A novel throughput control algorithm for semi-heterarchical industry 4.0 architecture

Modern market scenarios are imposing a radical change in the production concept, driving companies’ attention to customer satisfaction through increased product customization and quick response strategies to maintain competitiveness. At the same time, the growing development of Industry 4.0 technologies made possible the creation of new manufacturing paradigms in which an increased level of autonomy is one of the key concepts to consider. Taking the advantage from the recent development around the semi-heterarchical architecture, this work proposes a first model for the throughput control of a production system managed by such an architecture. A cascade control algorithm is proposed considering work-in-progress (WIP) as the primary control lever for achieving a specific throughput target. It is composed of an optimal control law based on an analytical model of the considered production system, and of a secondary proportional-integral-derivative controller capable of performing an additional control action that addresses the error raised by the theoretical model’s. The proposed throughput control algorithm has been tested in different simulated scenarios, and the results showed that the combination of the control actions made it possible to have continuous adjustment of the WIP of the controlled production system, maintaining it at the minimum value required to achieve the requested throughput with nearly zero errors

Predicting Failure Probability in Industry 4.0 Production Systems: A Workload-Based Prognostic Model for Maintenance Planning

Maintenance of equipment is a crucial issue in almost all industrial sectors as it impacts the quality, safety, and productivity of any manufacturing system. Additionally, frequent production rescheduling due to unplanned and unintended interruptions can be very time consuming, especially in the case of centrally controlled systems. Therefore, the ability to estimate the likelihood that a monitored machine will successfully complete a predefined workload, taking into account both historical data from the machine’s sensors and the impending workload, may be essential in supporting a new approach to scheduling activities in an Industry 4.0 production system. This study proposes a novel approach for integrating machine workload information into a well-established PHM algorithm for Industry 4.0, with the aim of improving maintenance strategies in the manufacturing process. The proposed approach utilises a logistic regression model to assess the health condition of equipment and a neural network computational model to estimate its failure probability according to the scheduled workloads. Results from a prototypal case study showed that this approach leads to an improvement in the prediction of the likelihood of completing a scheduled job, resulting in improved autonomy of CPSs in accepting or declining scheduled jobs based on their forecasted health state, and a reduction in maintenance costs while maximising the utilisation of production resources. In conclusion, this study is beneficial for the present research community as it extends the traditional condition-based maintenance diagnostic approach by introducing prognostic capabilities at the plant shop floor, fully leveraging the key enabling technologies of Industry 4.0

A Genetic-Algorithm-Based Approach for Optimizing Tool Utilization and Makespan in FMS Scheduling

This paper proposes a genetic algorithm approach to solve the identical parallel machines problem with tooling constraints in job shop flexible manufacturing systems (JS-FMSs) with the consideration of tool wear. The approach takes into account the residual useful life of tools and allocates a set of jobs with specific processing times and tooling requirements on identical parallel machines. Two metrics are introduced to evaluate the scheduling decisions and optimize the scheduling process, with the competitive goal of maximizing tool utilization and minimizing production makespan. The proposed approach searches for a set of optimal solutions on the Pareto front that offers the best possible balance between these two objectives, achieving optimal local performance in terms of both makespan and tool utilization. The approach is implemented with a customized genetic algorithm and validated on a real case study from a company operating in the aerospace sector, which confirms its effectiveness in increasing tool utilization and reducing the makespan. The results show that the proposed approach has significant practical implications for the manufacturing industry, particularly in the production of high-value materials such as those in the aerospace sector that require costly tools. This paper contributes to the operational research community by providing advanced scheduling algorithms that can optimize both the makespan and the tool utilization concurrently, improving production efficiency and maintaining competitiveness in the manufacturing industry

3D GRID-based pharmacophore and Metadynamics approaches for the rational design of N-Methyl β-sheet breaker peptides as inhibitors of the Alzheimer's Aβ-amyloid fibrillogenesis

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by the loss of the cognitive functions and dementia. Several scientific evidences report that a central role in the pathogenesis of AD is played by the brain deposition of insoluble aggregates of β-amyloid protein (Aβ) proteins, thus causing neuronal cell death [1]. For this reason, one of the promising approach is to inhibit the aggregation of Aβ peptides. Because Aβ is self-assembling, one possible strategy to prevent this process is to use short peptide fragments homologous to the full-length wild-type Aβ protein. From this consideration, several short synthetic peptides were designed as beta-sheet breakers (BSB) [2]. In particular, the pentapetide Ac-LPFFD-NH2 (iAβ5p) exhibited a certain capability to inhibit Aβ fibrillogenesis [3]. iAβ5p analogs [4] were, then, designed by introducing N-Methylation at the amide bond nitrogen were also promising BSB. Here, we describe the methodological approach, which combines 3D GRID-based pharmacophore peptide screening with Well-Tempered Metadynamics simulations aimed to the discovery of novel N-Methylated BSB. This approach led us to identify two promising, cell permeable, N-Methylated peptides that were further evaluated for their BSB properties showing a significant improvement of the fibrillogenesis inhibition with respect to the lead iAβ5p

