adapting to market variations and uncertainties. We classify the systems according to relationships between their production capacity, demand and return ranges. We then present an extensive numerical study of optimal policies for various system classes and show that adaptive policies outperform the conventional ones, thus proving the effectiveness of the proposed production control approach for complex industrially oriented systems. [ABSTRACT FROM AUTHOR]

In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios. [ABSTRACT FROM AUTHOR]

For one-of-a-kind production (OKP) manufacturers adopting an engineer-to-order or make-to-order marketing strategy, it is very important to set an appropriate due date. We design an incentive-compatibility due-date quotation procedure to help OKP manufacturers obtain the rational due-date quotation from OKP customers. Based on the rational due-date quotation decision-making, the performance of an OKP shop floor controlled by load-based pull systems is evaluated in simulation. Our findings show that if the collaboration of due-date quotations and load-based pull control is effective, the order profit of OKP manufacturers will not be reduced when the delivery delay occurs. This study can provide more insights on order quotation decision-making and pull production control for OKP manufacturers. [ABSTRACT FROM AUTHOR]

The availability of cyber-physical systems (CPS) in modern manufacturing networks provides a multitude of interesting opportunities from a manufacturing control perspective. Providing sensors, data gathering, local computation and communication capabilities modern CPS fulfil the technical requirements to act completely autonomously in a manufacturing network. While the distribution of decision-making authority to autonomous entities is feasible given such requirements, practice often sees the monopolisation of decision-making authority for centralised control. However, distributed production control approaches might be better suited given current manufacturing challenges, ranging from unreliable supply chains over highly volatile markets, to the demand for increasingly efficient and highly customisable production. In this article, we extend an existing scheduling complexity framework which enables practitioners and researchers alike to assess the aptitude of given manufacturing networks for both centralised and distributed control. In particular, we study the influence of a manufacturing network's topology ranging from assembly line to job shops on the aforementioned aptitude, with total production costs as objective. We utilise a multi-agent-based discrete-event simulation comparing an MILP-based centralised control approach and an autonomy based distributed control approach with weighted costs as decision function to evaluate this framework. Our results provide novel insights regarding the influence of manufacturing network topologies on the scheduling complexity of manufacturing networks. [ABSTRACT FROM AUTHOR]

In this paper, we address a Stochastic-Demand Assembly Job Shop Scheduling Problem (SD-AJSSP) in the presence of the commonality of sub-assemblies across products. We propose a new production methodology, named Assemble-to-Order with Commonality of Sub-Assemblies (ATO-CS) to not only solve the SD-AJSSP, but also, achieve a successful implementation of a mass customisation system by collectively aiming to (1) keep the production costs low by leveraging upon commonality of sub-assemblies in products' BOM and producing sub-assemblies on a mass scale during one of the two stages of production, (2) minimise the loss due to excess inventory build-up in anticipation of stochastic demand of products by postponing the production of certain apex sub-assemblies in products' BOM until the actual demand is realised, and (3) reduce the time of the products' delivery to customers. The ATO-CS method determines optimum production levels as well as schedules assembly operations/jobs over the machines at each stage of production, where the second stage is an assembly job shop and is shown to outperform commonly-used production methodologies. We also develop an algorithm for its implementation and show its efficacy over the use of the state-of-the-art commercial solver CPLEX® in obtaining a lower solution cost and smaller optimality gap. [ABSTRACT FROM AUTHOR]

In order to regulate work in process (WIP) to the desired value in the job shop production control system, capacity adjustment as an effective and efficient measure, which is typically achieved by flexible staffs and working time. In this paper, instead of traditional labour-oriented approaches, we consider a machinery-based capacity adjustment via reconfigurable machine tools (RMTs) to compensate for unpredictable events. To this end, we employ model predictive control (MPC) in combination with genetic algorithm (GA) to explicitly consider complex reconfiguration strategies and address the related integer assignment optimisation problems. To further reduce energy consumption and avoid frequent and unnecessary reconfigurations while keeping a certain level of performance, we adopt an event-triggered MPC scheme with the proposed 'Double-layer event-triggering conditions'. Through extensively illustrated simulations, we demonstrate the effectiveness and plug-and-play availability of the proposed method for a six-workstation four-product job shop system and compare it to a state-of-the-art method. [ABSTRACT FROM AUTHOR]

Complex product and production systems often result in high variability in the production flow, prohibiting the sustainable implementation of lean practices. In this paper the authors introduce a PDCA cycle to analyse and reduce variability in value streams. The value stream is divided into zones, which are then qualified as stable or unstable. Lean practices can be applied in stable zones, unstable zones remain expert-driven. Measures are introduced to reduce variability in unstable zones with the ultimate target, to turn them into stable zones, extending sustainable lean activities in the value stream, step by step. An IT system is developed to acquire, process and visualise the vast amount of data to provide structured information for experts and management for the reduction of variability in production. [ABSTRACT FROM AUTHOR]

