This paper introduces a two-stage (offline and online) artificial neural network (ANN) driven constraint creator model to improve the computational quality of day-ahead unit commitment (DAUC) in power systems. The DAU...
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This paper introduces a two-stage (offline and online) artificial neural network (ANN) driven constraint creator model to improve the computational quality of day-ahead unit commitment (DAUC) in power systems. The DAUC is crucial for planning 24-hour operations and complex bid clearing through mixed-integerlinear programs (MILP). However, slow convergence is common due to system complexity. Machine learning (ML) based methods have been used to enhance MILP-DAUC. Nonetheless, they can lead to sub-optimality and infeasibility. To overcome these challenges, (1) this paper proposes in the offline stage the ANN-generators subset (AGS) that can predict part of the optimal MILP-DAUC decisions using an ANN model. Online, only ML-generated decisions of AGS are used to form the ANN-driven constraints to enhance the main MILP-DAUC, forming the proposed ANN-MILP-DAUC method. (2) A feasibility handling process is proposed to retain the infeasible ML states to be optimized by the main MILP-DAUC formulation. (3) The proposed model issues an artificial factor that provides the percentage of generators accurately predicted and used as an ML training performance metric. The ANN model was trained using optimal MILP-DAUC solutions. Test results on IEEE 14-bus and 118-bus systems reported solution time reductions of 61.43% and 70.1%, respectively. Larger Polish 2383-bus, 3012-bus, and Ontario systems reported time reductions in the range of 33% compared with the main MILP-DAUC method using MOSEK (TM), a commercial solver. No degradation in the optimal solution was observed for all test systems, and the proposed method provides a lower-objective solution for the same running time, leading to better solutions.
The Internet of Things paradigm paves the way toward automating real-world tasks, especially in intensive domains. Nonetheless, the criticality of the intensive tasks to be automated, such as video recognition, speech...
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The Internet of Things paradigm paves the way toward automating real-world tasks, especially in intensive domains. Nonetheless, the criticality of the intensive tasks to be automated, such as video recognition, speech recognition, or data prediction, subjects them to very strict Quality of Service (QoS) requirements, such as short response times. To achieve these low response times, it is key to optimally place the microservices in a Computing Continuum infrastructure, considering their interactions in the form of workflows, their execution times, and the latencies of the underlying network fabric. While there are some proposals in the current state of the art to optimally place the application's microservices, these proposals do not consider the effects that multi-core processing can have over the microservices' execution times in their model, nor have their model compared with emulated network testbeds. This work proposes the Multi-core Microservice Placement Optimizer, a system that advances the state of the art by considering multi-core processing, making use of both the parallelization characteristics of microservices and the cores available at the computing devices. Our evaluation over a smart city case study, based on a real application and a fog computing testbed, shows that MUMIPLOP's model is more accurate than single-core models, and yields shorter response time than state-of-the-art techniques, enhancing the QoS obtained in the Computing Continuum.
In this paper, we propose novel mixed-integer linear programming (MIP) formulations to model decision problems posed as influence diagrams. We also present a novel heuristic that can be employed to warm start the MIP ...
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The advancement of the Intelligent Transport Systems (ITS) and the emerging Connected and Automated Vehicles (CAVs) technology are acknowledged to hold a great potential to mitigate challenging problems in the current...
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The advancement of the Intelligent Transport Systems (ITS) and the emerging Connected and Automated Vehicles (CAVs) technology are acknowledged to hold a great potential to mitigate challenging problems in the current transportation networks. Particularly, a proper traffic control strategy with a precise vehicular movement control scheme can alleviate the congestion and improve the safety and efficiency of the traffic. This paper proposes a novel bi-level control framework that combines a design of traffic signal timings at a network level, and a detailed trajectory control policy for individual vehicles at a link-level within a network of CAVs. We develop a group-based longitudinal trajectory planning scheme to coordinate vehicular movements at the lower level of our framework while abiding by the signal operations along with end-to-end vehicle routing decisions from the upper network level optimization. This joint and mutual interaction between the two different control strategies in the urban signalized corridors is complex and can significantly affect the overall network's performance, nevertheless has not been explored previously in the literature. The proposed framework enables such studies where we derive an efficient algorithm that iteratively solves the mixed-integer linear programming (MILP) and linearprogramming models in each link at the lower and over the network at the upper levels, respectively. Numerical results show the effectiveness of the proposed joint control framework in network performance regarding the average travel time, queue formation and dissipation across the network.
Welding is the most critical operation in the shipbuilding process and has a significant influence on the production cost and quality of ships. Therefore, the welding operation must be optimised. This paper presents a...
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Welding is the most critical operation in the shipbuilding process and has a significant influence on the production cost and quality of ships. Therefore, the welding operation must be optimised. This paper presents a real-world welding gantry robot scheduling problem at shipyards, in which three gantry robots function in parallel. Welding gantry robots cannot cross each other and should operate over a certain distance to avoid collisions. To minimise the makespan, the welding tasks given by line segments should be evenly distributed among the three gantry robots. The welding tasks assigned to each robot should be optimally sequenced to minimise the completion time, including the waiting time required to prevent collisions with neighbouring robots. In addition, long welding edges are split, and the split small length edges are assigned to the gantry robots. This paper proposes a mixed-integer linear programming model, three-stage solution approach, and variable neighbourhood search algorithm to solve this problem. Experimental tests conducted on 20 real problem instances revealed that the proposed approach can reduce the makespan by 14% on average when compared with the conventional method.
