Development and Numerical Optimization of a System of Integrated Agents for Serial Production Lines

In modern high-volume industries, the serial production line (SPL) is of growing importance due to the inexorable increase in the complexity of manufacturing systems and the associated production costs. Optimal decisions regarding buffer size and the selection of components when designing and implementing an SPL can be difficult, often requiring complex analytical models, which can be difficult to conceive and construct. Here, we propose a model to evaluate and optimize the design of an SPL, integrating numerical simulation with artificial intelligence (AI). Numerous studies relating to the design of SPL systems have been published, but few have considered the simultaneous consideration of a number of decision variables. Indeed, the authors have been unable to locate in the published literature even one work that integrated the selection of components with the optimization of buffer sizes into a single framework. In this research, a System of Integrated Agents Numerical Optimization (SIGN) is developed by which the SPL design can be optimized. A SIGN consists of a components selection system and a decision support system. A SIGN aids the selection of machine tools, buffer sizes, and robots via the integration of AI and simulations. Using a purpose-developed interface, a user inputs the appropriate SPL parameters and settings, selects the decision-making and optimization techniques to use, and then displays output results. It will be implemented in open-source software to broaden the impact of the SIGN and extend its influence in industry and academia. It is expected that the results of this research project will significantly influence open-source manufacturing system design and, consequently, industrial and economic development

A Development Of Optimal Buffer Allocation Determination Method For Μ-Unbalanced Unpaced Production Line

This research deals with a buffer allocation problem in an unpaced (asynchronous) μ-unbalanced production line. Kajian ini membincangkan masalah peruntukan pemampan di dalam talian pengeluaran tidak melangkah dengan ketidakseimbangan-μ

Production and inventory control in complex production systems using approximate dynamic programming.

Production systems focus not only on providing enough product to supply the market, but also on delivering the right product at the right price, while lowering the cost during the production process. The dynamics and uncertainties of modern production systems and the requirements of fast response often make its design and operation very complex. Thus, analytical models, such as those involving the use of dynamic programming, may fail to generate an optimal control policy for modern production systems. Modern production systems are often in possession of the features that allow them to produce various types of product through multiple working stations interacting with each other. The production process is usually divided into several stages, thus a number of intermediate components (WIP) are made to stock and wait to be handled by the next production stage. In particular, development of an efficient production and inventory control policy for such production systems is difficult, since the uncertain demand, system dynamics and large changeover times at the work stations cause significant problems. Also, due to the large state and action space, the controlling problems of modern production systems often suffer from the curse of dimensionality

Artificial Intelligence as an Enabler of Quick and Effective Production Repurposing Manufactur-ing: An Exploratory Review and Future Research Propositions

The outbreak of Covid-19 created disruptions in manufacturing operations. One of the most serious negative impacts is the shortage of critical medical supplies. Manufacturing firms faced pressure from governments to use their manufacturing capacity to repurpose their production for meeting the critical demand for necessary products. For this purpose, recent advancements in technology and artificial intelligence (AI) could act as response solutions to conquer the threats linked with repurposing manufacturing (RM). The study’s purpose is to investigate the significance of AI in RM through a systematic literature review (SLR). This study gathered around 453 articles from the SCOPUS database in the selected research field. Structural Topic Modeling (STM) was utilized to generate emerging research themes from the selected documents on AI in RM. In addition, to study the research trends in the field of AI in RM, a bibliometric analysis was undertaken using the R-package. The findings of the study showed that there is a vast scope for research in this area as the yearly global production of articles in this field is limited. However, it is an evolving field and many research collaborations were identified. The study proposes a comprehensive research framework and propositions for future research development

An artificial neural network based decision support system for energy efficient ship operations