The objective of this paper is to examine the use and applications of reinforcement learning (RL) techniques in the production planning and control (PPC) field addressing the following PPC areas: facility resource planning, capacity planning, purchase and supply management, production scheduling and inventory management. The main RL characteristics, such as method, context, states, actions, reward and highlights, were analysed. The considered number of agents, applications and RL software tools, specifically, programming language, platforms, application programming interfaces and RL frameworks, among others, were identified, and 181 articles were sreviewed. The results showed that RL was applied mainly to production scheduling problems, followed by purchase and supply management. The most revised RL algorithms were model-free and single-agent and were applied to simplified PPC environments. Nevertheless, their results seem to be promising compared to traditional mathematical programming and heuristics/metaheuristics solution methods, and even more so when they incorporate uncertainty or non-linear properties. Finally, RL value-based approaches are the most widely used, specifically Q-learning and its variants and for deep RL, deep Q-networks. In recent years however, the most widely used approach has been the actor-critic method, such as the advantage actor critic, proximal policy optimisation, deep deterministic policy gradient and trust region policy optimisation. [ABSTRACT FROM AUTHOR]

In recent times, renewable energy sources have gained significant popularity because of the dwindling reserves of fossil fuels and the significant environmental damage resulting from their use. Among the various renewable energy sources, hydrogen stands out as one of the most crucial technologies for the future. Hydrogen can be produced through several different methods, and electrolysis is among the most frequently used methods. This method employs electrical energy to decompose water into its constituent hydrogen and oxygen molecules. When this electrical energy is derived from renewable sources such as solar or wind, then green hydrogen is produced. Systems that use electrolysis to produce hydrogen are known as electrolyzers. One of the primary challenges in the electrolysis process is ensuring the purity of hydrogen; proper control of the levels and pressures of the gas tanks can improve this purity. Automatic control strategies are very important for regulating the levels and pressures of the oxygen and hydrogen tanks. Researchers have proposed various classical techniques for controller design to mitigate this issue. This study seeks to offer a comprehensive and up-to-date review of existing research on control methods for alkaline electrolyzers, which has attracted considerable attention in recent times. The findings of this review offer valuable insight for future studies aimed at developing new control strategies to enhance hydrogen purity. • Describe main control strategies applied to alkaline electrolyzers. • Comparison of control strategies applied to electrolyzers and finding gaps in the literature. • Describe the importance of hydrogen purity and its production control methods. • Identify applications of alkaline electrolyzers according to the control strategy. • Importance of electrolyzers with renewable energy sources as an energy vector. [ABSTRACT FROM AUTHOR]

This paper addresses some of the new challenges posed by scheduling and controlling the production of chimeric antigen receptor (CAR) T cells for personalized therapies. In this therapy, the patient's T cells are genetically modified by adding a gene for a receptor to help T cells attach to the cancer cell antigens. Then, the modified cells are grown and multiplied (called cell expansion) and finally returned to the patient. The production takes place in a manufacturing system that comprises several devices, which are capable of processing cells from multiple patients at the same time. The efficient operation of such a complex system requires a sophisticated control system accompanied by a scheduler to optimize the production of CAR T cells for several patients simultaneously. We propose a scheduling algorithm, which can handle the uncertain duration of cell expansion, and the maximum time lags between consecutive processes simultaneously, a combination hardly studied in the literature. We also describe the main features of an adaptive control system including service-oriented device connections as well as centralized control and monitoring. Finally, we describe how the overall system works, including a JSON based communication between scheduler and control system. [ABSTRACT FROM AUTHOR]

Purpose: Several studies have addressed the use of four-dimensional (4D) building information modeling (BIM) for construction management. However, the automation of the processes for generating 4D models and their integrated use with Location-Based Planning and the Last Planner® System is not well discussed. Therefore, this paper aims to develop a method for automating the generation and use of 4D BIM models integrated with Location-Based Planning and Last Planner® System supporting project control cycles. Design/methodology/approach: The research strategy adopted was Design Science Research. The automated method for using the 4D models was developed and refined in two residential building projects in Brazil, along with 31 meetings and involving 11 direct users. The assessment of the proposed method focuses on four constructs: the impact of process automation, the impact on the identification and assessment of site progress and the planning process, ease of adoption and utility of the proposed method. Findings: The results of this paper indicated increased adherence between planned and executed through an automated method for using the 4D models. The established routines enabled automating the link between the planning levels and the three-dimensional (3D) model, providing a more agile and updated data source and achieving 92.8% of user satisfaction regarding the deadline and frequency of delivery of the 4D model reports. Moreover, this study identified the relationships between the processes of the method proposed and Digital Models. Originality/value: The primary scientific value achieved in this study is creating a method for automating processes and simplifying steps for the generation and use of 4D BIM models in the production planning and control cycles during the construction phase. [ABSTRACT FROM AUTHOR]

