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The design of a chilled water plant involves selecting the size and type of chillers to be employed and determining the operating strategy. The types may include both gas engine and electric motor driven chillers. The issues that have to be considered in the selection problem are to incorporate external and internal factors into the decision making. External factors may include the utility rate schedules, the cooling load profile, and the outdoor temperature profile. Internal factors may include the chiller performance characteristics, initial and maintenance costs, and the chiller(s) operating strategy. A mathematical model representing the chilled water plant design problem is developed. The problem is approached as a mixed integer linear programming problem where non-linear chiller performance curves are transformed into linear constraints through the use of integer variables. The optimization task is to select the best cooling plant configuration and operating strategy to minimize life cycle cost. A solution procedure is developed which decomposes the optimization problem to reduce extensive computation time. Two case studies are provided to investigate the implementation of the mathematical model. / Master of Science

The petrochemical Industry is based upon the production of chemicals from petroleum and also deals with chemicals manufactured from the by products of petroleum refinery. At the preliminary stages of chemical plant development and design, the choice of chemical process route is the key design decision. In the past, economics were the most important criterion in choosing the chemical process route. Modified studies imply that the two of the important planning objectives for a petrochemical industry, environmental risk and the industrial safety involved in the development. For the economic evaluation of the industry, and for the proposed final chemicals products in the development, simple and clear economic indicators are needed to be able to indicate an overall economic gain in the development. Safety, as the second objective, is considered in this study as the risk of chemical plant accidents. Risk, when used as an objective function, has to have a simple quantitative form to be easily evaluated for a large number of possible plants in the petrochemical network. The simple quantitative form adopted is a safety index that enables the number of people affected by accidents resulting in chemical releases to be estimated. Environmental issues have now become important considerations due to the potential harmful impacts produced by chemical releases. In this study third objective of planning petrochemical industry was developed by involving environmental considerations and environmental risk index. Indiana Relative Chemical Hazard Score (IRCHS) was used to allow chemical industries routes to be ranked by environmental hazardous. The focus of this work is to perform early planning and decision-making for a petrochemical plants network for maximum economical gain, minimum risk to people from possible chemical accidents and minimum environmental risk. The three objectives, when combined with constraints describing the desired or the possible structure of the industry, will form an optimization model. For this study, the petrochemical planning model consists of a Mixed Integer Linear Programming (MILP) model to select the best routes from the basic feedstocks available in Kuwait -as a case study- to the desired final products with multiple objective functions. The economic, safety and environmental risk objectives usually have conflicting needs. The presence of several conflicting objectives is typical when planning. In many cases, where optimization techniques are utilized, the multiple objectives are simply aggregated into one single objective function. Optimization is then conducted to get one optimal result. This study, which is concerned with economic and risk objectives, leads to the identification of important factors that affecting the building-up of environmental management system for petrochemical industry. Moreover, the procedure of modelling and model solution can be used to simplify the decision-making for complex or large systems such as the petrochemical industry. It presents the use of simple multiple objective optimization tools within a petrochemical planning tool formulated as a mixed integer linear programming model. Such a tool is particularly useful when the decision-making task must be discussed and approved by officials who often have little experience with optimization theories

