Hybrid discrete event simulation with model predictive control for semiconductor supply-chain manufacturing

The industry has experienced significant advancements in recent years, primarily focusing on smart manufacturing, culminating in the Industry 4.0 (I4.0) revolution I4.0 emphasizes interconnectivity, real time data capture and transmission among machines, autonomy, and machine learning, providing manufacturing companies numerous growth opportunities. The Industrial Internet of Things (IIoT) is a core component of this revolution, becoming integral to each system and increasing complexity due to the vast number of interconnected devices and diverse physical components. The variety of virtual services distributed across the architectural layers of industrial systems (cloud, fog, edge) and the various connection types between IIoT devices introduce security and privacy challenges, which are critical issues for any system incorporating IIoT. To fully leverage IIoT’s potential, addressing these security and privacy concerns is essential. Research and design in this domain are challenging, particularly when creating a simulation environment to study a system’s behavior over time. Despite the extensive research in IoT and the significant benefits of simulation based approaches, there remains a challenge in creating detailed representations from the underlying IoT nodes to the application layer in the cloud, along with the underlying networking infrastructure. To assist researchers and practitioners in overcoming these challenges, we propose the Discrete Event System Specification (DEVS) formalism. DEVS provides a mathematical framework for modeling systems, whether discrete or continuous events, allowing for the simulation of these systems within the DEVS environment. Every system, whether real or conceptual, has a time base, inputs, outputs, and functions to determine the next state, as well as outputs that reflect the current state and inputs. Simulating the system within the DEVS environment allows one to study its behavior to predict and optimize performance patterns.

Artificial neural networks (ANNs), a branch of artificial intelligence, has become a very interesting domain since the eighties when back-propagation (BP) learning algorithm for multilayer feed-forward architecture was introduced to solve nonlinear problems. It is used extensively to solve complex nonalgorithmic problems such as prediction, pattern recognition and clustering. However, in the context of a holistic study, there may be a need to integrate ANN with other models developed in various paradigms to solve a problem. In this paper, we suggest discrete event system specification (DEVS) be used as a model of computation (MoC) to make ANN models interoperable with other models (since all discrete event models can be expressed in DEVS, and continuous models can be approximated by DEVS). By combining ANN and DEVS, we can model the complex configuration of ANNs and express its internal workings. Therefore, we are extending the DEVS-based ANN proposed by Toma et al. [A new DEVS-based generic artficial neural network modeling approach, The 23rd European Modeling and Simulation Symp. (Simulation in Industry), Rome, Italy, 2011] for comparing multiple configuration parameters and learning algorithms and also to do prediction. The DEVS models are described using the high level language for system specification (HiLLS), [Maïga et al., A new approach to modeling dynamic structure systems, The 29th European Modeling and Simulation Symp. (Simulation in Industry), Leicester, United Kingdom, 2015] a graphical modeling language for clarity. The developed platform is a tool to transform ANN models into DEVS computational models, making them more reusable and more interoperable in the context of larger multi-perspective modeling and simulation (MAS).

Embedded real-time software construction has usually posed interesting challenges due to the complexity of the tasks these systems have to execute. Most methods for developing these systems are either hard to scale up for large systems, or require a difficult testing effort with no guarantee for bug-free software products. Although formal methods have showed promising results, they are difficult to apply when the complexity of the system under development scales up. Instead, systems engineers have often relied on the use of modeling and simulation (M&S) techniques in order to make system development tasks manageable. Construction of system models and their analysis through simulation reduces both end costs and risks, while enhancing system capabilities and improving the quality of the final products. M&S let users experiment with “virtual’ systems, allowing them to explore changes, and test dynamic conditions in a risk-free environment. This is a useful approach, moreover considering that testing under actual operating conditions may be impractical and in some cases impossible. In this talk, we will present a Modeling and Simulation-based framework to develop embedded systems based on the DEVS (Discrete Event systems Specification) formalism. DEVS provides a formal foundation to M&S that proved to be successful for different complex systems. This approach combines the advantages of a simulationbased approach with the rigor of a formal methodology. We will discuss how to use this framework to incrementally develop embedded applications, and to integrate simulation models with hardware components seamlessly. One of the main aspects of the methodology is that it can be integrated with models of the environment in which the embedded controller will act. We will show how the Cell-DEVS and the QSS methods can be used for this task. We will introduce the main characteristics of the Cell-DEVS and QSS methods, and will show how to model physical systems. We will introduce an integrated environment that deals with these issues, orchestrating a cellular-based simulator (CD++), a GIS (GRASS) and data visualization (Google Earth), to simulate behavior and analyze results supporting the decision making for varied environmental scenarios. Our approach does not impose any order in the deployment of the actual hardware components, providing flexibility to the overall process. The use of DEVS improves reliability (in terms of logical correctness and timing), enables model reuse, and permits reducing development and testing times for the overall process. Consequently, the development cycle is shortened, its cost reduced, and quality and reliability of the final product is improved.