This article addresses a short-term mining planning problem. There are four objectives to be minimized: the deviations in grades and ore proportion in particle size ranges of the plant goals, the deviation in the wast...
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This article addresses a short-term mining planning problem. There are four objectives to be minimized: the deviations in grades and ore proportion in particle size ranges of the plant goals, the deviation in the waste mass to achieve the stripping rate, and the number of truck trips between mining fronts and discharges. The problem was solved through the lexicographical goal programming (LGP) method, which generates solutions that can guarantee a more comprehensive analysis of the decision-making process. The LGP method was tested by using several scenarios of a Brazilian mining company. These scenarios differ in the number of excavators and the tolerances concerning meeting the plants' ore grades. In the results, the impact on the values of the other objectives is analysed of varying the number of excavators and the tolerances in the plants' grade targets.
This paper proposes a new methaheurstic to solve the flow shop scheduling problem which is considered as an NP-hard problem for relatively high dimensions. The flow shop scheduling problems are commonly encountered in...
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This paper proposes a new methaheurstic to solve the flow shop scheduling problem which is considered as an NP-hard problem for relatively high dimensions. The flow shop scheduling problems are commonly encountered in many industrial applications and manufacturing systems. For the purpose, a mixedintegerlinearprogramming model is presented to articulate the relationship between the objective function and the constraints of the problem. A proposed hybrid greedy algorithm based on the Nash equilibrium concept and the genetic operators is an attempt to outperform the classical algorithms frequently employed to approach the optimal solution of scheduling problems. In order to minimize the makespan criterion, various computational experiments were conducted for different size of the problem. Furthermore, a comparative study is performed to assess the developed methaheuristic against other algorithms. Simulations have shown that the proposed procedure is the most effective and robust.
Designers of utility-scale solar plants with storage, seeking to maximize some aspect of plant performance, face multiple challenges. In many geographic locations, there is significant penetration of photovoltaic gene...
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Designers of utility-scale solar plants with storage, seeking to maximize some aspect of plant performance, face multiple challenges. In many geographic locations, there is significant penetration of photovoltaic generation, which depresses energy prices during the hours of solar availability. An energy storage system affords the opportunity to dispatch during higher-priced time periods, but complicates plant design and dispatch decisions. Solar resource variability compounds these challenges, because determining optimal system sizes requires simultaneously considering how the plant will be operated under the imposed market and weather conditions. We develop an approach to analyze the economic performance of hybrid and single-technology solar power plants, which incorporates optimal dispatch, and considers the expected electricity market and weather conditions. We utilize the System Advisor Model software package to simulate the operation of multiple renewable generation and energy storage technologies, in conjunction with hourly-fidelity generation decisions determined by a revenue-maximizing, mixed-integerlinear program. We show that, under our assumed market and weather conditions, the lifetime benefit-to-cost ratio can be improved by 6 to 19 percent, relative to a baseline design without optimizing, and that a concentrating solar power with thermal energy storage design produces significantly more energy per year, but is less profitable under our cost assumptions.
We present a two-phase methodology to address the problem of optimally deploying indoor wireless local area networks. In the first phase, we use Helmholtz's equation to simulate electromagnetic fields in a typical...
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We present a two-phase methodology to address the problem of optimally deploying indoor wireless local area networks. In the first phase, we use Helmholtz's equation to simulate electromagnetic fields in a typical environment such as an office floor. The linear system which results from the discretization of this partial differential equation is solved with a state-of-the-art library for sparse linear algebra. In the second phase, we formulate the network deployment problem in the setting of binary linearprogramming. This formulation employs the simulator output as input parameters, and jointly optimizes the number of access points, their locations, and their emission channels. We prove that this optimization problem is NP-Hard, and use mathematical programming based techniques and heuristics to solve it. We present numerical experiments on medium-sized buildings.
In this paper, an integrated harvest and production planning problem in the olive oil industry is addressed. The aim of the paper is to develop and optimize a mathematical model that integrates both olive harvest and ...
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In this paper, an integrated harvest and production planning problem in the olive oil industry is addressed. The aim of the paper is to develop and optimize a mathematical model that integrates both olive harvest and olive oil production process. The objective is to maximize the total profit while determining quantity of olives harvested from several olive groves, quantity of olives purchased from external farmers, quantity of olive oil produced, and by-product management to handle hazardous effects of olive oil production. The problem is formulated as a mixedintegerlinearprogramming model (MILP). Maximization of profit consists of two components;total sales revenue and total cost including harvesting, purchasing, fixed and variable processing costs. Constraints on the system include harvest planning, harvest capacity, production planning, and processing constraints. The proposed MILP model incorporates several distinguishing characteristics of the problem such as ripeness of olives, olive oil quality, organic and conventional farming, and by-product management. A numerical experiment based on a real-world case study was presented to verify the effectiveness of the developed model. The results show that simultaneously considering harvesting and production processes can significantly assist the profitability of the olive oil supply chain. A scenario analysis is conducted by extending the base model to explore olive loss in the olive groves which can occur due to the severe climatic conditions.
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