The Economic Production Quantity (EPQ) plays a crucial role in the manufacturing process. The traditional EPQ model operates under the assumption that Non-Conforming (NC) items are not produced, and the process remains under control. However, real-world scenarios often involve the production of NC products, leading to process deviations. This study introduces a model that optimizes the design of a control chart to incorporate NC products into the EPQ model. The model integrates inventory and quality-related costs to minimize the expected total cost by determining EPQ, sample size, and control limit. The solution methodology employs direct search techniques to solve this problem. The sensitivity analysis results demonstrate the significant cost reduction achieved by the proposed model compared to the classical EPQ model. The numerical examples presented in the study show an average cost reduction of approximately 21 percent, highlighting the potential effectiveness of the model in cost reduction efforts. [ABSTRACT FROM AUTHOR]

In the current manufacturing process of enterprises, there are some problems such as poor predictability and low level of intelligence, which lead to high product error rate and affect production efficiency. Therefore, this paper introduces the building information model in the field of engineering construction, and proposes a big data predictive manufacturing model based on the building information model, which divides the production process into production service system, resource planning system, production control system and after-sales service system, and realizes the overall process optimization of planning-production-sales on the basis of the close combination of each system and virtual model system. Finally, the application of error correction process in production line is verified from an empirical point of view, which provides a reference method and path for reducing production error rate and improving work efficiency. [ABSTRACT FROM AUTHOR]

In flexible manufacturing lines with delayed differentiation, the production process may fluctuate sharply when a control action is performed. As a result, the steady-state analysis algorithm is inaccurate for these production lines, and transient behavior studies have become crucial. However, dynamic analysis remains unexplored compared with the well-established theoretical system of steady-state analysis. Therefore, in this study, we propose a fast algorithm for predicting the production process performance in the delayed differentiation-based flexible production line under operation control. We first formulate practical problems existing in the auto, food, and furniture industries into a mathematical formation. Then, we offer closed-form formulae for predicting the production process performance using the built stochastic model in the production line with three machines. We also propose an algorithm to predict the performance of a production line having more than three machines. The proposed methods were verified to be highly accurate through comparison experiments. In terms of theoretical contributions, this study offers a research foundation for other transient-based studies. From a practical perspective, the proposed algorithms can be employed to predict the production process performance of processing lines under production control in advance. [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology & Material is the property of Automobile Technology & Material Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the advancement in global market integration, manufacturing enterprises face increasingly fierce competition, making the development of intelligent manufacturing systems a key factor in enhancing market competitiveness. However, manufacturing systems are characterized by varying characteristics of manufacturing resources and strong interdependencies, which make production control more complex. A bottleneck refers to the issue where the manufacturing system's actual production capacity is less than or equal to the demand placed on a resource. After extensive research, scholars have concluded that the definition, identification methods, and related studies on bottlenecks are not fixed but depend on the specific research subject and the type of bottleneck. Therefore, scholars suggest that only by accurately defining the bottlenecks within a system can appropriate models be employed for prediction, thereby avoiding issues such as inefficient resource allocation and delivery delays, or implementing measures to minimize these negative impacts. Particularly under unstable production conditions, dynamic bottlenecks are likely to occur, making the prediction and identification of bottlenecks under dynamic environmental conditions crucial. Currently, there is still a lack of research in real-time state sensing and integration, as well as a lack of systematic review of related research. To fill this research gap, this review comprehensively introduces the current state and achievements in bottleneck research within manufacturing systems, focusing on bottleneck categories, identification, shifting, and management. It also provides an outlook on future research trends and directions in the study of manufacturing system bottlenecks. [ABSTRACT FROM AUTHOR]

Copyright of Petroleum Geology & Recovery Efficiency is the property of Petroleum Geology & Recovery Efficiency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The subsea production system is a vital equipment for offshore oil and gas production. The control system is one of the most important parts of it. Collecting and processing the signals of subsea sensors is the only way to judge whether the subsea production control system is normal. However, subsea sensors degrade rapidly due to harsh working environments and long service time. This leads to frequent false alarm incidents. A combinatorial reasoning-based abnormal sensor recognition method for subsea production control system is proposed. A combinatorial algorithm is proposed to group sensors. The long short-term memory network (LSTM) is used to establish a single inference model. A counting-based judging method is proposed to identify abnormal sensors. Field data from an offshore platform in the South China Sea is used to demonstrate the effect of the proposed method. The results show that the proposed method can identify the abnormal sensors effectively. [ABSTRACT FROM AUTHOR]

