In this work, we demonstrate how power system capacity expansion models can understate the stochastic effects of thermal outages when considering resource availabilities on an hourly expected value basis, yielding sys...
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ISBN:
(纸本)9798350372793;9798350372786
In this work, we demonstrate how power system capacity expansion models can understate the stochastic effects of thermal outages when considering resource availabilities on an hourly expected value basis, yielding system designs with multiple orders of magnitude more shortfall risk than stated adequacy targets. We develop a novel approximation approach to efficiently endogenize awareness of this risk in a deterministic, linear capacity expansion framework. We compare this approach to exogenous tuning of an energy reserve margin, the leading alternative method to compensate for unmodeled probabilistic shortfall risk. Empirical results from a test system show that the new endogenous method cost-effectively meets all regional reliability targets with a single optimization solve, and produces a near-identical system design as the incumbent method without the need for repeated re-optimizations to find a n appropriate reserve level. The endogenous method may also use iterative reoptimizations to further improve solution quality, although these incremental benefits were modest in the system studied.
Nowadays, the assimilation of workers with disabilities is recognized as both a challenge and an opportunity in terms of staff diversity and of corporate social responsibility of different organizations. In this conte...
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ISBN:
(纸本)9798331540982;9798331540975
Nowadays, the assimilation of workers with disabilities is recognized as both a challenge and an opportunity in terms of staff diversity and of corporate social responsibility of different organizations. In this context, achieving a fair and efficient task distribution is an essential aspect of guaranteeing the effective and full involvement of all the workers, independently from their cognitive and physical capacities. A task distribution model is a method for assigning and managing tasks within a work team, organization or group of persons, usually aiming to optimize productivity and efficiency. The main objective is to ensure that the correct tasks are assigned to the appropriate persons, taking into account their abilities, experience and current work charge. Task distribution models are particularly relevant when including people with disabilities, where they must be flexible and adaptable enough to guarantee the full integration of all the team members. In this work, we present a mathematical programming model for task distribution in the context of hiring people with disabilities. We discuss a practical case study at the Intendencia de Montevideo (IdeM - the municipality of the city of Montevideo), as part of a project for improving the integration of people with disabilities in its staff (currently the IdeM staff only includes about 1.5% of employees with disabilities;while applicable laws state that this percentage should raise to at least 4%). We present the results of applying the mathematical programming model, comparing the results against manual assignments. Different objective functions are also taken into account, as the various stakeholders also have specific goals that are important to take into account. The results show that mathematical programming models are an effective tool that can be used to support decision making and improve the integration of workers with disabilities in an organization.
The Bicycle Sharing Systems (BSS) have emerged as a sustainable and convenient mode of urban transportation, providing an alternative to traditional commuting methods. Despite its benefits, BSS's efficient managem...
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ISBN:
(数字)9783031774263
ISBN:
(纸本)9783031774256;9783031774263
The Bicycle Sharing Systems (BSS) have emerged as a sustainable and convenient mode of urban transportation, providing an alternative to traditional commuting methods. Despite its benefits, BSS's efficient management and optimization face inherent challenges, ranging from system demand variations to station capacity constraints. This paper addresses a tactical problem in BSS, namely the districting problem. The districting BSS problem aims to find a network configuration where the stations are allocated to cluster centres so that each cluster meets balance constraints. The problem is modelled as an integer programming problem. In a previous work [4], we implemented a matheuristic based on a local search algorithm which selects the centre of each cluster of stations. Then, a mathematical solver solves the allocation of the stations to the centres, considering balancing constraints. In that paper, we limited our local search to choosing a cluster centre within predefined clusters to reduce the search space. This paper investigates the effect of the clustering strategy on the local search's performance. Thus, this paper implements a clustering strategy to provide the local search algorithm with better clusters or grids. We try the well-known k-means algorithm to provide our local search matheuristic algorithm with different grids to seek on. The obtained results significantly improve the algorithms' performance compared to the arbitrary pre-defined grid used in our previous work. Also, results show that some grid designs tend to be better than others and, thus, impact the final results.
Trains have a constrained schedule and are not available on demand. If a malfunction is detected, the moment and the place to fix the problem while keeping the network at its optimal use may not be easy to find, given...
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ISBN:
(纸本)9783031686337;9783031686344
Trains have a constrained schedule and are not available on demand. If a malfunction is detected, the moment and the place to fix the problem while keeping the network at its optimal use may not be easy to find, given that there will be multiple problems throughout the network. Deciding of the maintenance requests the solution of a scheduling problem. The goal of this research is to provide an efficient solution to this problem. To achieve this aim, we develop a mixed integer linear programming model, a constraint programming model and multiple heuristic algorithms such as a Local Branching (LB) over the mixed integer linear programming model and a Variable Partitioning Local Search (VPLS) over the constraint programming model.
This paper presents a comprehensive Integer programming (IP) model designed to optimize the robotic kitting process in industrial automotive settings. Robotic kitting, involving the efficient assembly and preparation ...
