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To optimise production planning, maintenance strategies, and quality control in imperfect manufacturing systems, most existing works set the buffer stocking time at the cycle start time. However, excess inventory holding costs will be incurred if the buffer is stocked too early. This means that while the abovementioned setting can make the optimisation model simple, it might increase costs. Taking the uncertain practical buffer stocking time of an imperfect manufacturing system into account, this study aimed to find the production cycle, maintenance frequency, quality inspection cycle, and number of inspections that will minimise the expectation unit cost of the system. To this end, we developed a new model to optimise production, maintenance, and quality control considering timely replenishment. First, excess inventory holding and shortage costs were considered and the production process was divided into five scenarios based on the buffer stocking time and inspection time for assignable causes. Second, the link between production, maintenance, and quality was addressed by capturing the dynamic and random behaviour of production systems. An x-bar control chart was integrated into the model to monitor quality. A case study and sensitivity analysis were undertaken to verify the effectiveness and superiority of the proposed optimisation strategy. [ABSTRACT FROM AUTHOR]

This paper studies a multi-period dynamic production planning problem in a hybrid manufacturing and remanufacturing system (HMRS), where new and remanufactured products are perfect substitutes. The HMRS encounters uncertain return amounts due to products' random usage durations. As returns are from previous sales, production planning in each period impacts both current sales and future returns. The random return amount and the correlation among different periods make it a complex system. To solve the problem, we first utilise the hazard rate function and in-use products' information to derive an estimator of the return amount. Then, we formulate a dynamic programming model and prove a threshold policy is optimal under uniformly distributed demand. We employ marginal analysis to derive an approximation of the optimal threshold value. Through simulating all alternatives, the derived threshold is verified to be a good approximation as it achieves more than 99% of the optimal revenue in most scenarios. In addition, the calculated return amount based on the hazard rate function is almost identical to the return amount obtained via simulation. Compared to other return measurements, our method achieves the highest revenue in all considered scenarios, including heterogeneous usage durations and general demand distributions. [ABSTRACT FROM AUTHOR]

Order release optimisation is essential in production planning, especially in discrete manufacturing. Order release planning models with load-dependent lead times must anticipate the time-dependent work-in-process and output for any given release schedule and thus require an anticipation model that approximates the time-dependent behaviour of queueing systems. We present a generic optimisation model for order release planning in stochastic, non-stationary manufacturing systems that includes a well-defined interface for the anticipation model. We develop two stationary backlog carryover (SBC) approaches to approximate time-dependent queueing behaviour and prove their consistency with the order release model. The resulting nonlinear programming model is shown to be a special case of the well-known clearing function models. A numerical study demonstrates that the optimised order releases for different demand patterns are close to the optimum that results from simulation-based optimisation even for extreme demand and release patterns. The resulting output closely matches the simulated output with some deviations. [ABSTRACT FROM AUTHOR]

This paper delves into the impacts of an ongoing global crisis on the resilience of supply chains. Furthermore, it proposes measures to address and mitigate the disruptions caused by the prevailing uncertainties. For example, while the economy has started to recover after the pandemic and demand has increased, companies have not fully returned to their pre-pandemic levels. To enhance their supply chain resilience and effectively manage disruptions, one viable strategy is the implementation of flexible/hybrid manufacturing systems. This research is motivated by the specific requirements of Vestel Electronics, a household appliances company, which seeks a flexible/hybrid manufacturing production setup involving dedicated machinery to meet regular demand and the utilisation of flexible manufacturing system (FMS) to handle surges in demand. We employ a scenario-based approach to model demand uncertainty, enabling the company to make immediate and adaptive decisions that take advantage of the cost-effectiveness of standard production and the responsiveness of FMS. To solve the problem, we propose a heuristic algorithm based on column generation. The numerical results demonstrate that our optimisation model provides solutions with an average optimality gap of less than 6% while also reducing the average cost of standard production schemes without FMS by over 12%. [ABSTRACT FROM AUTHOR]

Manufacturing companies strive to identify and manage the effects of unexpected disruptions (risks) on their production processes, which affect their performance and resilience. In this study, we propose a decision framework to capture the impact of interconnected risk sources, on the efficiency of manufacturing companies. The proposed framework utilises a novel mixed-integer linear programming (MILP) model to minimize the time of satisfying the orders while it considers the risk associated with suppliers and manufacturers. The MILP model considers the relationships among (i) material and suppliers and (ii) work centers to measure the propagation of risks throughout the production system. The proposed framework also utilises the Monte Carlo simulation to calculate the associated likelihood of delay and the distribution of the delivery time of orders. To show the complication of the propagation of risk, two distinct scenarios are compared. The first scenario considers zero risks, while the second one assigns probabilistic risk to the suppliers and work centers. The results highlight the magnitude and the complexity of the risk propagation from various interconnected sources through the production system. It also identifies the most vulnerable components of the production system affected more by various types of risk. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) introduces a set of technology-specific problems, such as the proper orientation of parts or the placement of several heterogeneous parts in the same build cycle, which are not addressed by traditional approaches to production planning and scheduling. Although these new production subproblems have been implicitly addressed by several works according to generic nesting and scheduling concepts, a literature review revealed that there is no uniformity in identifying and, thus, solving all these subproblems. For this reason, and as a result of an in-depth analysis of the existent literature on AM production planning and an analogy with classic cutting and packing typologies, the present paper offers a framework to formalise the production planning problem in AM at the operational level. This framework can be used as a reference to focus on and address these AM-related problems for efficient production planning. It is designed at the subproblem level and centres on production order processing in AM. A coding strategy is specifically developed for the framework, which is applied to a review of relevant works that propose models for the production planning of AM systems. Finally, the review results are discussed and possible extensions of the framework are proposed. [ABSTRACT FROM AUTHOR]