This manuscript presents a model of a system implementing individual stages of production for long steel products resulting from rolling. The system encompasses the order registration stage, followed by production planning based on information about the billet inventory status, then offers the possibility of scheduling orders for the melt shop in the form of melt sequences, manages technological knowledge regarding the principles of sequencing, and utilizes machine learning and optimization methods in melt sequencing. Subsequently, production according to the implemented plan is monitored using IoT and vision tracking systems for ladle tracking. During monitoring, predictions of energy demand and energy consumption in LMS processes are made concurrently, as well as predictions of metal overheating at the CST station. The system includes production optimization at two levels: optimization of the heat sequence and at the production level through the prediction of heating time. Optimization models and machine learning tools, including mainly neural networks, are utilized. The system described includes key components: optimization models for sequencing heats using Ant Colony Optimization (ACO) algorithms and neural network-based prediction models for power-on time. The manuscript mainly focuses on process modeling issues rather than implementation or deployment details. Machine learning models have significantly improved process efficiency and quality; the optimization of planning has reduced sequencing plan execution time; and power-on time prediction models estimate the main ladle heating time with 97% precision, enabling precise production control and reducing overheating. The system serves as an example of implementing the concept of a cyber–physical system. [ABSTRACT FROM AUTHOR]

The relevance of assessing the applicability of intelligent wells using autonomous inflow control devices lies in the active development of the relevant sector of the oil and gas industry and the limited understanding of the economic efficiency of intelligent wells. The use of autonomous inflow control devices allows for a change in the composition of flow to the well, thus contributing to delaying the breakthrough of undesirable formation fluids, but at the same time, such an effect affects the dynamics of formation fluid production, which undoubtedly has a huge impact on the economic effect of the project. The analysis of scientific publications on the topic of "intelligent well completion" as a downhole production monitoring and remote production control system has shown that the vast majority of researchers pay attention to the evaluation of technological efficiency, ignoring the economic aspects of the proposed solutions. This study considered the dependence of the economic effect on the geological reservoir and technological well characteristics for variant 1—intelligent horizontal well (HW) completion using autonomous inflow control devices and variant 2—conventional horizontal well completion using the open hole. Calculations of production levels and dynamics in the two variants were performed on a created sector hydrodynamic model of a horizontal well operating in the depletion mode. The analysis of the obtained results allowed us to determine the applicability criteria of the proposed configuration of formation and well characteristics at the object of study, as well as to establish general dependencies of the net discounted income of an intelligent well. As a result of this study, it was determined that the economic efficiency of intelligent well completion with the use of autonomous inflow control devices relative to conventional well completion increases with decreasing permeability and drawdown pressure on the reservoir and reaches maximum values at the object of study at the thickness of the oil-saturated part of the reservoir about 5–6 m and the location of the wellbore in it at 35–40% of the thickness of the oil-saturated part below the gas–oil contact (GOC). This article covers the research gap in evaluating the economic efficiency of intelligent HW completion using AICD relative to conventional HW completion in oil rims. [ABSTRACT FROM AUTHOR]

The production control for the mass individualisation paradigm of R&D-stage products is challenging due to the mix-flow and frequently-disturbed environment. With the convergence of the sustainable development goals and the increasing individualised demands in products, resilient manufacturing is envisioned in Industry 5.0 proposition. Concerning that conventional centralised production control methods suffer from low stability and inefficiency of decisions under frequent disruptions, this paper establishes a blockchained smart contract pyramid-driven multi-agent autonomous process control (BSCP-MAAPC) approach for improving the timeliness and adaptability of control towards resilient individualised manufacturing. Firstly, a blockchain-based multi-agent system architecture is designed based on agent encapsulation of manufacturing units. Blockchained smart contracts are used as the enabler of the multi-agent system for peer-to-peer negotiation and coordination of tasks. Secondly, a quad-play blockchained smart contract pyramid together with a series of decentralised control patterns are designed to enable the initial task dispatching of various individualised demands, as well as rapid dynamic adjustment of schedule in response to internal random disruptions. Finally, a blockchained smart contract pyramid-driven multi-agent autonomous process control system prototype is built in the ManuChain system, and experiments are conducted to analyze the proposed BSCP-MAAPC approach in different environments. HIGHLIGHTS A blockchained smart contract pyramid-driven multi-agent autonomous process control (BSCP-MAAPC) approach. A quad-play blockchained smart contract pyramid together with a series of decentralized control patterns. Blockchained smart contracts as the enabler of the multi-agent system for peer-to-peer negotiation and coordination of tasks. [ABSTRACT FROM AUTHOR]

