Bike-sharing systems have become a popular transportation alternative. Unfortunately, station networks are often unbalanced, with some stations being empty, while others being congested. Given the complexity of the un...
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Bike-sharing systems have become a popular transportation alternative. Unfortunately, station networks are often unbalanced, with some stations being empty, while others being congested. Given the complexity of the underlying planning problems to rebalance station inventories via trucks, many mathematical optimizations models have been proposed, mostly focusing on minimizing the unmet demand. This work explores the benefits of two alternative objectives, which minimize the deviation from an inventory interval and a target inventory, respectively. While the concepts of inventory intervals and targets better fit the planning practices of many system operators, they also naturally introduce a buffer into the station inventory, therefore better responding to stochastic demand fluctuations. We report on extensive computational experiments, evaluating the entire pipeline required for an automatized and data-driven rebalancing process: the use of synthetic and real-world data that relies on varying weather conditions, the prediction of demand and the computation of inventory intervals and targets, different reoptimization modes throughout the planning horizon, and an evaluation within a fine-grained simulator. Results allow for unanimous conclusions, indicating that the proposed approaches reduce unmet demand by up to 34% over classical models.
Energy-efficient train timetabling (EETT) is essential to achieve the full potential of energy -efficient train control, which can reduce operating costs and contribute to a reduction in CO2 emissions. This article pr...
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Energy-efficient train timetabling (EETT) is essential to achieve the full potential of energy -efficient train control, which can reduce operating costs and contribute to a reduction in CO2 emissions. This article proposes a bi-objective matheuristic to address the EETT problem for a railway network. To our knowledge, this article is the first to suggest using historical data from train operation to model the actual energy consumption, reflecting the different driving behaviours. The matheuristic employs a genetic algorithm (GA) based on NSGA-II. The GA uses a warm-start method to generate the initial population based on a mixed-integer program. A greedy first-come-first-served fail-fast repair heuristic is used to ensure feasibility throughout the evolution of the population. The objectives taken into account are energy consumption and passenger travel time. The matheuristic was applied to a real-world case from a large North European train operating company. The considered network consists of 107 stations and junctions, and 18 periodic timetables for 9 train lines. Our results show that for an entire network, a reduction up to 3.3% in energy consumption and 4.64% in passenger travel time can be achieved. The results are computed in less than a minute, making the approach suitable for integration with a decision support tool.
The emergence of an infectious disease pandemic may result in the introduction of restrictions in the distance and number of employees, as was the case of COVID-19 in 2020/2021. In the face of fluctuating restrictions...
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The emergence of an infectious disease pandemic may result in the introduction of restrictions in the distance and number of employees, as was the case of COVID-19 in 2020/2021. In the face of fluctuating restrictions, the process of determining seating plans in office space requires repetitive execution of seat assignments, and manual planning becomes a time-consuming and error-prone task. In this paper, we introduce the Epidemiology-constrained Seating Plan problem (ESP), and we show that it, in general, belongs to the NP-complete class. However, due to some regularities in input data that could affect computational complexity for practical cases, we conduct experiments for generated test cases. For that reason, we developed a computational environment, including the test case generator, and we published generated benchmarking test cases. Our results show that the problem can be solved to optimality by CPLEX solver only for specific settings, even in regular cases. Therefore, there is a need for new algorithms that could optimize seating plans in more general cases.
With increasing wind farm integrations, unit commitment (UC) is more difficult to solve because of the intermittent and random nature of wind power outputs. A robust optimization model for UC is built to deal with the...
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ISBN:
(纸本)9781457710001
With increasing wind farm integrations, unit commitment (UC) is more difficult to solve because of the intermittent and random nature of wind power outputs. A robust optimization model for UC is built to deal with the errors on wind power predictions. The robust optimization method is based on scenario analysis, while the probability of each selected scenario is derived mathematically. In the proposed UC formulation, spinning reserve requirement is specially considered to support possible wind power change between any two successive periods. Network security constraints are considered by DC power flow equations and the whole UC formulation is a mixed-integer programming problem. Case studies on the IEEE 30-bus system demonstrate the effectiveness of the proposed method. The influences of wind power on UC results and network security are also discussed.
In this article, a generalization of the ECP algorithm to cover a class of nondifferentiable mixed-integer NonLinear programming problems is studied. In the generalization constraint functions are required to be -pseu...