The Industry 4.0 revolution is changing the manufacturing landscape. A broad set of new technologies emerged (including software and connected equipment) that digitise manufacturing systems. These technologies bring new vitality and opportunities to the manufacturing industry, but they also bring new challenges. This paper focuses on the impact of Industry 4.0 on production planning approaches and software. We first propose a digital twin framework that integrates production planning systems and frontier technologies. The frontier technologies that may impact production planning software are the internet of things, cloud manufacturing, blockchain, and big data analytics. Second, we provide a state-of-the-art on the application of each technology in the production planning, as well as a detailed analysis of the benefit and application status. Finally, this paper discusses the future research and application directions in the production planning. We conclude that Industry 4.0 will lead to the construction of data-driven models for production planning software. These tools will include models built accurately from data, account for uncertainty, and partially actuate the decision autonomously. [ABSTRACT FROM AUTHOR]

Optimization of the machines allows you to increase their productivity and reduce the labor costs for performing individual elements of the technological cycle of their work in the forest. In this regard, the authors offer theoretical studies that take into account the peculiarities of the work of one of the most popular logging machines today, namely, a machine for collecting and forwarding logs (forwarder). The article discusses the technological features of the functioning of these machines, identifies ways to reduce the cost of collecting logs, justifies the rational width of apiaries, which allows collecting logs with a minimum number of movements between work positions. The main purpose of the study is to substantiate the mathematical dependence for determining the rational values of such interrelated parameters of the forwarder's operation as the distance between the working positions of collecting logs and the width of apiaries. The authors propose an analytical approach to solving the problem. On the basis of the standard technological scheme of the harvester's movement through the swath, geometric regularities of the trajectory of the manipulator movement, dimensional characteristics and location of the prepared logs are revealed. As a result of the work, mathematical dependencies are constructed that make it possible to determine the rational values of the desired parameters. The results can be used by research organizations in planning the production process of logging operations. The introduction of author's recommendations into production will contribute to improving the efficiency and operation of forestry equipment. [ABSTRACT FROM AUTHOR]

This review paper focuses on the application of neural networks in semiconductor packaging, particularly examining how the Back Propagation Neural Network (BPNN) model predicts the work-in-process (WIP) arrival rates at various stages of semiconductor packaging processes. Our study demonstrates that BPNN models effectively forecast WIP quantities at each processing step, aiding production planners in optimizing machine allocation and thus reducing product manufacturing cycles. This paper further explores the potential applications of neural networks in enhancing production efficiency, forecasting capabilities, and process optimization within the semiconductor industry. We discuss the integration of real-time data from manufacturing systems with neural network models to enable more accurate and dynamic production planning. Looking ahead, this paper outlines prospective advancements in neural network applications for semiconductor packaging, emphasizing their role in addressing the challenges of rapidly changing market demands and technological innovations. This review not only underscores the practical implementations of neural networks but also highlights future directions for leveraging these technologies to maintain competitiveness in the fast-evolving semiconductor industry. [ABSTRACT FROM AUTHOR]

Planning and designing factory layouts are frequently performed within virtual environments, relying on inputs from various cross-disciplinary activities e.g., product development, manufacturing process planning, resource descriptions, ergonomics, and safety. The success of this process heavily relies on the expertise of the practitioners performing the task. Studies have shown that layout planning often hinges on the practitioners' knowledge and interpretation of current rules and requirements. As there is significant variability in this knowledge and interpretation, there is a risk that decisions are made on incorrect grounds. Consequently, the layout planning process depends on individual proficiency. In alignment with Industry 4.0 and Industry 5.0 principles, there is a growing emphasis on providing practitioners involved in industrial development processes with efficient decision support tools. This paper presents a digital support function integrated into a virtual layout planning tool, developed to support practitioners in considering current rules and requirements for space claims in the layout planning process. This digital support function was evaluated by industry practitioners and stakeholders involved in the factory layout planning process. This initiative forms part of a broader strategy to provide advanced digital support to layout planners, enhancing objectivity and efficiency in the layout planning process while bridging cross-disciplinary gaps. [ABSTRACT FROM AUTHOR]

In many industries, companies receive customer orders that include multiple products. To simplify the use of optimization models for planning purposes, these orders are broken down, and the quantities of each product are grouped with the same products from other orders to be completed in the same period. Consequently, traditional production planning models enforce minimum demand constraints by product and period rather than by individual orders. An important drawback of this aggregation procedure is that it requires a fixed order fulfillment period, potentially missing opportunities for more efficient resource use through early completion. This paper introduces a novel mathematical formulation that preserves the integrity of customer orders, allowing for early fulfillment when possible. We compare a traditional linear programming model with a new mixed-integer programming approach using a sawmill case study. Although more complex than the traditional model, the proposed formulation reduces costs by approximately 6% by enabling early order completion and offers greater flexibility and control over the production process. This approach leads to better resource utilization and more precise order management, presenting a valuable alternative to conventional production planning models. [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.)