Supply chain management (SCM) in semiconductor manufacturing poses significant challenges that arise from the presence of long throughput times, unique constraints, and stochasticity in throughput time, yield, and customer demand. To address these concerns, a model predictive control (MPC) algorithm is developed which relies on a control-oriented formulation to generate daily decisions on starts of factories. A multiple-degree-of-freedom observer formulated for ease of tuning is implemented to achieve robustness and performance in the presence of nonlinearity and stochasticity in both supply and demand. The control algorithm is configured to meet the requirements of meeting customer demand (both forecasted and unforecasted), and track inventory and starts targets provided by higher level decision policies. Unique features of semiconductor manufacturing, such as capacity limits, packaging, and product reconfiguration, are formally addressed by imposing different constraints related to starts and inventories. This functionality contrasts that of standard approaches to MPC and makes this controller suitable as a tactical decision tool for semiconductor manufacturing and similar forms of high-volume discrete-parts manufacturing problems. Two representative case studies are examined under diverse realistic conditions with this flexible formulation of MPC. It is demonstrated that system robustness, performance, and high levels of customer service are achieved with proper tuning of the filter gains and weights, as well as the presence of adequate capacity in the supply chain.

A branch-and-price algorithm for the robust single-source capacitated facility location problem under demand uncertainty

System models are constructed to design, study, and understand complex systems. According to the systems modeling language (SysML) that is a standard for model-based system engineering, all engineering activities should be performed using a common model. To validate complex system models defined in SysML, simulation is usually employed. There are numerous efforts to simulate SysML models using different simulation methods and tools. However, the efficient support of automated generation of executable simulation code is still an issue tangled by the research community. This paper introduces DEVSys, an integrated framework for utilizing existing SysML models and automatically producing executable discrete event simulation code, according to model driven architecture (MDA) concepts. Although this approach is not simulation-specific, discrete event system specification (DEVS) was employed, due to the similarities between SysML and DEVS, mainly in system structure description, and the mature, yet ongoing research on expressing executable DEVS models in a simulator-neutral manner. DEVSys framework elements include (a) a SysML profile for DEVS, enabling integration of simulation capabilities into SysML models, (b) a meta-model for DEVS, allowing the utilization of MDA concepts and tools, (c) a transformation of SysML models to DEVS models, using a standard model transformation language as query/view/transform (QVT), and (d) the generation of DEVS executable code for a DEVS simulation environment with an extensible markup language (XML) interface. The definition and implementation of DEVSys elements, as well as the process for its application are demonstrated and discussed, with the aid of a simple working example.

