The role of learning on industrial simulation design and analysis

The capability of modeling real-world system operations has turned simulation into an indispensable problemsolving methodology for business system design and analysis. Today, simulation supports decisions ranging from sourcing to operations to finance, starting at the strategic level and proceeding towards tactical and operational levels of decision-making. In such a dynamic setting, the practice of simulation goes beyond being a static problem-solving exercise and requires integration with learning. This article discusses the role of learning in simulation design and analysis motivated by the needs of industrial problems and describes how selected tools of statistical learning can be utilized for this purpose

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Towards a Methodology for the Economic Performance Increase of Production Lines using Reinforcement Learning

The increasing number of variants in product portfolios contributes to the challenge of efficient manufacturing on production lines due to the resulting small batch sizes and thus frequent product changes that lower the average overall plant effectiveness. Especially for companies that manufacture at high speed on production lines, such as in the Fast Moving Consumer Good (FMCG) industry, it is a central task of operational management to increase the performance of production lines. Due to the multitude of different adjustment levers at several interdependent machines, the identification of efficient actions and their combination into economic improvement trajectories is challenging. There is a variety of approaches to address this challenge, e.g. simulation-based heuristics. However, these approaches mostly focus on details instead of giving a holistic perspective of the possibilities to improve a production line or are limited in practical application. In other areas of application, reinforcement learning has shown remarkable success in recent years. The principle feasibility of using reinforcement learning in this application context has been demonstrated as well. However, it became apparent that the integration of expert knowledge throughout the improvement process is necessary. For this reason this paper transforms five modules defined from an engineering point of view into the mathematical scheme of a markov decision problem, a default framework for reinforcement learning. This provides the foundation for applying reinforcement learning in combination with expert knowledge from an engineering perspective

Short-Term Robustness of Production Management Systems: New Methodology

This paper investigates the short-term robustness of production planning and control systems. This robustness is defined here as the systems ability to maintain short-term service probabilities (i.e., the probability that the fill rate remains within a prespecified range), in a variety of environments (scenarios). For this investigation, the paper introduces a heuristic, stagewise methodology that combines the techniques of discrete-event simulation, heuristic optimization, risk or uncertainty analysis, and bootstrapping. This methodology compares production control systems, subject to a short-term fill-rate constraint while minimizing long- term work-in-process (WIP). This provides a new tool for performance analysis in operations management. The methodology is illustrated via the example of a production line with four stations and a single product; it compares Kanban, Conwip, Hybrid, and Generic production control schemes.manufacturing;inventory;risk analysis;robustness and sensitivity analysis;scenarios

Self-Evaluation Applied Mathematics 2003-2008 University of Twente

This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008