Artificial intelligence (AI) has found applications in enterprises′ production planning processes. However, a critical question remains: could AI replace human planners? We conducted a comparative analysis to evaluate the main task of planners in an intermittent process: planning the duration of production orders. Specifically, we analysed the results of a human planner using master data and those of an AI algorithm compared to the actual realisation. The case study was conducted in a large production company using a sample of production products and machines. We were able to confirm two of the three research questions (RQ1 and RQ3), while the results of the third question (RQ2) did not meet our expectations. The AI algorithms demonstrated significant improvement with each iteration. Despite this progress, it is still difficult to determine the exact threshold at which AI outperforms human planners due to the unpredictability of unexpected events. Even though AI significantly improves prediction accuracy, the inherent variability and incomplete input data pose a major challenge. As progress is made, robust data collection and management strategies need to be integrated to bridge the gap between the potential of AI and its practical application, fostering the symbiosis between human expertise and AI capabilities in production planning. [ABSTRACT FROM AUTHOR]

In the vision of smart manufacturing and Industry 4.0, it is vital to automate production processes. There is a significant gap in current practices, where the derivation of production processes from product data still heavily relies on human expertise, leading to inefficiencies and a shortage of skilled labor. This paper proposes a universal framework for skill-based cyber–physical production systems (CPPS) that formalizes production knowledge into machine-processable formats. Key contributions include a novel conceptual model for skill-based production processes and an automated method to derive production plans from high-level CPPS skills for production planning and execution. This framework aims to enhance smart manufacturing by enabling more efficient, transparent, and automated production planning, thereby addressing the critical gap in current manufacturing practices. The framework's benefits include making production processes explainable, optimizing multi-criteria systems, and eliminating human biases in process selection. A case study illustrates the framework's application, demonstrating its current capabilities and potential for modern manufacturing. [ABSTRACT FROM AUTHOR]

Copyright of Quintessenz der Zahntechnik is the property of Quintessenz Verlags GmbH 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.)

In this paper, a practical relevant operational production planning problem in complex manufacturing systems is addressed. In this problem, lots are planned individually to provide a more detailed plan than approaches that only consider production quantities. A three-step approach, which is currently fully integrated and used in a Decision Support System, is then introduced. This work follows the one of Mhiri et al. [2018. "Heuristic Algorithm for a WIP Projection Problem at Finite Capacity in Semiconductor Manufacturing." IEEE Transactions on Semiconductor Manufacturing 31 (1): 62–75] who addressed this problem. We push the approach a step further by introducing new optimisation possibilities through new smoothing rules, whose performance is studied according to different indicators. Furthermore, we present the production planning process in which the decision support tool is embedded and how it bridges the gap between the upper and lower planning levels. [ABSTRACT FROM AUTHOR]

In Industry 4.0, the production planning and execution of smart factories (SFs) full of continuously delivered small-batch orders become dynamic and complicated. Traditional centralised manufacture planning is difficult to handle unexpected disturbances. With the aid of new information technologies, resources in SFs become smart and connected to make autonomous decisions. This paper tries to release intelligence of smart connected resources to allocate production tasks and logistics tasks in SFs coordinately and autonomously. The architecture is modelled as an autonomous decision-making manufacturing system with IIoT support, which aims to synchronously allocate manufacturing tasks by the bidding of resources in SFs. Then, a dynamic production-logistics-integrated tasks allocation model is built. The orders makespan and resources utilisation are considered as the objective function, and production resources and logistics resources are integrated to autonomously communicate and interact with each other to bid for dynamic production-logistics integrated operations. To figure out, a reinforcement learning (RL) algorithm is studied, which makes operations decisions for each job step by step based on in-situ data during manufacturing process. Finally, a demonstrative case showed that compared to centralised scheduling system, the RL-based model performs better in handling production-logistics-integrated tasks allocation problem in SFs full of dynamic and small-batch individualised orders. [ABSTRACT FROM AUTHOR]

Flexible process planning (FPP) involves selecting and sequencing the requisite operations according to technological requirements, and meanwhile allocating a right machine, a right tool and a right access direction to each selected operation by a given criterion. In this article, the FPP problem is exactly and concisely formulated as linear integer programming models based on the topology of the AND/OR-network under two criteria: production cost minimisation and completion time minimisation. Distinctively, more flexible manufacturing elements and process plan evaluation criteria are considered; more complicated tool and access direction changeover identifications are linearly expressed without the big-M parameter. Compared with the latest mathematical programming models for process planning, the proposed models have lower complexity and better performance. The results from numerous comparative experiments indicate that (i) the number of decision variables of the proposed models reduces approximately by 68% and the number of constraints of the proposed models dramatically reduces by 99%; (ii) within the same running time, the proposed models can exactly solve more benchmark cases than the latest models; and (iii) the solutions obtained by the proposed models are also better than the best ones founded by some state-of-the-art meta-heuristic algorithms. [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]

