Status and Future of Manufacturing Execution Systems

This paper proposes a taxonomy for characterizing manufacturing execution systems and discusses how they can benefit from the recent Developments of Industry 4.0. The study is based on a literature review. The taxonomy contributes to theory and practice by providing a framework for benchmarking of manufacturing execution systems. The taxonomy can be utilized in the selection or design process of the manufacturing execution systems. Outlining the further opportunities provided by Industry 4.0 technologies, the paper also provides directions for future improvements of manufacturing execution systems.acceptedVersio

Status and Future of Manufacturing Execution Systems

This paper proposes a taxonomy for characterizing manufacturing execution systems and discusses how they can benefit from the recent Developments of Industry 4.0. The study is based on a literature review. The taxonomy contributes to theory and practice by providing a framework for benchmarking of manufacturing execution systems. The taxonomy can be utilized in the selection or design process of the manufacturing execution systems. Outlining the further opportunities provided by Industry 4.0 technologies, the paper also provides directions for future improvements of manufacturing execution systems.acceptedVersio

Approach For Autonomous Control Of Intralogistics Considering Deterministic And Probabilistic Material Demand Information In Flexible Production Systems

In today's dynamic production landscape, flexible and resilient production systems are essential to meet the constant changes in internal product and production requirements as well as external market and customer demands. To meet these challenges, various flexible and resilient production system approaches offer the necessary structural, process-related and technological flexibility and resilience. However, the intralogistics material provision within these complex production systems is challenging due to increasing degrees of freedom and uncertainties caused by emerging turbulence, which has even risen very sharply in recent years due to global instability. This makes it difficult to coordinate material demands and material provision in the production system precisely regarding location and quantity. This paper presents a comprehensive approach for determining the material requirements and autonomously executing the corresponding material provision processes in a complex production system, which considers both deterministic and probabilistic information about the material demand’s location, quantity, and time. Utilizing autonomous control in intralogistics decentralizes complexity management in flexible production systems by transferring decision-making and process execution tasks to the system elements. For the development and verification of the comprehensive approach, an experimental research study was pursued based on a flexible production system, including simulation and practical experiments. Defining the deterministic and probabilistic material demands is based on the Monte Carlo method for carrying out simulation experiments with parameter variation. The autonomously controlled, target size-optimized execution of material provision to fulfil the determined material demands is based on an agent-based modelling and control approach for manual and automated intralogistics transport resources. The study showed that improved logistics performance (throughput time and adherence to schedules) can be achieved in flexible production systems in the event of turbulences by considering deterministic and probabilistic material demand information together with autonomous control of material provision

Decision Support Systems in the Context of Cyber-Physical Systems: Influencing Factors and Challenges for the Adoption in Production Scheduling

Cyber-physical systems promise a complete networking of all actors and resources involved in production and thus an improved availability of information. In this context decision support systems enable appropriate processing and presentation of the captured data. In particular, production scheduling could benefit from this, since it is responsible for the short-term planning and control of released orders. Since decision support systems and cyber-physical systems together are not yet widely used in production scheduling, the aim of this research study is to analyze the adoption of these technologies. In order to do so, we conducted a qualitative interview study with experts on production scheduling. Thereby, we identified eleven influencing factors and 22 related challenges, which affect the adoption of decision support systems in production scheduling in the context of cyber-physical systems. The results help to explain the adoption and can serve as a starting point for the development of those systems

Agent-Based Modelling and Heuristic Approach for Solving Complex OEM Flow-Shop Productions under Customer Disruptions

The application of the agent-based simulation approach in the flow-shop production environment has recently gained popularity among researchers. The concept of agent and agent functions can help to automate a variety of difficult tasks and assist decision-making in flow-shop production. This is especially so in the large-scale Original Equipment Manufacturing (OEM) industry, which is associated with many uncertainties. Among these are uncertainties in customer demand requirements that create disruptions that impact production planning and scheduling, hence, making it difficult to satisfy demand in due time, in the right order delivery sequence, and in the right item quantities. It is however important to devise means of adapting to these inevitable disruptive problems by accommodating them while minimising the impact on production performance and customer satisfaction. In this paper, an innovative embedded agent-based Production Disruption Inventory-Replenishment (PDIR) framework, which includes a novel adaptive heuristic algorithm and inventory replenishment strategy which is proposed to tackle the disruption problems. The capabilities and functionalities of agents are utilised to simulate the flow-shop production environment and aid learning and decision making. In practice, the proposed approach is implemented through a set of experiments conducted as a case study of an automobile parts facility for a real-life large-scale OEM. The results are presented in term of Key Performance Indicators (KPIs), such as the number of late/unsatisfied orders, to determine the effectiveness of the proposed approach. The results reveal a minimum number of late/unsatisfied orders, when compared with other approaches