The petrochemical Industry is based upon the production of chemicals from petroleum and also deals with chemicals manufactured from the by products of petroleum refinery. At the preliminary stages of chemical plant development and design, the choice of chemical process route is the key design decision. In the past, economics were the most important criterion in choosing the chemical process route. Modified studies imply that the two of the important planning objectives for a petrochemical industry, environmental risk and the industrial safety involved in the development. For the economic evaluation of the industry, and for the proposed final chemicals products in the development, simple and clear economic indicators are needed to be able to indicate an overall economic gain in the development. Safety, as the second objective, is considered in this study as the risk of chemical plant accidents. Risk, when used as an objective function, has to have a simple quantitative form to be easily evaluated for a large number of possible plants in the petrochemical network. The simple quantitative form adopted is a safety index that enables the number of people affected by accidents resulting in chemical releases to be estimated. Environmental issues have now become important considerations due to the potential harmful impacts produced by chemical releases. In this study third objective of planning petrochemical industry was developed by involving environmental considerations and environmental risk index. Indiana Relative Chemical Hazard Score (IRCHS) was used to allow chemical industries routes to be ranked by environmental hazardous. The focus of this work is to perform early planning and decision-making for a petrochemical plants network for maximum economical gain, minimum risk to people from possible chemical accidents and minimum environmental risk. The three objectives, when combined with constraints describing the desired or the possible structure of the industry, will form an optimization model. For this study, the petrochemical planning model consists of a Mixed Integer Linear Programming (MILP) model to select the best routes from the basic feedstocks available in Kuwait -as a case study- to the desired final products with multiple objective functions. The economic, safety and environmental risk objectives usually have conflicting needs. The presence of several conflicting objectives is typical when planning. In many cases, where optimization techniques are utilized, the multiple objectives are simply aggregated into one single objective function. Optimization is then conducted to get one optimal result. This study, which is concerned with economic and risk objectives, leads to the identification of important factors that affecting the building-up of environmental management system for petrochemical industry. Moreover, the procedure of modelling and model solution can be used to simplify the decision-making for complex or large systems such as the petrochemical industry. It presents the use of simple multiple objective optimization tools within a petrochemical planning tool formulated as a mixed integer linear programming model. Such a tool is particularly useful when the decision-making task must be discussed and approved by officials who often have little experience with optimization theories

The primary design goal of many embedded systems for multimedia applications is usually meeting the performance requirement at a minimum cost. In this thesis, we proposed two different ILP based approaches for hardware/software (HW/SW) partitioning and pipelined scheduling of embedded systems for multimedia applications. One ILP approach solves the HW/SW partitioning and pipelined scheduling problem simultaneously. Another ILP approach separates the HW/SW partitioning and pipelined scheduling problem into two phases. The first phase is focusing on the HW/SW partitioning and mapping problem. Second phase is used to solve the pipelined scheduling problem. The two ILP approaches not only partition and map each computation task of a particular multimedia application onto a component of the heterogeneous multiprocessor architecture, but also schedules and pipelines the execution of these computation tasks while considering communication time. For the first ILP model, the objective is to minimize the total component cost and the number of pipeline stages subject to the throughput constraint. In the second ILP approach, the objective of the first phase and second phase is to minimize the total component cost and the number of pipeline stages subject to the throughput constraint, respectively. Finally, experiments on three real multimedia applications (JPEG Encoder, MP3 Decoder, Wavelet Video Encoder) are used to demonstrate the effectiveness of the proposed approaches.

This thesis project will attempt to answer the question if it is possible to gain performance by using SIMD instructions when generating code for scalar computation. The current trend in processor architecture is to equip the processors with multi-way SIMD units to form so-called throughput cores. This project uses the CELL SPE processor for a concrete implementation. To get good code quality the thesis project continues work on the code generator by Mattias Eriksson and Andrzej Bednarski based on integer linear programming. The code generator is extended to handle generation of SIMD code for 32bit operands. The result show for some basic blocks, positive impact in execution time of the generated schedule. However, further work has to be done to get a feasable run time of the code generator.

The management of large-scale transportation infrastructure is becoming a very complex task for the urban areas of this century which are covering bigger geographic spaces and facing the inclusion of connected and self-controlled vehicles. This new system paradigm can leverage many forms of sensing and interaction, including a high-scale mobile sensing approach. To obtain a high penetration sensing system on urban areas more practical and scalable platforms are needed, combined with estimation algorithms suitable to the computational capabilities of these platforms. The purpose of this work was to develop a transportation framework that is able to handle different kinds of sensing data (e.g., connected vehicles, loop detectors) and optimize the traffic state on a defined traffic network. The framework estimates the traffic on road networks modeled by a family of Lighthill-Whitham-Richards equations. Based on an equivalent formulation of the problem using a Hamilton-Jacobi equation and using a semi-analytic formula, I will show that the model constraints resulting from the Hamilton-Jacobi equation are linear, albeit with unknown integer variables. This general framework solve exactly a variety of problems arising in transportation networks: traffic estimation, traffic control (including robust control), cybersecurity and sensor fault detection, or privacy analysis of users in probe-based traffic monitoring systems. This framework is very flexible, fast, and yields exact results. The recent advances in sensors (GPS, inertial measurement units) and microprocessors enable the development low-cost dedicated devices for traffic sensing in cities, 5 which are highly scalable, providing a feasible solution to cover large urban areas. However, one of the main problems to address is the privacy of the users of the transportation system, the framework presented here is a viable option to guarantee the privacy of the users by design.