Background: The aim of this paper is to develop a maturity model (MM) for demand and capacity management (DCM) processes in healthcare settings, which yields opportunities for organisations to diagnose their planning and production processes, identify gaps in their operations and evaluate improvements. Methods: Informed by existing DCM maturity frameworks, qualitative research methods were used to develop the MM, including major adaptations and additions in the healthcare context. The development phases for maturity assessment models proposed by de Bruin et al. were used as a structure for the research procedure: (1) determination of scope, (2) design of a conceptual MM, (3) adjustments and population of the MM to the specific context and (4) test of construct and validity. An embedded single-case study was conducted for the latter two - four units divided into two hospitals with specialised outpatient care introducing a structured DCM work process. Data was collected through interviews, observations, field notes and document studies. Thematic analyses were carried out using a systematic combination of deductive and inductive analyses - an abductive approach - with the MM progressing with incremental modifications. Results: We propose a five-stage MM with six categories for assessing healthcare DCM determined in relation to patient flows (vertical alignment) and organisational levels (horizontal alignment). Our application of this model to our specific case indicates its usefulness in evaluating DCM maturity. Specifically, it reveals that transitioning from service activities to a holistic focus on patient flows during the planning process is necessary to progress to more advanced stages. Conclusion: In this paper, a model for assessing healthcare DCM and for creating roadmaps for improvements towards more mature levels has been developed and tested. To refine and finalise the model, we propose further evaluations of its usefulness and validity by including more contextual differences in patient demand and supply prerequisites. [ABSTRACT FROM AUTHOR]

Minimizing job waiting time for completing related operations is a critical objective in industries such as chemical and food production, where efficient planning and production scheduling are paramount. Addressing the complex nature of flow shop scheduling problems, which pose significant challenges in the manufacturing process due to the vast solution space, this research employs a novel multiobjective genetic algorithm called distance from ideal point in genetic algorithm (DIPGA) to identify Pareto-optimal solutions. The effectiveness of the proposed algorithm is benchmarked against other powerful methods, namely, NSGA, MOGA, NSGA-II, WBGA, PAES, GWO, PSO, and ACO, using analysis of variance (ANOVA). The results demonstrate that the new approach significantly improves decision-making by evaluating a broader range of solutions, offering faster convergence and higher efficiency for large-scale scheduling problems with numerous jobs. This innovative method provides a comprehensive listing of Pareto-optimal solutions for minimizing makespan and total waiting time, showcasing its superiority in addressing highly complex problems. [ABSTRACT FROM AUTHOR]

The milling process has proven to be a versatile process for the manufacturing of complex components at the macro- and micro-scales. Development of the cutting force prediction model in milling is important for process planning and optimization, as well as the controlling of machining accuracy. The well-established milling force prediction models include analytical, empirical, and numerical models, among which the analytical models are the most useful for characterizing the milling process and enhancing the understanding of the mechanics of the milling process. This paper makes a detailed review on the analytical models for the force predictions in macro- and micro-milling processes by analyzing the calculation modeling of the instantaneous uncut chip thickness (IUCT) and the determination methods of milling force coefficients. The development of the IUCT model starts from the studying of the effects of circular and trochoidal tool tip locus, later tool and workpiece deflections, tool runout, and tool wear, then the effects of cutting tool edge radius and workpiece material properties in the micro-milling process. The methods for cutting force coefficients are summarized and divided into three subgroups: the first is in constant form and obtained from the experiment, the second is expressed as the functions of the depth of cut, while the third is represented as the polynomial form under different influencing factors including machining conditions, tool geometries, and IUCT effect. The modeling laws and the key challenges for milling forces are also discussed for future research. [ABSTRACT FROM AUTHOR]

Ontology plays a significant role in AI (Artificial Intelligence) and robotics by providing structured data, reasoning, action understanding, context awareness, knowledge transfer, and semantic learning. The structured framework created by the ontology for knowledge representation is crucial for enabling intelligent behavior in robots. This paper provides a state-of-the-art analysis on the existing ontology approaches and at the same time consolidates the terms in the robotic task planning domain. The major gap identified in the literature is the need to bridge higher-level robotic process management and lower-level robotic control. This gap makes it difficult for operators/non-robotic experts to integrate robots into their production processes as well as evaluate key performance indicators (KPI) of the processes. To fill the gap, the authors propose an ontology for skill-based robotics process planning (ORPP). ORPP not only provides a standardization in the robotic process planning in the agile manufacturing domain but also enables non-robotic experts to design and plan their production processes using an intuitive Process-Task-Skill-Primitive structure to control low-level robotic actions. On the performance level, this structure provides traceability of the KPIs down to the robot control level. [ABSTRACT FROM AUTHOR]

The steelmaking–continuous casting process (SCCP) is a complex manufacturing process which exhibits the distinct features of process manufacturing. The SCCP involves a variety of production elements, such as multiple process routes, a wide array of smelting and auxiliary devices, and a variety of raw and auxiliary materials. The production-simulation of SCCP holds a natural advantage in being able to accurately depict the intricate production behavior involved, and this serves as a crucial tool for optimizing the production operation of the SCCP. This paper thoroughly considers the various production elements involved in the SCCP, such as the fluctuation of the converter smelting cycle, fluctuation of heat weight, and ladle operation. Based on the Plant Simulation software platform, a dynamic simulation model of the SCCP is established and detailed descriptions are provided regarding the design of an SCCP using dynamic-operation rules. Additionally, a dynamic operational control program for the SCCP is developed using the SimTalk language, one which ensures the continuous operation of the caster in the SCCP, using the discrete simulation platform. The effectiveness of the proposed dynamic simulation model is verified by the total completion time of the production plan, the transfer time of the heat among the different processes, and the frequency of ladle turnover. The simulation's results indicate that the dynamic simulation model has a satisfactory effect in simulating the actual production process. On this basis, the application effects of different schedules are compared and analyzed. Compared with a heuristic schedule, the optimized schedule based on the "furnace–machine coordinating" mode reduces the weighted value of total completion time by 8.7 min, reduces the weighted value of transfer waiting time by 45.5 min, and the number of rescheduling times is also reduced, demonstrating a better application effect and verifying the optimizing effect of the "furnace–machine coordinating" mode on the schedule. [ABSTRACT FROM AUTHOR]