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In this article, a generalization of the ECP algorithm to cover a class of nondifferentiable mixed-integer NonLinear programming problems is studied. In the generalization constraint functions are required to be -pseudoconvex instead of pseudoconvex functions. This enables the functions to be nonsmooth. The objective function is first assumed to be linear but also -pseudoconvex case is considered. Furthermore, the gradients used in the ECP algorithm are replaced by the subgradients of Clarke subdifferential. With some additional assumptions, the resulting algorithm shall be proven to converge to a global minimum.
Network design problems are at the heart of several applications in domains such as transportation, telecommunication, energy and natural resources. This paper proposes a new multi-period network design problem varian...
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Network design problems are at the heart of several applications in domains such as transportation, telecommunication, energy and natural resources. This paper proposes a new multi-period network design problem variant, in which modular capacities can be added or reduced along the planning horizon in order to adapt to demand changes. The problem further allows to represent economies of scales in function of the total arc capacity, a detail that has typically been overlooked in the literature. This paper particularly emphasizes the different alternatives to formulate the problem. We propose two different mixed-integer programming formulations and analyze further modeling alternatives. We theoretically compare the strength of all formulations and evaluate their computational performance in extensive experiments. The results suggest that a recent modeling technique using more precise decision variables yields the strongest formulation, which also results in significantly faster solution times. The use of this formulation may therefore be beneficial when considering similar problem variants. Finally, we also evaluate the economical benefits of the features introduced in this new problem variant, indicating that both the selection of the capacity levels and the capacity adjustment along time are likely to result in significant cost savings.
Genome-scale models have expanded beyond their metabolic origins. Multiple modeling frameworks are required to combine metabolism with enzymatic networks, transcription, translation, and regulation. Mathematical progr...
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Genome-scale models have expanded beyond their metabolic origins. Multiple modeling frameworks are required to combine metabolism with enzymatic networks, transcription, translation, and regulation. Mathematical programming offers a powerful set of tools for tackling these “multi-modality” models, although special attention must be paid to the connections between modeling types. This chapter reviews common methods for combining metabolic and discrete logical models into a single mathematical programming framework. Best practices, caveats, and recommendations are presented for the most commonly used software packages. Methods for troubleshooting large sets of logical rules are also discussed. less
Nutrient export from diffuse sources poses a significant threat to watersheds, and methods to support the effective management of these watersheds is essential. However, real-world nutrient flows and watershed managem...
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Nutrient export from diffuse sources poses a significant threat to watersheds, and methods to support the effective management of these watersheds is essential. However, real-world nutrient flows and watershed management systems are highly complex, with a high degree of uncertainty in their descriptive information. Thus, an effective optimization method for watershed management must be developed to deal with this uncertainty, which is crucial for formulating and implementing appropriate management practices. This research presents an inexact simulation-based left-hand-side chance-constrained mixed-integer programming (ISLCCMIP) model to determine nutrient export characteristics and optimal management strategies. By introducing interval and stochastic parameters into the simulation process, the uncertain characteristics of nutrient export loads can be considered. Uncertainties and complexities in management processes can also be handled through incorporating interval parameter programming and mixed-integer programming within a left-hand-side chance-constrained programming framework. The proposed ISLCCMIP model can correlate the randomness in the simulation process and the optimization results. The East River basin in South China was selected as the case study area to apply the proposed model. The results indicated that inorganic nitrogen (N) and phosphorus (P) were the main forms for the nutrient export from diffuse sources in this basin. Five of the nine subbasins were identified as critical source areas for nutrient export. Planting areas of different crops, application amounts of chemical fertilizers, and quantities of livestock types can be optimized to achieve the maximum economic benefit under limited N and P discharge permits. Particularly, planting areas of vegetable and soybean (Glycine max L.) would be first decreased, while rice (Oryza sativa L.) and tubers would be retained, as the pollution emission standards become stricter. Decrease in the planting are
The design of telecommunication network concerns the selection of arcs in a graph with involved cost as low as possible, but satisfies constraints such as point-to-point demands routed across the network, arc capacity...
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The design of telecommunication network concerns the selection of arcs in a graph with involved cost as low as possible, but satisfies constraints such as point-to-point demands routed across the network, arc capacity, hop constraints and so on. Such a design must allocate enough flows and diverse routing paths through the network to ensure that feasible information flows continue to exist, even when components of the network fail. In this paper, we propose a reliable mathematical model to optimally design a minimum-cost survivable telecommunication network that continues to support a good communication under any node failure scenario.
A bi-level tournament selection method for handling multi-objectives and constraints is provided for low dimensional simplex evolution(LDSE).The idea is general and can apply to other evo utionary algorithms.
A bi-level tournament selection method for handling multi-objectives and constraints is provided for low dimensional simplex evolution(LDSE).The idea is general and can apply to other evo utionary algorithms.
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