Injection molding is a pivotal industrial process renowned for its high production speed, efficiency, and automation. Controlling the motion speed of injection molding machines is a crucial factor that influences production processes, directly affecting product quality and efficiency. This paper aims to tackle the challenge of achieving optimal tracking control of injection speed in a standard class of injection molding machines (IMMs) characterized by nonlinear dynamics. To achieve this goal, we propose a learning-based model predictive control (LMPC) scheme that incorporates Gaussian process regression (GPR) to predict and model uncertainty in the injection molding process (IMP). Specifically, the scheme formulates a nonlinear tracking control problem for injection speed, utilizing a GPR-based learning residual model to capture uncertainty and provide accurate predictions. It learns the dynamics model and historical data of the IMM, automatically adjusting the injection speed according to target requirements for optimal production control. Additionally, the optimization problem is efficiently solved using a control-constrained differential dynamic programming approach. Finally, we conduct comprehensive numerical experiments to demonstrate the effectiveness and efficiency of the proposed LMPC scheme for controlling injection speed in IMP. [ABSTRACT FROM AUTHOR]

Traditional manufacturing workshop processes are challenging to change, and passive monitoring methods, such as quality traceability and completion rate monitoring, pose challenges for discrete workshops in achieving real-time and accurate production control. Therefore, combined with the digital twin system, a multi-level production status monitoring model is established to monitor discrete manufacturing workshops actively. This article focuses on discrete manufacturing workshops' modeling and self-evolution production processes. It also proposes an active monitoring architecture for production status in discrete workshops using digital twin technology. A full-information model is constructed for a discrete manufacturing workshop. Then, a multi-node interaction logic is established for the discrete manufacturing workshop. Finally, based on the fuzzy C-means clustering algorithm, production anomalies are monitored, and real-time monitoring of disturbances caused by various production factors, including material status, equipment status, and work-in-progress status, is performed to assess their impact on production. Based on deep Bayesian network learning algorithms, we can monitor production performance and analyze workshop capacity, equipment utilization, bottleneck rhythm, etc. This allows us to actively monitor production performance through the self-evolution of digital twin models. This article aims to proactively predict changes in workshop production status and provide support for further plan completion rate improvement, capacity prediction, and capacity optimization. [ABSTRACT FROM AUTHOR]

In order to achieve intelligent production and quality control, improve production efficiency and accuracy, the author proposes an application method of artificial intelligence technology in mechanical manufacturing and automation. The author aims to explore the specific application of artificial intelligence technology in the mechanical manufacturing industry and summarize the main advantages of automation technology. By analyzing these advantages, the aim is to provide targeted recommendations for future technological development. Meanwhile, the author aims to provide theoretical support for promoting the application of modern artificial intelligence technology and automation technology in the field of mechanical manufacturing. In the manufacturing process, intelligent production and quality control can be achieved, improving production efficiency and accuracy; In the field of automation, artificial intelligence technology can achieve intelligent control systems and autonomous decision-making, improving the flexibility and adaptability of production lines. Overall, the application of artificial intelligence technology has brought revolutionary changes to mechanical manufacturing, promoting the upgrading and development of the industry. [ABSTRACT FROM AUTHOR]

This paper outlines the main idea and approach of the H2020 ASSISTANT (LeArning and robuSt deciSIon SupporT systems for agile mANufacTuring environments) project. ASSISTANT is aimed at the investigation of AI-based tools for adaptive manufacturing environments, and focuses on the development of a set of digital twins for integration with, management of, and decision support for production planning and control. The ASSISTANT tools are based on the approach of extending generative design, an established methodology for product design, to a broader set of manufacturing decision making processes; and to make use of machine learning, optimisation, and simulation techniques to produce executable models capable of ethical reasoning and data-driven decision making for manufacturing systems. Combining human control and accountable AI, the ASSISTANT toolsets span a wide range of manufacturing processes and time scales, including process planning, production planning, scheduling, and real-time control. They are designed to be adaptable and applicable in a both general and specific manufacturing environments. [ABSTRACT FROM AUTHOR]

Sawmills are key elements of the forest product industry supply chain, and they play important economic, social, and environmental roles. Sawmill production planning and control are, however, challenging owing to several factors, including, but not limited to, the heterogeneity of the raw material. The emerging concept of digital twins introduced in the context of Industry 4.0 has generated high interest and has been studied in a variety of domains, including production planning and control. In this paper, we investigate the benefits digital twins would bring to the sawmill industry via a literature review on the wider subject of sawmill production planning and control. Opportunities facilitating their implementation, as well as ongoing challenges from both academic and industrial perspectives, are also studied. [ABSTRACT FROM AUTHOR]