In manufacturing and service systems, the negative effects of variability propagation are usually addressed through approximation models and other tools, such as simulation and optimization algorithms. This paper investigates the conditions in which these approaches are ineffective, and considering only the mean and variance of the processing time distribution is misleading. A scenario analysis deepens the impacts of considering the entire processing time distribution (beyond its mean and variance) on the inter-departure times in balanced and unpaced lines modeled through Discrete Event Simulation. The results correlate the impacts on variability propagation of approximating the processing time distribution with system characteristics such as utilization levels, line sizes, and inter-arrival and processing time variability. Production planning approaches can benefit from these results to reduce variability propagation, particularly in flexible and reconfigurable manufacturing systems, largely adopted in Industry 4.0, that can highly influence processing time distributions by varying product mix and line configuration. A balanced and unpaced line, with a single inter-arrival time distribution, and two processing time distributions, alpha and beta, rispectively, which have the same Squared Coefficient of Variation and different shape that lead to different variability propagation in the inter-departure time distribution at the end of the line. [ABSTRACT FROM AUTHOR]

Copyright of Automatisierungstechnik is the property of De Gruyter 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.)

In the context of the growing demand for alternative protein sources with the growth of the human population and increasing ecological awareness, the rearing of yellow mealworm larvae (Tenebrio molitor) is a promising option for the production of sustainable protein. The article presents a comprehensive approach to planning and scheduling the production of yellow mealworm larvae in a small enterprise, focusing on the organizational, technical, and economic aspects of the production process. The production installation, the method of rearing using an automated feeding system, and the monitoring of larvae development were described and an attempt was made to identify the key parameters of the process that affect its efficiency. Particular attention was paid to the calculation algorithm implemented in the spreadsheet, which allows the selection of the production batch size and the frequency of their launch, so as to maximize the available capacity of storage racks for cuvettes. In addition, the article analyses logistical challenges related to the production of larvae, including transport activities in order to meet, among others, the demand for feed. Finally, the estimation of revenues and economic indicators, such as profitability and return on investment, is presented, pointing to the need for further improvements in the production process and cost optimization to achieve favorable financial results. The results of the research emphasize the potential of rearing yellow mealworm larvae as a sustainable source of protein while simultaneously pointing to key areas that require further research and development. [ABSTRACT FROM AUTHOR]

One of the goals of Industry 4.0 is to increase the transparency of the value chain through modern tools in production processes. This article aims to discuss the possibility of increasing the efficiency of a production system by modernizing it with the use of computer modelling tools. This article describes a method for the simulation modelling of a selected production system using the specialized FlexSim 2023 software in a 3D environment. The results and benefits of the practical application of the object-oriented modelling are presented, as well as the possibilities of collecting simulation data used to optimize production processes. The analyses were conducted at a selected production plant in a case study. The research assessed the effectiveness of the existing system and determined the impact of process changes in the event of the introduction of a new design solution. The simulation identified bottlenecks in the material flow. The basis for creating the simulation model was the analysis of the technological process. A simulation model for a real situation was created, and a simulation model was designed to identify and indicate a solution to eliminate the detection of the bottleneck. The problem area identified using visualization in the technological process slowed down the entire production process and contributed to time and economic losses. Thus, the authors confirmed the thesis that the simulation modelling of production systems using the FlexSim program can help eliminate bottlenecks and increase the efficiency of human resource use. At the same time, the use of this tool can lead to increased efficiency, reduced costs and improved sustainability and other performance indicators important for modern production environments as part of the promoted Industry 4.0 idea. A noticeable result of these changes was an increase in production from about 80–90 units. In addition, it was noticed that the condition of the machines preceding the stand changed. [ABSTRACT FROM AUTHOR]

This paper proposes a Machine Learning (ML) approach to perform an Ahead-of-Time (AoT) prediction of decorated particle-board production disruptions and defects. We worked with a Portuguese company that is adopting the Industry 4.0 concept aiming to improve their decorated particleboard production planning (e.g., reducing production time and waste of materials). This company's business needs are addressed in terms of two nontrivial binary Classification tasks (production disruptions and defects). The AoT prediction is achieved by using only input attributes available before the execution of the production process. To reduce the modeling effort, we focus on Automated ML (AutoML) methods, under two main approaches: Single-Target Classification (STC) and Multi-Target Classification (MTC). The former is achieved by adopting the popular H2O AutoML tool, while the latter adopts a deep learning neural network automatically tuned by using a Bayesian search. The computational experiments adopted a realistic rolling window evaluation over recently collected industrial data (comprising 14 months). Overall, interesting predictive results were achieved by both AutoML approaches, outperforming a baseline Decision Tree method. In addition, an eXplainable Artificial Intelligence (XAI) method based on a Sensitivity Analysis (SA) was adopted, allowing the identification of the most relevant inputs, which is valuable knowledge to support the decorated particleboard production planning. [ABSTRACT FROM AUTHOR]