Agent-Based Modelling and Heuristic Approach for Solving Complex OEM Flow-Shop Productions under Customer Disruptions

The application of the agent-based simulation approach in the flow-shop production environment has recently gained popularity among researchers. The concept of agent and agent functions can help to automate a variety of difficult tasks and assist decision-making in flow-shop production. This is especially so in the large-scale Original Equipment Manufacturing (OEM) industry, which is associated with many uncertainties. Among these are uncertainties in customer demand requirements that create disruptions that impact production planning and scheduling, hence, making it difficult to satisfy demand in due time, in the right order delivery sequence, and in the right item quantities. It is however important to devise means of adapting to these inevitable disruptive problems by accommodating them while minimising the impact on production performance and customer satisfaction. In this paper, an innovative embedded agent-based Production Disruption Inventory-Replenishment (PDIR) framework, which includes a novel adaptive heuristic algorithm and inventory replenishment strategy which is proposed to tackle the disruption problems. The capabilities and functionalities of agents are utilised to simulate the flow-shop production environment and aid learning and decision making. In practice, the proposed approach is implemented through a set of experiments conducted as a case study of an automobile parts facility for a real-life large-scale OEM. The results are presented in term of Key Performance Indicators (KPIs), such as the number of late/unsatisfied orders, to determine the effectiveness of the proposed approach. The results reveal a minimum number of late/unsatisfied orders, when compared with other approaches

Bi-level dynamic scheduling architecture based on service unit digital twin agents

Pure reactive scheduling is one of the core technologies to solve the complex dynamic disturbance factors in real-time. The emergence of CPS, digital twin, cloud computing, big data and other new technologies based on the industrial Internet enables information acquisition and pure reactive scheduling more practical to some extent. However, how to build a new architecture to solve the problems which traditional dynamic scheduling methods cannot solve becomes a new research challenge. Therefore, this paper designs a new bi-level distributed dynamic workshop scheduling architecture, which is based on the workshop digital twin scheduling agent and multiple service unit digital twin scheduling agents. Within this architecture, scheduling a physical workshop is decomposed to the whole workshop scheduling in the first level and its service unit scheduling in the second level. On the first level, the whole workshop scheduling is executed by its virtual workshop coordination (scheduling) agent embedded with the workshop digital twin consisting of multi-service unit digital twins. On the second level, each service unit scheduling coordinated by the first level scheduling is executed in a distributed way by the corresponding service unit scheduling agent associated with its service unit digital twin. The benefits of the new architecture include (1) if a dynamic scheduling only requires a single service unit scheduling, it will then be performed in the corresponding service unit scheduling without involving other service units, which will make the scheduling locally, simply and robustly. (2) when a dynamic scheduling requires changes in multiple service units in a coordinated way, the first level scheduling will be executed and then coordinate the second level service unit scheduling accordingly. This divide-and-then-conquer strategy will make the scheduling easier and practical. The proposed architecture has been tested to illustrate its feasibility and practicality

A simulated approach of a cyber physical system with reactive decision making for flexible manufacturing systems

Este trabajo contiene la planificación de una solución para sistemas de manufactura flexibles, construida como un modelo ciber físico con toma de decisiones reactiva, que aporta a la reducción del tiempo perdido por motivo de las perturbaciones que puedan aparecer en el sistema. Se tiene un proceso basado en el modelo de Valenciennes, el cual cuenta con un detallado proceso de la construcción de la herramienta, la elección de los requerimientos y perturbaciones y relación e interacción entre las partes que lo constituyen. Así mismo, detalla elementos necesarios como las reglas de decisión y el proceso de toma de decisiones, permitiendo mediante los resultados presentar un paralelo entre los escenarios sin perturbaciones y aquellos en los que se presentan. Del mismo modo evalúa el comportamiento entre un sistema ciber físico reactivo y un modelo de control predictivo, para estrechar la relación entre estas.This work contains the planning of a solution for flexible manufacturing systems, built as a cyber-physical model with reactive decision-making, which contributes to the reduction of time lost due to the disturbances that may appear in the system. There is a process based on the Valenciennes model, which has a detailed process for the construction of the tool, the choice of requirements and disturbances, and the relationship and interaction between its constituent parts. Likewise, it details necessary elements such as decision rules and the decision-making process, allowing the results to present a parallel between the undisturbed scenarios and those in which they are present. In the same way, it evaluates the behavior between a reactive cyber- physical system and a predictive control model, to narrow the relationship between them.Ingeniero (a) IndustrialPregrad