In this paper, we study the influence of seasonal demands and forecasts on the performance of an Automatic Pipeline, Variable Inventory, Order-Based, Production Control System (APVIOBPCS) using linear control theory. In particular, we consider a system that uses a seasonal forecast based on a no-trend, additive-seasonality exponential-smoothing model, and compare its performance to an equivalent system using simple exponential smoothing. We find that the system with seasonal forecasting significantly outperforms the system with simple exponential smoothing under certain demand assumptions. With optimal parameter settings, the forecast error of the seasonal model can be up to 40% lower. However, we also find that the forecast superiority does not necessarily translate to the performance of the system measured through the bullwhip metrics. In addition, the seasonal forecasting model is very sensitive to the demand frequency and smoothing parameters, while the simple exponential smoothing model is very robust. This implies that the real life benefits of implementing a seasonal forecasting model are not obvious and depend on the particular situation; under a large number of settings (e.g. low seasonality, high-smoothing), the good performance of simple exponential smoothing certainly justifies its popularity in the industry and research worlds alike. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the impact in terms of Fill Rate of production control policies in a two-product, two-echelon supply chain dynamic problem with production capacity. The factory node is subject to two different disruptive occurrences (i.e. failure events and changeovers) that can cause unforeseen problems in the supply chain. To control these adverse events, the factory can adopt different production control policies. We compare the well-known Hedging Corridor Policy with two variants, namely Modified Hedging Corridor Policy and Improved Modified Hedging Corridor Policy, and Demand-Driven Material Requirements Planning policy. Firstly, we use the Response Surface Methodology to calibrate the endogenous factors for each strategy. Then, through an extended full-factorial Design Of Experiments, we evaluate the effectiveness of the production control policies for several operational and market scenarios defined by varying exogenous factors. Interestingly, our study reveals that the Hedging Corridor Policy represents the best rule to increase the Fill Rate. The policies have been compared also in terms of indicators related to the factory inventory level. The results seem to point out that efficient production control policies (measured in terms of operational efficiency) do not necessarily yield the best results when measured in terms of supply chain efficiency. [ABSTRACT FROM AUTHOR]

Many companies in practice want to dynamically adjust planned lead times in their production planning and control systems in response to demand fluctuations. But for decades it has been recognised that this can lead to escalating planned lead times and realised throughput times. Authors have highlighted the negative impact of this 'lead time syndrome', especially in the context of Material Requirements Planning systems, prompting the development of alternative concepts intended to overcome its vicious cycle, such as Workload Control. Yet some authors have shown that increasing planned lead times has advantages – it can improve end-item service levels. To resolve this paradox, we conjecture that the effects of the lead time syndrome are limited when demand is independent of internally planned lead times, such as in make-to-order companies, and subsequently use simulation to prove this conjecture. We show that although dynamic planned lead times have a detrimental effect on performance in make-to-order systems, it is not an increase in planned lead times that leads to a performance loss. Rather, it is the decrease of lead times in low load periods that increases workloads in upcoming periods of high load. This questions the use of upper bounds (WIP-cap) in these contexts. [ABSTRACT FROM AUTHOR]

Short-term and stochastic variations can create negative effects on the delivery capability, and companies often adopt their processes accordingly. What is then ignored is the fact that causes for variations are often self-induced and, therefore, possible to affect. Proposed actions in current literature focus mainly on continuous improvement without explicitly emphasizing that certain behaviours, rules and routines have a direct impact on variations affecting the company. In this study, a framework of causes for self-induced variations is established with proposed actions for each cause to reduce the variations. The framework is situated relative to practice for the actions applied at six manufacturing companies. This study sought to increase the understanding of self-induced variations and where companies can apply the proposed framework for an overview of 22 causes and 60 appropriate actions to support the systematic reduction of self-induced variations that can be internally or externally generated. [ABSTRACT FROM AUTHOR]

Acid resistance testing of concrete – development of a rapid testing method In many applications in agriculture, industry and waste water systems, concrete damage caused by acid attack is the decisive damage mechanism for durability. First, the cement paste on the concrete surface is decomposed by dissolving attack before a diffusion‐inhibiting covering layer forms between the surface and the attack zone. Hereafter, the damaging process progresses diffusion controlled and the damage zone advances into the concrete interior. Finally, progressive surface concrete erosion occurs which becomes decisive for the development of damage. In order to predict durability, it is essential to reliably record all three types of damage‐stages, including their interactions, as part of performance tests. However, this means that long test times of several months are inevitable, thus no meaningful chemical acid resistance tests for quality control are available. However, if the erosion behaviour for a specific concrete composition is known through performance tests, the test duration can be significantly reduced, e.g. for production control. This article presents such a suitable rapid test method for characterizing the acid resistance of concrete, which detects the early damage stages of acid attack quickly and cost‐effectively. The comparability and reproducibility of the test results are demonstrated and the application limits and possibilities are discussed. [ABSTRACT FROM AUTHOR]

To bridge the gap in the research on the control and drive methods of the key equipment of the new subsea production control system, all-electric subsea gate valve actuator, and to solve the problems of the valve control and drive system in the traditional subsea production system, this paper proposed a full-featured and feasible electrical system of the all-electric subsea gate valve actuator containing the control system and the drive system. The key functions were realized, including status monitoring, redundant communication, redundant power supply, redundant drive, and low-power open-position holding. The electrical system is suitable for monitoring and controlling all-electric subsea gate valve actuators with various specifications and is highly integrated, open, efficient, and real-time. The electrical system prototype was developed and successfully tested for several functions. The results showed that the all-electric subsea gate valve actuator electrical system was capable of controlling and driving the actuator, monitoring the status information of the internal and external environment of the system, as well as the power output information of the drive system, and having redundancy features. [ABSTRACT FROM AUTHOR]

