With the advent of cheap and available sensors, there is a need for intelligent sensor selection and placement for various purposes. While previous research was focused on the most efficient sensor networks, we presen...
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ISBN:
(纸本)9781510650893;9781510650886
With the advent of cheap and available sensors, there is a need for intelligent sensor selection and placement for various purposes. While previous research was focused on the most efficient sensor networks, we present a new mathematical framework for efficient and resilient sensor network installation. Specifically, in this work we formulate and solve a sensor selection and placement problem when network resilience is also a factor in the optimization problem. Our approach is based on the binary linear programming problem. The generic formulation is probabilistic and applicable to any sensor types, line-of-site and non-line-of-site, and any sensor modality. It also incorporates several realistic constraints including finite sensor supply, cost, energy consumption, as well as specified redundancy in coverage areas that require resilience. While the exact solution is computationally prohibitive, we present a fast algorithm that produces a near-optimal solution that can be used in practice. We show how such formulation works on 2D examples, applied to infrared (IR) sensor networks designed to detect and track human presence and movements in a specified coverage area. Analysis of coverage and comparison of sensor placement with and without resilience considerations is also performed.
The problem of precisely computing the worst-case blocking time that tasks may experience is one of the fundamental issues of schedulability analysis of real-time applications. While exact methods have been proposed f...
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The problem of precisely computing the worst-case blocking time that tasks may experience is one of the fundamental issues of schedulability analysis of real-time applications. While exact methods have been proposed for more sophisticated protocols, the problem is indeed complex in case of the Priority Inheritance Protocol, even restricting the attention to uniprocessor systems, non-nested resource accesses, and non-self-suspending tasks. Besides a very simple method leading in general to loose upper bounds, only one algorithm of exponential complexity has been so far reported in literature to tighten such bounds. In this article, we describe a novel approach which, leveraging an operational research technique for modeling the problem, computes the same tight bounds in polynomial time. We then discuss the scenarios in which, assuming no conditional statements in the tasks' code, the computed bounds derive from an actually impossible blocking chain, and we refine the initial model to more precisely compute the worst-case blocking times for any task set in any possible operating condition.
This article presents a bridge between topology optimization (TO) and additive manufacturing. On the one hand, it presents an algorithm that considers the design variables as binary and then solves the problem by bina...
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This article presents a bridge between topology optimization (TO) and additive manufacturing. On the one hand, it presents an algorithm that considers the design variables as binary and then solves the problem by binary linear programming to optimize a ferromagnetic core. On the other hand, a 3-D printing process is developed to manufacture the shapes obtained by TO. Finally, these magnetic parts are characterized through electrical measurements.
Conceptual and relational data models of online transaction processing (OLTP) applications are usually created and maintained following the principle of normalization, which implies avoidance of redundancy. Data retri...
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Conceptual and relational data models of online transaction processing (OLTP) applications are usually created and maintained following the principle of normalization, which implies avoidance of redundancy. Data retrieval from a disk-based normalized relational database often requires complex and inefficient queries that may cause noticeable performance issues when executed on larger volumes of data. Computer professionals sometimes intentionally trade off the strict normal form to optimize data retrieval queries through error-prone manual tuning and denormalization. We propose a fully automatic optimization approach, based on data redundancy, that relies on a formal cost-benefit model. We prove that finding the optimal level of data redundancy, for given workload statistics, is an NP-Complete optimization problem. A detailed reduction of the problem to binary linear programming is presented in the paper. The proposed optimization approach was evaluated using the TPCE benchmark for OLTP systems. The evaluation has shown that the proposed optimization approach is highly scalable, and that it can be efficiently applied to real-life relational data models.
Every year about one third of the food production intended for humans gets lost or wasted. This wastefulness of resources leads to the emission of unnecessary greenhouse gas, contributing to global warming and climate...
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Every year about one third of the food production intended for humans gets lost or wasted. This wastefulness of resources leads to the emission of unnecessary greenhouse gas, contributing to global warming and climate change. The solution proposed by the SORT project is to "recycle'' the surplus of food by reconditioning it into animal feed or fuel for biogas/biomass power plants. In order to maximize the earnings and minimize the costs, several choices must be made during the reconditioning process. Given the extremely complex nature of the process, Decision Support Systems (DSSs) could be helpful to reduce the human effort in decision making. In this paper, we present a DSS for food recycling developed using two approaches for finding the optimal solution: one based on binary linear programming (BLP) and the other based on Answer Set programming (ASP), which outperform our previous approach based on Constraint Logic programming (CLP) on Finite Domains (CLP(FD)). In particular, the BLP and the CLP(FD) approaches are developed in (ECLPSe)-P-i, a Prolog system that interfaces with various state-of-the-art Mathematical and Constraint programming solvers. The ASP approach, instead, is developed in clingo. The three approaches are compared on several synthetic datasets that simulate the operative conditions of the DSS.
Clustering addresses the problem of assigning similar objects to groups. Since the size of the clusters is often constrained in practical clustering applications, various capacitated clustering problems have received ...