Reducing fuel consumption of ships against volatile fuel prices and greenhouse gas emissions resulted from international shipping are the challenges that the industry faces today. The potential for fuel savings is possible for new builds, as well as for existing ships through increased energy efficiency measures; technical and operational respectively. The limitations of implementing technical measures increase the potential of operational measures for energy efficient ship operations. Ship owners and operators need to rationalise their energy use and produce energy efficient solutions. Reducing the speed of the ship is the most efficient method in terms of fuel economy and environmental impact. The aim of this paper is twofold: (i) predict ship fuel consumption for various operational conditions through an inexact method, Artificial Neural Network ANN; (ii) develop a decision support system (DSS) employing ANN based fuel prediction model to be used on-board ships on a real time basis for energy efficient ship operations. The fuel prediction model uses operating data -‘Noon Data’ - which provides information on a ship’s daily fuel consumption. The parameters considered for fuel prediction are ship speed, revolutions per minute (RPM), mean draft, trim, cargo quantity on board, wind and sea effects, in which output data of ANN is fuel consumption. The performance of the ANN is compared with multiple regression analysis (MR), a widely used surface fitting method, and its superiority is confirmed. The developed DSS is exemplified with two scenarios, and it can be concluded that it has a promising potential to provide strategic approach when ship operators have to make their decisions at an operational level considering both the economic and environmental aspects

Joint Control of Manufacturing and Onsite Microgrid System Via Novel Neural-Network Integrated Reinforcement Learning Algorithms

Microgrid is a promising technology of distributed energy supply system, which consists of storage devices, generation capacities including renewable sources, and controllable loads. It has been widely investigated and applied for residential and commercial end-use customers as well as critical facilities. In this paper, we propose a joint state-based dynamic control model on microgrids and manufacturing systems where optimal controls for both sides are implemented to coordinate the energy demand and supply so that the overall production cost can be minimized considering the constraint of production target. Markov Decision Process (MDP) is used to formulate the decision-making procedure. The main computing challenge to solve the formulated MDP lies in the co-existence of both discrete and continuous parts of the high-dimensional state/action space that are intertwined with constraints. A novel reinforcement learning algorithm that leverages both Temporal Difference (TD) and Deterministic Policy Gradient (DPG) algorithms is proposed to address the computation challenge. Experiments for a manufacturing system with an onsite microgrid system with renewable sources have been implemented to justify the effectiveness of the proposed method

Intelligent shop scheduling for semiconductor manufacturing

Semiconductor market sales have expanded massively to more than 200 billion dollars annually accompanied by increased pressure on the manufacturers to provide higher quality products at lower cost to remain competitive. Scheduling of semiconductor manufacturing is one of the keys to increasing productivity, however the complexity of manufacturing high capacity semiconductor devices and the cost considerations mean that it is impossible to experiment within the facility. There is an immense need for effective decision support models, characterizing and analyzing the manufacturing process, allowing the effect of changes in the production environment to be predicted in order to increase utilization and enhance system performance. Although many simulation models have been developed within semiconductor manufacturing very little research on the simulation of the photolithography process has been reported even though semiconductor manufacturers have recognized that the scheduling of photolithography is one of the most important and challenging tasks due to complex nature of the process. Traditional scheduling techniques and existing approaches show some benefits for solving small and medium sized, straightforward scheduling problems. However, they have had limited success in solving complex scheduling problems with stochastic elements in an economic timeframe. This thesis presents a new methodology combining advanced solution approaches such as simulation, artificial intelligence, system modeling and Taguchi methods, to schedule a photolithography toolset. A new structured approach was developed to effectively support building the simulation models. A single tool and complete toolset model were developed using this approach and shown to have less than 4% deviation from actual production values. The use of an intelligent scheduling agent for the toolset model shows an average of 15% improvement in simulated throughput time and is currently in use for scheduling the photolithography toolset in a manufacturing plant

A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)

Neuromorphic computing systems comprise networks of neurons that use asynchronous events for both computation and communication. This type of representation offers several advantages in terms of bandwidth and power consumption in neuromorphic electronic systems. However, managing the traffic of asynchronous events in large scale systems is a daunting task, both in terms of circuit complexity and memory requirements. Here we present a novel routing methodology that employs both hierarchical and mesh routing strategies and combines heterogeneous memory structures for minimizing both memory requirements and latency, while maximizing programming flexibility to support a wide range of event-based neural network architectures, through parameter configuration. We validated the proposed scheme in a prototype multi-core neuromorphic processor chip that employs hybrid analog/digital circuits for emulating synapse and neuron dynamics together with asynchronous digital circuits for managing the address-event traffic. We present a theoretical analysis of the proposed connectivity scheme, describe the methods and circuits used to implement such scheme, and characterize the prototype chip. Finally, we demonstrate the use of the neuromorphic processor with a convolutional neural network for the real-time classification of visual symbols being flashed to a dynamic vision sensor (DVS) at high speed.Comment: 17 pages, 14 figure