Sand production is one of the challenges facing the oil industry. This paper reviews the latest research advances in chemical sand control and aims to provide a reference for related researchers. Firstly, the commonly used chemical sand control materials are introduced. Second, recent advances in chemical sand control are reviewed, including materials, methods, and processes. Third, laboratory methods for sand control research are presented. Finally, the gaps and challenges of chemical sand control materials are pointed out, and possible future directions for chemical sand control methods are envisioned. [ABSTRACT FROM AUTHOR]

This article shows the result of the research carried out on the El Jardín farm belonging to the Provincial Flora and Fauna Company, located at km 15 of the Palmar - La Tagua highway, in the Manuel Tames municipality of the province. from Guantánamo, where the bioproducts azotobacter, phosphorin, an efficient microorganism are used as alternatives for the production of bean crops, with the objective of evaluating the response of bean plants to the application of alternative products under conditions of La Tagua. A randomized block design was used, with four treatments and four replications. The variables of plant height, stem thickness, number of leaves, number of legumes per plant and number of seeds per legume were evaluated. On the different days of evaluations, the best plant responses were recorded in treatment four, where an efficient microorganism was used, achieving an increase in general, highlighting that the other variants evaluated were superior to the production control. Therefore, the application of bioproducts would be an alternative to increase the yields of bean crops. [ABSTRACT FROM AUTHOR]

The quality of Ti alloy casing is crucial for the safe and stable operation of aero engines. However, the fluctuation of key process parameters during the investment casting process of titanium alloy casings has a significant influence on the volume and number of porosity defects, and this influence cannot be effectively suppressed at present. Therefore, this paper proposes a strategy to control the influence of process parameters on shrinkage volume and number. This study constructed multiple regression prediction models and neural network prediction models of porosity volume and number for a ZTC4 casing by simulating the gravity investment casting process. The results show that the multiple regression prediction model and neural network prediction model of shrinkage cavity total volume have an accuracy of over 99%. The accuracy of the neural network prediction model is higher than that of the multiple regression model, and the neural network model realizes the accurate prediction of shrinkage defect volume and defect number through pouring temperature, pouring time, and mold shell temperature. The sensitivity degree of casing defects to key process parameters, from high to low, is as follows: pouring temperature, pouring time, and mold temperature. Further optimizing the key process parameter window reduces the influence of process parameter fluctuation on the volume and number of porosity defects in casing castings. This study provides a reference for actual production control process parameters to reduce shrinkage cavity and loose defects. [ABSTRACT FROM AUTHOR]

In the real-time scheduling (RTS) research field, it has been shown that employing multiple dispatching rules (MDRs) for the components in a flexible manufacturing system will improve production performance much more than a single dispatching rule (SDR). To fulfill the goal of Industry 4.0 in production control and improve production performance, this study deploys a deep reinforcement learning-based multi-agent (DRLBMA) approach for real-time scheduling. The proposed approach uses the MDRs strategy by integrating two main methodologies: an off-line learning module and a Deep Q-learning-based multi-agent module. The proposed method employs a two-level self-organizing map (SOM) to determine the system's states. The proposed methodology determines the best MDRs decision. The approach is applied to a case study of a smart manufacturing system. The results of the proposed method are compared to different scheduling strategies, such as reinforcement learning (RL)-based real-time scheduling, two-level self-organized map (SOM), and continuous rescheduling using a single dispatching rule, such as earliest due date (EDD), shortest processing time (SPT), and longest processing time (LPT). The findings reveal that, in terms of the total weighted tardiness, throughput, and mean cycle time performance criteria, the proposed DRLBMA-based real-time scheduling is more efficient than these scheduling strategies. [ABSTRACT FROM AUTHOR]

Intelligent manufacturing technology is required to upgrade existing enterprises' management and production operations. To construct a ground breaking fusion structure, this project unites the theoretical underpinnings, technical breakthroughs, and applications of data analytics, optimisation, and intelligent production engineering. It is driven by China's desire of cutting-edge commodities and efficient growth methods. This research establishes the broad framework for merging optimisation and data analytics. There is a list of data analytics and system optimisation technologies that can address important challenges with intelligent manufacturing. By integrating data analytics and optimisation, businesses may better forecasting and management of new terrain, as well as reveal hidden information to increase decision-making efficacy. [ABSTRACT FROM AUTHOR]

