Petri Nets Models for Analysis and Control of Public Bicycle-Sharing Systems

International audienc

Reliability engineering of large jit production systems

This paper introduces the rationale and the fundamental elements and algorithms of a reliability engineering methodology, and discusses its application to the design of a large, multi-cell and heterogeneous production system with just-in-time (JIT) deliveries. The failure analysis and the non-reliability costs assessment of such systems is a complex task. In order to cope with such complexity, a two level hierarchical modelling and evaluation framework was developed. According to this framework, the internal behaviour of each manufacturing cell and the overall flow of materials are described, respectively, by local and global models. Local models are firstly obtained from the failure and repair processes of the manufacturing equipment. Then, these models are combined with the failure propagation delays introduced by the work-in-process buffers in order to obtain the system level model. The second part of the paper addresses several design issues of the production system that directly impact the reliability of the deliveries, such as the layout of the plant, the redundancy of the manufacturing equipment and the capacity of the work-in-process buffers. A distinctive feature of the reliability evaluation algorithm resides on the ability to deal with reliability models containing stochastic processes with generalized distributions. This fundamental requirement comes from the fact that repair and failure propagation processes typically present hyper-exponential distributions, e.g., lognormal distributions, that can’t be assessed using the conventional reliability techniques. The paper will also explain how the behavioural and structural characteristics of JIT production systems were explored in order to implement effective evaluation algorithms that fit the requirements of this class of systems.DST -Department of Science and Technology, Government of Kerala(600/09

A unified race algorithm for offline parameter tuning

This paper proposes uRace, a unified race algorithm for efficient offline parameter tuning of deterministic algorithms. We build on the similarity between a stochastic simulation environment and offline tuning of deterministic algorithms, where the stochastic element in the latter is the unknown problem instance given to the algorithm. Inspired by techniques from the simulation optimization literature, uRace enforces fair comparisons among parameter configurations by evaluating their performance on the same training instances. It relies on rapid statistical elimination of inferior parameter configurations and an increasingly localized search of the parameter space to quickly identify good parameter settings. We empirically evaluate uRace by applying it to a parameterized algorithmic framework for loading problems at ORTEC, a global provider of software solutions for complex decision-making problems, and obtain competitive results on a set of practical problem instances from one of the world's largest multinationals in consumer packaged goods

Specification and Automatic Generation of Simulation Models with Applications in Semiconductor Manufacturing

The creation of large-scale simulation models is a difficult and time-consuming task. Yet simulation is one of the techniques most frequently used by practitioners in Operations Research and Industrial Engineering, as it is less limited by modeling assumptions than many analytical methods. The effective generation of simulation models is an important challenge. Due to the rapid increase in computing power, it is possible to simulate significantly larger systems than in the past. However, the verification and validation of these large-scale simulations is typically a very challenging task. This thesis introduces a simulation framework that can generate a large variety of manufacturing simulation models. These models have to be described with a simulation data specification. This specification is then used to generate a simulation model which is described as a Petri net. This approach reduces the effort of model verification. The proposed Petri net data structure has extensions for time and token priorities. Since it builds on existing theory for classical Petri nets, it is possible to make certain assertions about the behavior of the generated simulation model. The elements of the proposed framework and the simulation execution mechanism are described in detail. Measures of complexity for simulation models that are built with the framework are also developed. The applicability of the framework to real-world systems is demonstrated by means of a semiconductor manufacturing system simulation model.Ph.D.Committee Chair: Alexopoulos, Christos; Committee Co-Chair: McGinnis, Leon; Committee Member: Egerstedt, Magnus; Committee Member: Fujimoto, Richard; Committee Member: Goldsman, Davi

An effective tool for supply chain decision support during new product development process

The global marketplace has transformed supply chain design into a discipline which requires business sense supported by mathematical expertise. Several methods have been introduced to support supply chain design, most notably mixed integer programming. The current methods are tailor-made for situations where a product's bill-of-material is fixed. However, this assumption does not hold during product development where several competing product designs exist. Therefore this research investigates the question of what is an effective way to support supply chain decisions during new product development. The study is divided into four research questions, corresponding to the articles from which the dissertation is compiled: (1) Does a product structure-driven method exist for modeling and analyzing supply chains? (2) If such a method is discovered, what is its mathematical formulation? (3) Is there evidence to support the theoretical and practical usability of such a method? (4) How can strategic supply chain decisions be validated? Regarding question (1) the research finds that there is a shortage of methods that fulfill supply chain modeling and analysis requirements imposed by new product development process. During the research a Petri-net based method was constructed which satisfies these requirements. For question (2), the formal definitions of the constructed Petri net class are provided. Regarding question (3), the research finds that the created method and associated tool are useful aids when solving the question of the effect of demand variation and the number of product variants on the optimal supply chain. Furthermore, interviews with end users of the tool implementation provide evidence of the Petri net method's practical usefulness. Regarding question (4), the research finds that the validation of strategic supply chain decisions from companies' reporting systems is important, but it has not become a common practice due to the challenges in integrating various IT systems