Due to increased traffic congestion and carbon emissions, Bike Sharing Systems (BSSs) are adopted in various cities for short distance travels, specifically for last mile transportation. The success of a bike sharing system depends on its ability to have bikes available at the "right" base stations at the "right" times. Typically, carrier vehicles are used to perform repositioning of bikes between stations so as to satisfy customer requests. Owing to the uncertainty in customer demand and day-long repositioning, the problem of having bikes available at the right base stations at the right times is a challenging one. In this paper, we propose a multi-stage stochastic formulation, to consider expected future demand over a set of scenarios to find an efficient repositioning strategy for bike sharing systems. Furthermore, we provide a Lagrangian decomposition approach (that decouples the global problem into routing and repositioning slaves and employs a novel DP approach to efficiently solve routing slave) and a greedy online anticipatory heuristic to solve large scale problems effectively and efficiently. Finally, in our experimental results, we demonstrate significant reduction in lost demand provided by our techniques on real world datasets from two bike sharing companies in comparison to existing benchmark approaches.

This study aims to apply the PDCA (Plan, Do, Check, Act) method as a systematic strategy in inventory control for phase-out products to reduce deadstock at PT XYZ. The background of the study shows a significant increase in the amount of scrapping in the last three years due to the accumulation of uncontrolled deadstock. The research uses a qualitative descriptive approach with data collection techniques through observation, interviews, and historical company data. Through the PDCA stages, it was found that the main causes of deadstock stemmed from the inconsistency of phase-out control methods, manual monitoring processes by the PIC, and uncertainty in customer demand. The implementation of solutions in the form of standardizing control methods, digitizing inventory systems, and implementing an early warning system yielded significant results. The evaluation results showed that the improvement strategy was able to reduce the potential for deadstock by up to 80.7% in the sample product model. Thus, the PDCA method proved to be effective as a continuous improvement approach in inventory control during the product phase-out. This research is recommended to be applied more widely in inventory management in companies-.

Dynamic optimization of natural gas networks under customer demand uncertainties

Fine-Grained Parallel and Distributed Spatial Stochastic Simulation of Biological Reactions

Optimal energy-efficient predictive controllers in automotive air-conditioning/refrigeration systems

It is shown how systems can be advantageously represented as discrete event models by using DEVS (discrete event system specification), set-theoretic formalism. Such DEVS models provide a basis for the design of event-based logic control. In this control paradigm, the controller expects to receive confirming sensor responses to its control commands within definite time windows determined by its DEVS model of the system under control. The event-based control paradigm is applied in advanced robotic and intelligent automation, showing how classical process control can be readily interfaced with rule-based symbolic reasoning systems. >

Discrete event models are widely used to replicate, analyze, and understand complex systems. DEVS (Discrete Event System Specification) formalism enables hierarchical modeling, so it provides an efficiency in the model development of complex models. However, the hierarchical modeling incurs prolonged simulation executions due to indirect event exchanges through the model hierarchy. Although direct event paths are applied to mitigate this overhead, the situation becomes even worse when a model changes its structures during simulation execution, called a dynamic structure model. This article suggests Coupling Relation Graph (CRG) and Strongly Coupled Component (SCC) concepts to improve hierarchical and dynamic structure DEVS simulation execution. CRG is a directed graph representing DEVS model structure, and SCC is a group of connected components in a CRG. Using CRG and SCC, this article presents (1) how to develop CRG from a DEVS model and (2) how to construct and update direct event paths with respect to dynamic structural changes. In particular, compared to the previous works, the proposed method focuses on the reduction of the updating costs for the direct event paths. Through theoretical and empirical analyses, this article shows that the proposed method significantly reduces the simulation execution time, especially when a simulation model contains lots of components and changes its model structures frequently. We expect that the proposed method would support the faster simulation executions of complex hierarchical and dynamic structure models.

Simulation-based optimization of process control policies for inventory management in supply chains

The uses of UAVs are developing rapidly and are now also used to extend human capabilities. Their control poses several problems that are still open because the system is subject to strong constraints. In this context landing on mobile platform is one of challenging one. In this paper an automated solution for autonomous landing of a UAV on a moving target based on camera vision only is proposed. The method uses a discrete Model Predictive Control (MPC) approach based on Linear Time Invariant (LTI) model. Constraints on inputs, input rates and states are considered and solved using a Neural Optimizer. Software In the Loop (SIL) experiments were performed and proved that the method is valid. Finally a tool-chain for implementation and testing using open-source components only is explained.