Laser metal deposition is a branch of additive manufacturing that offers advantages over traditional manufacturing techniques for forming overhanging thin-walled metal parts. Previously, helical paths that were suitable for manufacturing such parts were not only limited to stacking material on a flat surface but were also fixed to the model boundaries. In order to solve these two problems to meet more complex process requirements, a non-planar helical path generation method is proposed for laser metal deposition. The method is based on the characteristics of the additive manufacturing process planning flow, which first slices the model using curved surfaces, then offsets the contours on the sliced layering, and finally generates continuous helical paths according to the contracted or expanded contours. In order to verify the feasibility of the method, hollow blades are formed on cylindrical surfaces following the planned paths. The results show that the proposed method is not only capable of assisting the laser metal deposition process to fabricate thin-walled structures on non-planar surfaces but also capable of freely adjusting the contour dimension. [ABSTRACT FROM AUTHOR]

Steel-making and continuous casting (SCC) processes are considered the main bottlenecks in the steel industry. We consider planning and scheduling problems in SCC processes and provide an overview of the literature with our commentary. The planning problem has as goal to create out of customer orders production units for the various production processes. The scheduling problem assigns these production units to the production facilities over different time intervals. We review a large number of planning and scheduling papers and propose two basic models for the planning and the scheduling problems that incorporate the most common and essential features. We describe for each problem the respective constraints and objectives as well as the practical implications in an actual production environment. We also analyse the methodologies used in their solutions, including decomposition strategies. In the last section, we present the conclusions of our survey and propose new research directions. [ABSTRACT FROM AUTHOR]

During the planning stages of new factories for the Body-In-White assembly, the processes used per production system need to be defined. Each production system uses a specific combination of processes, with each process belonging to a main process group. The combination of the processes and groups is subject to restrictions. Since the amount of possible combinations is too large to individually check for restrictions, we propose a Neural Network using an energy measurement derived from Hopfield networks. The proposed network memorizes former correct combinations and provides a recommendation score on how likely a new planned configuration is. Since processes can be paired with processes from their own group or with themselves, the Neural Network is modified to allow loops for joining vertices with themselves. This modification is achieved by adjusting the energy function of Hopfield networks to measure the activation of the combinations of clusters, meaning the edges, and not the activation of vertices during the training phase. We implemented the network for the process planning of factories of a leading European automotive manufacturer, and the results using correct, incorrect, and random process combinations indicate a strong capability of detecting anomalous process combinations. [ABSTRACT FROM AUTHOR]

Geometrical tolerances are used to limit the variations of the actual measured features of a part relative to its ideal features or datum features in form, profile, orientation, location, and run-out. Although the geometrical feature variations can also contribute significantly to the manufacturing cost as well as the quality of a product, the current studies on concurrent tolerancing mainly focus on the concurrent optimal allocation of dimensional tolerances, with little consideration of geometrical tolerances. The purpose of this paper is to extend the concurrent tolerance allocation model to take the geometrical tolerance requirements into account for multi-process machining parts. Firstly, the function relationship between the quality loss and the process tolerances of a product with multiple relevant assembly dimensions is studied, and the geometrical tolerance is attached to the contact point of the corresponding feature surface of the part as a zero-length dimension parameter with tolerance. Then, the functional assembly tolerances are assigned to process dimensional and geometrical tolerances based on the given part process planning, during which geometrical tolerances are treated as the equivalent dimensional tolerances or the additional tolerance constraints according to their own characteristics. An extended model for concurrently allocating dimensional and geometrical tolerances is established by using total cost minimization as the criteria, the product function constraints, geometrical tolerance constraints, and economical process tolerance bounds as constraint conditions, and the nonlinear programming technique is used to solve the extended model to obtain the optimized design tolerances and process tolerances. Finally, an example of concurrent tolerance allocation of bevel gear assembly is given to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In the process of ship production, the production of pipeline occurs throughout the production process. Key issues to address in the pipeline-processing workshop of a shipyard include uneven production loads and weak flow rhythmicity. Additionally, uncertainties in processing time and other real-world factors can pose significant challenges. The fuzzy number method is used to describe processing and completion times, with the goal of minimizing the fuzzy maximum completion time. To achieve this, a scheduling method based on the Black Widow Optimization Algorithm (BWOA) is proposed for the pipeline-processing workshop in a shipyard. This algorithm aims to effectively reduce the blindness of the production plan, ensure the rationality and stability of the plan, shorten the production cycle of a ship pipeline, improve the production efficiency, and realize the lean shipbuilding mode. A simulation was conducted to evaluate the BWOA algorithm. The final simulation test results show that the algorithm provided a better scheduling plan and a stabler average value than comparable methods, which proves its effectiveness in the scheduling of pipeline processing in shipyards. [ABSTRACT FROM AUTHOR]

Manufacturing processes are often disrupted by unexpected events, such as machine breakdowns, raw material shortages, and the arrival of new orders. Effectively managing these uncertainties is crucial for maintaining the feasibility and optimality of the production system. The efficiency of a manufacturing system is heavily dependent on the optimality of its scheduling plan. In this study, we present a reactive scheduling approach based on the S-graph framework. The proposed method is specifically designed to handle the arrival of new jobs and generate schedules with the shortest makespan, i.e., the minimum total completion time. Whenever a new order is received, the method dynamically adjusts the production plan through rescheduling. Three distinct scheduling policies are identified that determine which tasks require scheduling or rescheduling and which tasks should remain unchanged in their schedules. To evaluate the effectiveness of the algorithm, we solve several examples from the literature and analyze the results. The findings demonstrate the efficiency and efficacy of the proposed approach. The ability to accommodate new job arrivals and generate schedules with a minimized makespan highlights the practical relevance and benefits of the S-graph-based reactive scheduling method. [ABSTRACT FROM AUTHOR]