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ISBN:
(纸本)9783031586750;9783031586767
This paper presents a comprehensive Integer programming (IP) model designed to optimize the robotic kitting process in industrial automotive settings. Robotic kitting, involving the efficient assembly and preparation of kits using automated systems, plays a crucial role in modern manufacturing facilities. The proposed IP model considers various key aspects related to the cycle time, including preparation time for kit boxes on Automated Guided Vehicles (AGVs), picking time with Autonomous Mobile Robots (AMRs), image acquisition and processing time, AMR and AGV travel times, and removal time of empty component bins by AMRs. The objective is to minimize the energy consumption of AGVs in the kitting process, enhancing operational efficiency while ensuring accurate kit assembly. The formulation of the mathematical programming model allows for the consideration of flow-related activities, improving the adaptability and flexibility of the kitting process to varying order patterns. Numerical experiments demonstrate the effectiveness of the model in achieving key insights into AGVs' energy demand, contributing to advancements in mapping this process in industrial automation and logistics.
In this paper, we present methodologies for optimal selection for renewable energy sites under a different set of constraints and objectives. We consider two different models for the site-selection problem - coarse-gr...
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ISBN:
(纸本)9780998133171
In this paper, we present methodologies for optimal selection for renewable energy sites under a different set of constraints and objectives. We consider two different models for the site-selection problem - coarse-grained and fine-grained, and analyze them to find solutions. We consider multiple different ways to measure the benefits of setting up a site. We provide approximation algorithms with a guaranteed performance bound for two different benefit metrics with the coarse-grained model. For the fine-grained model, we provide a technique utilizing Integer Linear Program to find the optimal solution. We present the results of our extensive experimentation with synthetic data generated from sparsely available real data from solar farms in Arizona.
In the face of increasing billion-dollar weather events in the United States, grid resilience has become a central issue for electric utilities, operators and customers. Hence, this work presents service restoration a...
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ISBN:
(纸本)9798350381849;9798350381832
In the face of increasing billion-dollar weather events in the United States, grid resilience has become a central issue for electric utilities, operators and customers. Hence, this work presents service restoration and data-enhanced visualization for improved decision-making and resilience in the distribution system. First, we present an exploratory visual interface that increases situational awareness in the distribution system by allowing system operators to glean actionable intelligence from historical outage data. In addition, this study proposes a prescriptive service restoration framework that allows distribution system operators to manage outages in a proactive manner by leveraging outage forecasts to minimize out-of-service loads in the event of predicted outages in the distribution network. The proposed framework is formulated as a mixed integer linear programming problem and is validated using a modified IEEE 13-node test feeder. Results show a decrease in the impact of predicted outages as a result of implementing the topology optimization and service restoration framework.
Production of a digital photograph that reproduces the original scene as accurately as possible requires solution of the problem of color correction, i.e., the challenge of finding find a mapping converting the coordi...
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The advancement of renewable energy(RE)represents a pivotal strategy in mitigating climate change and advancing energy transition efforts.A current of research pertains to strategies for fostering RE *** the frequentl...
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The advancement of renewable energy(RE)represents a pivotal strategy in mitigating climate change and advancing energy transition efforts.A current of research pertains to strategies for fostering RE *** the frequently proposed approaches,employing optimization models to facilitate decision-making stands out *** from an extensive dataset comprising 32806 literature entries encompassing the optimization of renewable energy systems(RES)from 1990 to 2023 within the Web of Science database,this study reviews the decision-making optimization problems,models,and solution methods thereof throughout the renewable energy development and utilization chain(REDUC)*** review also endeavors to structure and assess the contextual landscape of RES optimization modeling *** evidenced by the literature review,optimization modeling effectively resolves decisionmaking predicaments spanning RE investment,construction,operation and maintenance,and ***nantly,a hybrid model that combines prediction,optimization,simulation,and assessment methodologies emerges as the favored approach for optimizing RES-related deci*** primary framework prevalent in extant research solutions entails the dissection and linearization of established models,in combination with hybrid analytical strategies and artificial intelligence *** advancements within modeling encompass domains such as uncertainty,multienergy carrier considerations,and the refinement of spatiotemporal *** the realm of algorithmic solutions for RES optimization models,a pronounced focus is anticipated on the convergence of analytical techniques with artificial intelligence-driven ***,this study serves to facilitate a comprehensive understanding of research trajectories and existing gaps,expediting the identification of pertinent optimization models conducive to enhancing the efficiency of REDUC development endeavors.
This paper presents a strategy based on binary labelling of nodes for the creation of anti-loop formulations from existing strategies. This strategy prevents by default the formation of odd cycles, therefore it can ha...
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This paper presents a strategy based on binary labelling of nodes for the creation of anti-loop formulations from existing strategies. This strategy prevents by default the formation of odd cycles, therefore it can have important role in iterative procedures based on generating subtour elimination constraints. It can also be used to modify the classic strategies used in problems associated to graphs. In this paper we focus on this last application. The behavior of this strategy is analyzed with two problems associated with graphs, the Asymmetric Traveling Salesman Problem (ATSP) and the Steiner Problem, where two configurations that modify the Miller-Tucking-Zemlig proposal to avoid cycles are compared. The experimental analysis shows that this strategy keep a good convergence, highlighting its use for the Steiner problem.
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