A comprehensive literature classification of simulation optimisation methods

Simulation Optimization (SO) provides a structured approach to the system design and configuration when analytical expressions for input/output relationships are unavailable. Several excellent surveys have been written on this topic. Each survey concentrates on only few classification criteria. This paper presents a literature survey with all classification criteria on techniques for SO according to the problem of characteristics such as shape of the response surface (global as compared to local optimization), objective functions (single or multiple objectives) and parameter spaces (discrete or continuous parameters). The survey focuses specifically on the SO problem that involves single per-formance measureSimulation Optimization, classification methods, literature survey

Simulation Optimization for Manufacturing System Design

A manufacturing system characterized by its stochastic nature, is defined by both qualitative and quantitative variables. Often there exists a situation when a performance measure such as throughput, work-in-process or cycle time of the system needs to be optimized with respect to some decision variables. It is generally convenient to express a manufacturing system in the form of an analytical model, to get the solutions as quickly as possible. However, as the complexity of the system increases, it gets more and more difficult to accommodate that complexity into the analytical model due to the uncertainty involved. In such situations, we resort to simulation modeling as an effective alternative.Equipment selection forms a separate class of problems in the domain of manufacturing systems. It assumes a high significance for capital-intensive industry, especially the semiconductor industry whose equipment cost comprises a significant amount of the total budget spent. For semiconductor wafer fabs that incorporate complex product flows of multiple product families, a reduction in the cycle time through the choice of appropriate equipment could result in significant profits. This thesis focuses on the equipment selection problem, which selects tools for the workstations with a choice of different tool types at each workstation. The objective is to minimize the average cycle time of a wafer lot in a semiconductor fab, subject to throughput and budget constraints. To solve the problem, we implement five simulation-based algorithms and an analytical algorithm. The simulation-based algorithms include the hill climbing algorithm, two gradient-based algorithms biggest leap and safer leap, and two versions of the nested partitions algorithm. We compare the performance of the simulation-based algorithms against that of the analytical algorithm and discuss the advantages of prior knowledge of the problem structure for the selection of a suitable algorithm

A comprehensive literature classification of simulation optimisation methods

Simulation Optimization (SO) provides a structured approach to the system design and configuration when analytical expressions for input/output relationships are unavailable. Several excellent surveys have been written on this topic. Each survey concentrates on only few classification criteria. This paper presents a literature survey with all classification criteria on techniques for SO according to the problem of characteristics such as shape of the response surface (global as compared to local optimization), objective functions (single or multiple objectives) and parameter spaces (discrete or continuous parameters). The survey focuses specifically on the SO problem that involves single per-formance measur

A comprehensive literature classification of simulation optimisation methods

Simulation Optimization (SO) provides a structured approach to the system design and configuration when analytical expressions for input/output relationships are unavailable. Several excellent surveys have been written on this topic. Each survey concentrates on only few classification criteria. This paper presents a literature survey with all classification criteria on techniques for SO according to the problem of characteristics such as shape of the response surface (global as compared to local optimization), objective functions (single or multiple objectives) and parameter spaces (discrete or continuous parameters). The survey focuses specifically on the SO problem that involves single per-formance measur

Coupling soft computing, simulation and optimization in supply chain applications : review and taxonomy

Supply chain networks are typical examples of complex systems. Thereby, making decisions in such systems remains a very hard issue. To assist decision makers in formulating the appropriate strategies, robust tools are needed. Pure optimization models are not appropriate for several reasons. First, an optimization model cannot capture the dynamic behavior of a complex system. Furthermore, most common practical problems are very constrained to be modeled as simple tractable models. To fill in the gap, hybrid optimization/simulation techniques have been applied to improve the decision-making process. In this paper we explore the near-full spectrum of optimization methods and simulation techniques. A review and taxonomy were performed to give an overview of the broad field of optimization/simulation approaches applied to solve supply chain problems. Since the possibilities of coupling them are numerous, we launch a discussion and analysis that aims at determining the appropriate framework for the studied problem depending on its characteristics. Our study may serve as a guide for researchers and practitioners to select the suitable technique to solve a problem and/or to identify the promising issues to be further explored

Transiency-driven Resource Management for Cloud Computing Platforms

Modern distributed server applications are hosted on enterprise or cloud data centers that provide computing, storage, and networking capabilities to these applications. These applications are built using the implicit assumption that the underlying servers will be stable and normally available, barring for occasional faults. In many emerging scenarios, however, data centers and clouds only provide transient, rather than continuous, availability of their servers. Transiency in modern distributed systems arises in many contexts, such as green data centers powered using renewable intermittent sources, and cloud platforms that provide lower-cost transient servers which can be unilaterally revoked by the cloud operator. Transient computing resources are increasingly important, and existing fault-tolerance and resource management techniques are inadequate for transient servers because applications typically assume continuous resource availability. This thesis presents research in distributed systems design that treats transiency as a first-class design principle. I show that combining transiency-specific fault-tolerance mechanisms with resource management policies to suit application characteristics and requirements, can yield significant cost and performance benefits. These mechanisms and policies have been implemented and prototyped as part of software systems, which allow a wide range of applications, such as interactive services and distributed data processing, to be deployed on transient servers, and can reduce cloud computing costs by up to 90\%. This thesis makes contributions to four areas of computer systems research: transiency-specific fault-tolerance, resource allocation, abstractions, and resource reclamation. For reducing the impact of transient server revocations, I develop two fault-tolerance techniques that are tailored to transient server characteristics and application requirements. For interactive applications, I build a derivative cloud platform that masks revocations by transparently moving application-state between servers of different types. Similarly, for distributed data processing applications, I investigate the use of application level periodic checkpointing to reduce the performance impact of server revocations. For managing and reducing the risk of server revocations, I investigate the use of server portfolios that allow transient resource allocation to be tailored to application requirements. Finally, I investigate how resource providers (such as cloud platforms) can provide transient resource availability without revocation, by looking into alternative resource reclamation techniques. I develop resource deflation, wherein a server\u27s resources are fractionally reclaimed, allowing the application to continue execution albeit with fewer resources. Resource deflation generalizes revocation, and the deflation mechanisms and cluster-wide policies can yield both high cluster utilization and low application performance degradation

Cargo revenue management for space logistics

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 79-82).This thesis covers the development of a framework for the application of revenue management, specifically capacity control, to space logistics for use in the optimization of mission cargo allocations, which in turn affect duration, infrastructure availability, and forward logistics. Two capacity control algorithms were developed; the first is based on partitioning of Monte Carlo samples while the second is based on bid-pricing with high-frequency price adjustments. The algorithms were implemented in Java as a plugin module to SpaceNet 2.0, an existing integrated modeling and simulation tool for space logistics. The module was tested on a lunar exploration concept which emphasizes global exploration of the Moon using mobile infrastructure. Results suggest that revenue management produces better capacity allocations in shorter duration missions, while producing nominal capacity allocations (i.e. those in the deterministic case) in the long run.by Nii A. Armar.S.M

Integrated vehicle dispatching for container terminal

Ph.DDOCTOR OF PHILOSOPH