The Dynamic lot sizing (DLS) model is widely used in production planning, to minimize inventory levels, which basically will minimize production costs. The DLS model tries to eliminate the assumption of a fixed demand level throughout the period used in the Economic Order Quantity (EOQ) model. The characteristics of business processes in online retailers are the basis of this research to develop DLS models. This research develops a DLS model for retailers with multi-supplier cases, quantity discounts, and capacity constraints that consider Advanced Demand Information (ADI). Based on the numerical test results, it appears that ADI has an effect on reducing total production costs in the DLS model. The results of the numerical tests also showed that the model was able to solve the problems of the production planning process for SMEs and retailers who considered ADI. [ABSTRACT FROM AUTHOR]

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Today, due to shop-floor uncertainties and widespread cross-enterprise collaborations, manufacturing systems of enterprises are increasingly demanded to be agile, adaptive, flexible and interoperable. Process planning systems are mission-critical constituent components of manufacturing systems in machining job shops of small and medium-sized enterprises in the machining and metal cutting sector. Cloud-based adaptive distributed process planning, which includes global supervisory planning in the cloud and local operation planning based on function block and cloud technologies, provides an effective approach for enhancing agility, adaptability, flexibility and interoperability of manufacturing systems. [ABSTRACT FROM AUTHOR]

The highly competitive and volatile market puts companies in a tough position. While cost and time efficiency are important to stay competitive, environmental awareness is more and more critical. The reconfigurable manufacturing system (RMS) paradigm is suggested to cope with these new challenges. In addition to its six fundamental characteristics, it is seen as an enabler for Industry 4.0. This article investigates the multi-objective process planning problem in an environmentally conscious manner in a reconfigurable manufacturing environment. Four criteria are minimised: total production cost, total production time, total amount of greenhouse gas produced by machines, and total quantity of hazardous liquid wastes. To address the problem, modified versions of the non-dominated sorting genetic algorithm (NSGA) method, namely new dynamic NSGA-II (NewD-NSGA-II) and New NSGA-III, are developed and evaluated. Rich experimental results are presented and analysed using three metrics to demonstrate the efficacy of the proposed approaches: inverted generational distance (IGD), diversity measure (DM), and cardinality of the mixed Pareto fronts (CMPF). The effects of the similarity coefficient on the convergence of the NewD-NSGA-II and New NSGA-III are investigated, and the TOPSIS technique is used to assist the decision-maker in evaluating and selecting the best process plans. [ABSTRACT FROM AUTHOR]

This paper studies a common planning problem encountered in steelmaking production. The problem is to group different customer orders into a set of batches to accommodate the mass production mode of steelmaking furnaces. We formulate the problem as a novel mixed-integer programming model by considering the practical technological requirements. To solve the problem, we propose a cooperative coevolution framework in which an effective decomposition scheme based on graph partitioning is developed. The decomposition scheme first explores the problem structure by considering the production process rules and then exploits the batch information of the best-so-far solution to identify the potentially better decompositions. To solve each decomposed subcomponent, we propose a new differential evolution algorithm which incorporates a subpopulation-based classification mechanism and local search with an external archive strategy to balance the abilities of exploration and exploitation. Computational tests on a set of real production data as well as on a more diverse set of randomly generated problem instances show that our method is effective and efficient in practical application and outperforms other benchmark algorithms. [ABSTRACT FROM AUTHOR]

Manufacturing in the age of Industry 4.0 can be characterised by a high product variety and complex material flows. The increasing individualisation of products requires adaptive production planning and control systems. Research in the area of Machine Learning demonstrates the applicability and potential of Reinforcement Learning (RL) systems for the control of complex manufacturing. However, a major disadvantage of RL-methods is that they are usually considered as 'black box' models. For this reason, this paper investigates methods of explainable reinforcement learning in production control. Based on a comprehensive literature review an approach to increase the plausibility of RL-based control strategies is presented. The approach combines the advantages of high prediction accuracy (e.g. neural networks) and high explainability (e.g. decision trees). In doing so, understandable control strategies such as heuristics can be generated, and an advanced RL-system can be designed including specific domain expertise. The results are demonstrated based on a real-world system, taken from semiconductor manufacturing, which is investigated in a simulated approach. [ABSTRACT FROM AUTHOR]

In the manufacturing industry, production efficiency is the core competitiveness of enterprises, but also an important goal of enterprise production planning management, and in recent years, the production efficiency of China's manufacturing industry does not meet the requirements, and there are still many problems to be solved. In the face of this situation, the relevant fields have explored intelligent manufacturing and proposed intelligent manufacturing technology, which mainly integrates information technology with traditional manufacturing system, can realize the important measures of intelligence, automation, digitalization and information in the manufacturing process, and can help solve the current key problems. Therefore, this paper takes ship manufacturing as the background. The real-time optimization technology of intelligent ship manufacturing based on learning algorithm is studied. This technology can effectively improve ship production efficiency and reduce production cost. [ABSTRACT FROM AUTHOR]