No description available.

The rising cost of military aircraft has driven the DoD to increase the utilization of commercial off-the-shelf (COTS) components in new acquisitions. Despite several demonstrated advantages of COTS-based systems, challenges relating to obsolescence arise when attempting to design and sustain such systems using traditional acquisition processes. This research addresses these challenges through the creation of an Agile Systems Engineering framework that is specifically aimed at COTS-based systems. This framework, known as the Cyber-physical Acquisition Strategy for COTS-based Agility-Driven Engineering (CASCADE), amends the traditional systems engineering process through the addition of an "identification phase" during which requirements are balanced against the capabilities of commercially-available components. The CASCADE framework motivates the creation of a new Mixed Integer Linear Programming (MILP) formulation which enables the creation of optimum obsolescence mitigation plans. Using this CASCADE MILP formulation, two sets of experiments are carried out: First, verification experiments demonstrate that the CASCADE MILP conforms to expected trends and agrees with existing results. Next, the CASCADE MILP is applied to a representative set of COTS-based systems in order to determine the appropriate level of obsolescence forecast accuracy, and to uncover new system-level cost-vs-reliability trends associated with COTS component modification.

Enticed by the promise of larger sales and better access to customers, consumer goods compa- nies (CGCs) are increasingly looking to evade traditional retailers and reach their customers directly–with direct-to-customer (DTC) policy. DTC trend has emerged to have major im- pact on logistics operations and distribution channels. It oers significant opportunities for CGCs and wholesale brands to better control their supply chain network by circumventing the middlemen or retailers. However, to do so, CGCs may need to develop their omni-channel strategies and fortify their supply chains parameters, such as fulfillment, inventory flow, and goods distribution. This may give rise to changes in the supply chain network at all strategic, tactical and operational levels. Motivated by recent interests in DTC trend, this master thesis considers the time-evolving supply chain system of an international healthcare company with preordained configuration. The input is bottleneck part of the company’s distribution network and involves 20% ≠ 25% of its total market. A mixed-integer linear programming (MILP) multiperiod optimization model is developed aiming to make tactical decisions for designing the distribution network, or more specifically, for determining the best strategy for distributing the products from manufacturing plant to primary distribution center and/or regional distribution centers and from them to customers. The company has got one manufacturing site (Mfg), one primary distribution center (PDP) and three dierent regional distribution centers (RDPs) worldwide, and the customers can be supplied from dierent plants with various transportation modes on dierent costs and lead times. The company’s motivation is to investigate the possibility of reduction in distribution costs by in-time supplying most of their demand directly from the plants. The model selects the best option for each customer by making trade-os among criteria involving distribution costs and lead times. Due to the seasonal variability and to account the market fluctuability, the model considers the full time horizon of one year. The model is analyzed and developed step by step, and its functionality is demonstrated by conducting experiments on the distribution network from our case study. In addition, the case study distribution network topology is utilized to create random instances with random parameters and the model is also evaluated on these instances. The computational experiments on instances show that the model finds good quality solutions, and demonstrate that significant cost reduction and modality improvement can be achieved in the distribution network. Using one-year actual data, it has been shown that the ratio of direct shipments could substantially improve. However, there may be many factors that can impact the results, such as short-term decisions at operational level (like scheduling) as well as demand fluctuability, taxes, business rules etc. Based on the results and managerial considerations, some possible extensions and final recommendations for distribution chain are oered. Furthermore, an extensive sensitivity analysis is conducted to show the eect of the model’s parameters on its performance. The sensitivity analysis employs a set of data from our case study and randomly generated data to highlight certain features of the model and provide some insights regarding its behaviour.