A Deep Learning Algorithm for the Throughput Estimation of a CONWIP Line

The ability to meet increasingly personalized market demand in a short period of time and at a low cost can be regarded as a fundamental principle for industrialized countries’ competitive revival. The aim of Industry 4.0 is to resolve the long-standing conflict between the individuality of on-demand output and the savings realized through economies of scale. Significant progress has been established in the field of Industry 4.0 technologies, but there is still an open gap in the literature regarding methodologies for efficiently manage the available productive resources of a manufacturing system. The CONtrolled Work-In-Progress (CONWIP) production logic, proposed by Spearman et al., allows controlling the Work-In-Progress (WIP) in a production system while monitoring the throughput. However, an affordable estimation tool is still required to deal with the increased variability that enters the current production system. Taking advantage of recent advances in the field of machine learning, this paper contributes to the development of a performance estimation tool for a production line using a deep learning neural network. The results demonstrated that the proposed estimation tool can outperform the current best-known mathematical model by estimating the throughput of a CONWIP Flow-Shop production line with a given variability and WIP value set into the system

On the modelling of a decentralized production control system in the Industry 4.0 environment

The paper deals with a decentralized production control in an Industry 4.0 environment. In such a kind of systems, the capability to deliver a high level of product customization together with reduced response time is crucial to maintain competitiveness and to increase profit. A semi-heterarchical architecture, formed by three levels, in which the first is responsible for meeting business objectives, the second to maintain target system general performances, and the third to tackle operative scheduling problems, is first discussed as a framework for the future implementation in an Industry 4.0 environment. Successively, the problem to model the system form a dynamic point of view is addressed directly at the second architectural level. This paper, in particular, contributes to the semi-heterarchical architecture development, by proposing a first mathematical model of the shop-floor of a such a system, involving the use of the population dynamic modelling. Finally, the results of the first implementation in a simulated environment are reported

A systemic analysis of the impacts of Product 4.0 on the triple bottom-line of Sustainability

The advent of innovative technologies is reshaping every aspect of our lives. Smart products incorporating these new technologies have become commonplace in both our private and professional lives. The use of advanced technologies has shown great potential in improving and streamlining various daily activities, but the products that support their deployment in real life present serious threats to the environment. Indeed, the speed at which new technological products are brought onto the market and old ones are discarded is generating a dual negative effect: an exponential increase in electrical and electronic waste and unsustainable exploitation of non-renewable natural resources. This situation in turn can have significant effects on the economic sustainability of our societies, due to the increasing costs of waste disposal and the increasingly limited availability of raw materials. As is evident, the introduction of smart products has its positive and negative sides that influence all aspects of sustainability within a complex structure of cause-effect relationships. Therefore, this article investigates the main variables at play and their interconnections when considering smart products. To analyse the effects of these variables, a Causal Loop Diagram (CLD) is developed and thoroughly discussed. The proposed CLD highlights the sustainability aspects of smart products. In addition, it highlights how the introduction of the so-called”Product 4.0” can be a solution to improve the triple bottom-line of sustainability

A novel dispatching rule for semi-heterarchical architectures in the industry 4.0 context

Industry 4.0 is changing the way to produce, pursuing increased flexibility of production systems and an ever-greater decision-making autonomy of the machines. The aim is to achieve high level of performances even in market scenarios requiring high level of customization, as the Mass Customisation (MC) paradigm imposes. Current hierarchical Manufacturing Planning and Control (MPC) systems showed limits in catching this goal, primarily due to their structural lack of flexibility. For this reason, the interest in the hybrid MPC architectures like the semi-heterarchical one is increasing. The objective of this work is to contribute to the design of such an architecture, proposing a new scheduling mechanism for the lowest decisional level. This mechanism, differently from the ones already proposed in the literature, schedules the next jobs to be admitted in the system choosing them by couples. The proposed rule has been tested through a simulation environment in three different scenarios of demand generation rate. The results showed an improvement in demand absorption and productivity compared to the rules used up to now

On the open job-shop scheduling problem: A decentralized multi-agent approach for the manufacturing system performance optimization

This paper investigates a dynamic integration of the process planning and scheduling operations of a typical Open Job-Shop manufacturing system. For this purpose, a modified CNP-based negotiation protocol - through a multi-agent modelling for jobs and operating machines - is proposed. This approach allows the introduction of an agents' hybrid behavior, considering both the own return and the system profit achieving the production performance maximization. Finally, a series of simulation runs are conducted in order to compare the performance of the proposed protocol with a recent optimization approach that uses a simple composite dispatching rule