Phase planning is one of the most important components of lean-based production planning that provides basic guidelines for the entire production process. In modular integrated construction (MiC) projects, the complicated and drawn-out features of the manufacturing or production process pose difficulties for informed phase planning decisions under uncertainties. However, owing to the nascent nature of MiC, traditional approaches have little prior knowledge of the uncertainties. This research aimed to address this problem by proposing a data-driven optimization method based on a set of valuable historical production data to hedge against uncertainties during production. A real-life case study was then conducted to validate this optimization approach. The planning solution, including the (1) critical production path, (2) detailed production schedules, and (3) simulated production process, balances production schedules and uncertainties to ensure feasible and robust phase planning. This optimization method can make full use of historical production data rather than approximations of the probability distributions to handle uncertainties in the phase planning process. This research provides an innovative and robust solution for MiC production managers to efficiently conduct phase planning under uncertainties. It enriches the literature on phase planning and contributes to lean MiC manufacturing. The biggest novelty of this research is to open up a window for researchers and practitioners to look into MiC production in factories, which are traditionally like a "black box" unknown to us. [ABSTRACT FROM AUTHOR]

Typically, the final process in the manufacture of molds is mechanical polishing, a process predominantly conducted manually. This is an important process step to improve the surface quality of the mold without affecting the geometry of the cavity. Currently, the European mold manufacturing industry faces two challenges for the polishing phase of manufacture. Firstly, economics when compared to global low-cost production areas. Secondly, the industry is starting to suffer the loss of skilled benchmen specialized in performing such operations. To address these two problems, robotic polishing has been investigated as an alternative or supplement to the conventional manual polishing process in the manufacturing of molds because of its high efficiency, improved reliability, and robustness. This paper presented a new methodology that allows robotic polishing to replace majority of manual polishing work for the manufacture of molds and achieves complete information integration with current CAD/CAM systems. Based on a number of experimental works, the types of features that are suitable for robotic polishing have been identified and a new robotic polishing feature classification has been proposed. A new polishing process planning has been developed to be integrated with current CAD/CAM systems, which includes a robotic polishing process knowledge database; a polishing process selection method; and a polishing process sequencing using genetic algorithm aiming to combine minimum polishing time and the specific constraints and rules for polishing process sequencing. Finally, a case study based on the proposed methods has been given, which demonstrates the proposed methodology is able to be implemented in the practical environment to allow robotic polishing to take majority of manual polishing workloads for mold manufacturing and achieve information integration with current CAD/CAM systems. [ABSTRACT FROM AUTHOR]

Background: Optimization approaches and a models can be applied for critical production systems that experience equipment failure, repair delays and product quality control in order to maximize the total profit. We can cite, as an example, flexible manufacturing systems. Methods: Our methodology involves developing a decision model integrated with an information system to coordinate various system operations, ensuring timely response to customer requests. The module of the information system is provided to optimally manage the production flow and parts ordering according to machine availability. The objective is to determine the optimal order thresholds of part batches that maximize the total profit. Results: Numerical results are provided to analyze the influence of system reliability and uncertainty on decision variables, offering insights into the system's performance and robustness. By using our method, the advancement of the flexible production systems is carried out by addressing key operational challenges and optimizing production processes for enhanced efficiency and profitability. Conclusions: To achieve this, an optimization algorithm is employed to identify optimal solutions that enhance profitability. [ABSTRACT FROM AUTHOR]

Computer technology influences the capability to enhance the functionality of manufacturing and product design technologies. Innovations in computational design and digital manufacturing empower designers and manufacturers to create novel representations and algorithms for designing, analyzing, and planning the production of highly complicated products achievable through state-of-the-art technology. Various principles, including computational physics, geometric reasoning, and automated spatial planning, enable engineers to generate entirely new categories of products in the footwear industry. This study aims to review the methods and tools that have been published in the literature for the last twenty years, and provide a better understanding of the parameters, tools, and controls that contribute to the design and manufacturing processes of shoes. The main focus is on highlighting the product design-related trends within the footwear industry. A structured framework becomes apparent in the literature through the grouping and presentation of information. This framework facilitates drawing conclusions about the trends and existing needs derived from in-depth research in the field of footwear. Additionally, it reveals the upcoming methods and tools that will contribute to the enhancement and development of this emerging and promising industry sector. In conclusion, the categorization limitation within the footwear industry could serve as the foundation for exploring key areas to be analyzed further in other industries, for instance, in furniture, clothing, and packaging. [ABSTRACT FROM AUTHOR]

The intricate process of planning production, involving product life cycle management and the synthesis of manufacturing information, is crucial for coherence in manufacturing. Manufacturing companies, operating in a high-mix, low-volume production environment, integrate production planning with management to focus on production processes, emphasizing high-quality, rapid product delivery. This includes material item planning to anticipate future demands and ensure sufficient raw material and finished product quantities, considering purchasing, production, and sales capacities. This study explores the electro technical sector, specifically a manufacturing entity specializing in low-voltage plastic cable distribution boxes. It scrutinizes the vital role of seasonal data cleaning in optimizing production planning, with a targeted focus on three products. The implementation of a chase demand strategy is related to capacity planning, taking into account the change in production capacity linked to demand over time. The problem in implementing this strategy is related to the fluctuating level of quality due to changes in demand for specified products. [ABSTRACT FROM AUTHOR]