The study offers an analytical approach for assessing production control system performance in terms of volume and variety features of the product being manufactured for the flexible manufacturing system. Throughput, resource utilization, cycle time, and maximum completion time are the four performance indicators taken into account. The objective was to quantify the performance of the production system and define the reactive capability of production control architectures in a market that is becoming increasingly competitive. The examination of the production performances of control systems motivates the evaluation of these architectures through the introduction of scheduling approaches that deal with uncertainty. Results revealed the semi-heterarchical control architecture outperforms the hierarchical control architecture through multiple performance criteria. A case study with regard to a manufacturing control system was presented in order to highlight the significance of the adopted methodology and the contribution of the research. [ABSTRACT FROM AUTHOR]

Problem definition: Production systems deteriorate stochastically due to use and may eventually break down, resulting in high maintenance costs at scheduled maintenance moments. This deterioration behavior is affected by the system's production rate. Although producing at a higher rate generates more revenue, the system may also deteriorate faster. Production should thus be controlled dynamically to tradeoff deterioration and revenue accumulation in between maintenance moments. We study systems for which the relation between production and deterioration is known and the same for each system and systems for which this relation differs from system to system and needs to be learned on-the-fly. The decision problem is to find the optimal production policy given planned maintenance moments (operational) and the optimal interval length between such maintenance moments (tactical). Methodology/results: For systems with a known production-deterioration relation, we cast the operational decision problem as a continuous time Markov decision process and prove that the optimal policy has intuitive monotonic properties. We also present sufficient conditions for the optimality of bang-bang policies, and we partially characterize the structure of the optimal interval length, thereby enabling efficient joint optimization of the operational and tactical decision problem. For systems that exhibit variability in their production-deterioration relations, we propose a Bayesian procedure to learn the unknown deterioration rate under any production policy. Numerical studies indicate that on average across a wide range of settings (i) condition-based production increases profits by 50% compared with static production, (ii) integrating condition-based production and maintenance decisions increases profits by 21% compared with the state-of-the-art sequential approach, and (iii) our Bayesian approach performs close, especially in the bang-bang regime, to an Oracle policy that knows each system's production-deterioration relation. Managerial implications: Production should be adjusted dynamically based on real-time condition monitoring and the tactical maintenance planning should anticipate and integrate these operational decisions. Our proposed framework assists managers to do so optimally. Funding: This work was supported by the Nederlandse Organisatie voor Wetenschappelijk Onderzoek [Grant 439.17.708]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0473. [ABSTRACT FROM AUTHOR]

The application of new generation information technology in ITMA ASIA+CITME 2022 was discussed. From aspects of storage logistics digital twin, precision management of raw cotton, single spindle monitoring, remote equipment operation and maintenance, application scenarios and solutions of new generation information technology were reviewed. It was pointed out that the research and development direction of textile machinery enterprises mainly focused on the digitalization, automation, intelligent production and quality control of high-efficiency, energy-saving and high value-added textiles. The new generation information technology had made the digital upgrading of the industry faster and more efficient, the overall level of intelligence in textile production was significantly improved. The new ecology of textile industry based on industrial Internet was accelerating. It is considered the integration and application of new generation information technology with the textile industry has promoted collaborative innovation in product research and development, manufacturing and service, which helping enterprises achieve strategic goals of improving quality, efficiency and high-quality development. [ABSTRACT FROM AUTHOR]

Given the fact that finding the optimal sequence in a flexible flow shop is usually an NP-hard problem, priority-based sequencing rules are applied in many real-world scenarios. In this contribution, an innovative reinforcement learning approach is used as a hyper-heuristic to dynamically adjust the k-values of the ATCS sequencing rule in a complex manufacturing scenario. For different product mixes as well as different utilisation levels, the reinforcement learning approach is trained and compared to the k-values found with an extensive simulation study. This contribution presents a human comprehensible hyper-heuristic, which is able to adjust the k-values to internal and external stimuli and can reduce the mean tardiness up to 5%. [ABSTRACT FROM AUTHOR]

Efficient performance evaluation methods are needed to design and control production systems. We propose a method to analyse single-server open queueing network models of manufacturing systems composed of delay, batching, merge and split blocks with correlated interarrival and service times. Our method (SLQNA) is based on using a supervised learning approach to determine the mean, the coefficient of variation, and the first-lag autocorrelation of the inter-departure time process as functions of the mean, coefficient of variation and first-lag autocorrelations of the interarrival and service times for each block, and then using the predicted inter-departure time process as the input to the next block in the network. The training data for the supervised learning algorithm is obtained by simulating the systems for a wide range of parameters. Gaussian Process Regression is used as a supervised learning algorithm. The algorithm is trained once for each block. SLQNA does not require generating additional training data for each unique network. The results are compared with simulation and also with the approximations that are based on Markov Arrival Process modelling, robust queueing, and G/G/1 approximations. Our results show that SLQNA is flexible, computationally efficient, and significantly more accurate and faster compared to the other methods. [ABSTRACT FROM AUTHOR]