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Clustering addresses the problem of assigning similar objects to groups. Since the size of the clusters is often constrained in practical clustering applications, various capacitated clustering problems have received increasing attention. We consider here the capacitated p-median problem (CPMP) in which p objects are selected as cluster centers (medians) such that the total distance from these medians to their assigned objects is minimized. Each object is associated with a weight, and the total weight in each cluster must not exceed a given capacity. Numerous exact and heuristic solution approaches have been proposed for the CPMP. The state-of-the-art approach performs well for instances with up to 5,000 objects but becomes computationally expensive for instances with a much larger number of objects. We propose a matheuristic with new problem decomposition strategies that can deal with instances comprising up to 500,000 objects. In a computational experiment, the proposed matheuristic consistently outperformed the state-of-the-art approach on medium-and large-scale instances while having similar performance for small-scale instances. As an extension, we show that our matheuristic can be applied to related capacitated clustering problems, such as the capacitated centered clustering problem (CCCP). For several test instances of the CCCP, our matheuristic found new best-known solutions.
In this study, we deal with the problem of scheduling charging periods of electrical vehicles (EVs) to satisfy the users' demands for energy consumption as well as to optimally utilize the available power. We assu...
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In this study, we deal with the problem of scheduling charging periods of electrical vehicles (EVs) to satisfy the users' demands for energy consumption as well as to optimally utilize the available power. We assume three-phase EV charging stations, each equipped with two charging ports (links) that can serve up to two EVs in the scheduling period but not simultaneously. Considering such a specification, we propose an on-off scheduling scheme wherein control over an energy flow is achieved by flexibly switching the ports in each station on and off in a manner such as to satisfy the energy demand of each EV, flatten the high energy-consuming load on the whole farm, and to minimize the number of switching operations. To satisfy these needs, the on-off scheduling scheme is formulated in terms of a binary linear programming problem, which is then extended to a quadratic version to incorporate the smoothness constraints. Various algorithmic approaches are used for solving a binary quadratic programming problem, including the Frank-Wolfe algorithm and successive linear approximations. The numerical simulations demonstrate that the latter is scalable, efficient, and flexible in a charging procedure, and it shaves the load peak while maintaining smooth charging profiles.
Resiliency of distribution systems under extreme operating conditions is critical, especially when the utility is not available. With the large-scale deployment of distributed resources, it becomes possible to restore...
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Resiliency of distribution systems under extreme operating conditions is critical, especially when the utility is not available. With the large-scale deployment of distributed resources, it becomes possible to restore critical loads with local non-utility resources. Distribution system operators (DSOs) need to determine the critical loads to be restored, considering limited resources and distribution facilities. Several studies on resiliency have been conducted for the restoration of distribution systems. However, the inherent asynchronous characteristic of the information availability has not been incorporated. With incomplete and asynchronous information, decisions may be made that result in underutilization of generation resources. In this paper, a new distribution system restoration approach is proposed, considering uncertain devices and associated asynchronous information. It uses a two-module architecture that efficiently optimizes restoration actions using a binary linear programming model and evaluates their feasibility with unbalanced optimal power flow. Networked microgrids are included in the model. The IEEE 123-node test feeder is used for validation. The results show that asynchronous messages may affect the restoration actions significantly and the impacts can be mitigated by the proposed decision support tool for the DSOs.
作者:
Ozcan, UgurGazi Univ
Engn Fac Dept Ind Engn TR-06570 Ankara Turkey Gazi Univ
Dept Wood Prod Ind Engn Technol Fac TR-06500 Ankara Turkey
Parallel assembly lines are special flow type production systems where two or more neighbouring assembly lines are balanced together. In some industries such as automobile and household appliances industries, neighbou...
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Parallel assembly lines are special flow type production systems where two or more neighbouring assembly lines are balanced together. In some industries such as automobile and household appliances industries, neighbouring lines are frequently used. In parallel assembly lines, a setup can be defined as a set of activities to perform a task, such as movements, selecting a new assembly tool and/or taking a position in order to using the most suitable installation procedure. Setups in a workstation on the same line may occur in two ways;forward setup and backward setup. And also, a setup in a workstation on two neighbouring lines may occur. In this study, this is named as line-switching setups. In this paper, the problem of balancing and scheduling tasks in parallel assembly lines with sequence-dependent setup times (PALBPS) is introduced and characterised. A binarylinear mathematical programming (BLP) model and a simulated annealing (SA) algorithm are proposed to model and solve the PALBPS. A computational study is performed in order to test the proposed SA algorithm. The computational results show that the proposed algorithm is very effective and successful for the PALBPS.
The multi-satellite image acquisition scheduling problem is traditionally seen as a complex optimization problem containing a generic objective function that represents the priority structure of the satellite operator...
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The multi-satellite image acquisition scheduling problem is traditionally seen as a complex optimization problem containing a generic objective function that represents the priority structure of the satellite operator. However, the majority of literature neglect the collective and contemporary effect of factors associated with the operational goal in the objective function, i.e., uncertainty in cloud cover, customer priority, image quality criteria, etc. Consequently, the focus of the article is to integrate a real-time scoring approach of imaging attempts that considers these aspects. This is accomplished in a multi-satellite planning environment, through the utilization of the multi-criteria decision making (MCDM) models, Elimination and Choice Expressing Reality (ELECTRE-III) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and the formulation of a binary linear programming model. The two scoring approaches belong to different model classes of MCDM, respectively an outranking approach and a distance to ideal point approach, and they are compared with a naive approach. Numerical experiments are conducted to validate the models and illustrate the importance of criteria neglected in previous studies. The results demonstrate the customized behaviour allowed by MCDM methods, especially the ELECTRE-III approach.
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