The rendezvous and docking mission is usually divided into several phases, and the mission planning is performed phase by phase. A new planning method using mixed integer nonlinear programming, which investigates sing...
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The rendezvous and docking mission is usually divided into several phases, and the mission planning is performed phase by phase. A new planning method using mixed integer nonlinear programming, which investigates single phase parameters and phase connecting parameters simultaneously, is proposed to improve the rendezvous mission's overall performance. The design variables are composed of integers and continuous-valued numbers. The integer part consists of the parameters for station-keeping and sensor-switching, the number of maneuvers in each rendezvous phase and the number of repeating periods to start the rendezvous mission. The continuous part consists of the orbital transfer time and the station-keeping duration. The objective function is a combination of the propellant consumed, the sun angle which represents the power available, and the terminal precision of each rendezvous phase. The operational requirements for the spacecraft-ground communication, sun illumination and the sensor transition are considered. The simple genetic algorithm, which is a combination of the integer-coded and real-coded genetic algorithm, is chosen to obtain the optimal solution. A practical rendezvous mission planning problem is solved by the proposed method. The results show that the method proposed can solve the integral rendezvous mission planning problem effectively, and the solution obtained can satisfy the operational constraints and has a good overall performance. (C) 2010 Elsevier Ltd. All rights reserved.
To handle the detrimental effects brought by leakage of radioactive gases at nuclear power station, we propose a bus based evacuation optimization problem. The proposed model incorporates the following four constraint...
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To handle the detrimental effects brought by leakage of radioactive gases at nuclear power station, we propose a bus based evacuation optimization problem. The proposed model incorporates the following four constraints, 1) the maximum dose of radiation per evacuee, 2) the limitation of bus capacity, 3) the number of evacuees at demand node（bus pickup stop）,4) evacuees balance at demand and shelter nodes, which is formulated as a mixed integer nonlinear programming（MINLP）problem. Then, to eliminate the difficulties of choosing a proper M value in Big-M method, a Big-M free method is employed to linearize the nonlinear terms of the MINLP problem. Finally, the resultant mixedinteger linear program（MILP） problem is solvable with efficient commercial solvers such as CPLEX or Gurobi, which guarantees the optimal evacuation plan *** evaluate the effectiveness of proposed evacuation model, we test our model on two different scenarios（a random one and a practical scenario）. For both scenarios, our model attains executable evacuation plan within given 3600 seconds computation time.
Risk scores are simple classification models that let users make quick risk predictions by adding and subtracting a few small numbers. These models are widely used in medicine and criminal justice, but are difficult t...
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Risk scores are simple classification models that let users make quick risk predictions by adding and subtracting a few small numbers. These models are widely used in medicine and criminal justice, but are difficult to learn from data because they need to be calibrated, sparse, use small integer coefficients, and obey application-specific constraints. In this paper, we introduce a machine learning method to learn risk scores. We formulate the risk score problem as a mixedintegernonlinear program, and present a cutting plane algorithm to recover its optimal solution. We improve our algorithm with specialized techniques that generate feasible solutions, narrow the optimality gap, and reduce data-related computation. Our algorithm can train risk scores in a way that scales linearly in the number of samples in a dataset, and that allows practitioners to address application-specific constraints without parameter tuning or post-processing. We benchmark the performance of different methods to learn risk scores on publicly available datasets, comparing risk scores produced by our method to risk scores built using methods that are used in practice. We also discuss the practical benefits of our method through a real-world application where we build a customized risk score for ICU seizure prediction in collaboration with the Massachusetts General Hospital.
作者：
Vielma, Juan PabloMIT
Sloan Sch Management 77 Massachusetts Ave Cambridge MA 02139 USA
There is often a significant trade-off between formulation strength and size in mixedintegerprogramming. When modeling convex disjunctive constraints (e.g. unions of convex sets), adding auxiliary continuous variabl...
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There is often a significant trade-off between formulation strength and size in mixedintegerprogramming. When modeling convex disjunctive constraints (e.g. unions of convex sets), adding auxiliary continuous variables can sometimes help resolve this trade-off. However, standard formulations that use such auxiliary continuous variables can have a worse-than-expected computational effectiveness, which is often attributed precisely to these auxiliary continuous variables. For this reason, there has been considerable interest in constructing strong formulations that do not use continuous auxiliary variables. We introduce a technique to construct formulations without these detrimental continuous auxiliary variables. To develop this technique we introduce a natural non-polyhedral generalization of the Cayley embedding of a family of polytopes and show it inherits many geometric properties of the original embedding. We then show how the associated formulation technique can be used to construct small and strong formulation for a wide range of disjunctive constraints. In particular, we show it can recover and generalize all known strong formulations without continuous auxiliary variables.
Rapid global industrial development has led to a significant increase in waste generation, including wastewater. Improper disposal of wastewater leads to the degradation of water bodies, endangering marine life and po...
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Rapid global industrial development has led to a significant increase in waste generation, including wastewater. Improper disposal of wastewater leads to the degradation of water bodies, endangering marine life and posing health hazards to the nearby communities. This study addresses the current lack of integration in the design of wastewater treatment plants and the challenge presented by the conflicting criteria of economic impact and environmental cost. A multi-period and multi-criterion non-linear programming model for a wastewater treatment plant that simultaneously considers economic and environmental tradeoffs, alternative plant configurations, disposal and reuse options is then developed. The study considers the variability of inputs in the form of water quality and quantity in order to demonstrate the natural variations presented by wastewater sources across a planning horizon. The proposed model is applied to a case study of an actual water utility company in the Philippines. It was seen that the integration of disposal and reuse options had facilitated the realization of improved economic and environmental benefits as it was able to match the effluent water quality to the best suited option. The model enabled the improvement of the treatment process of wastewater inputs by considering alternative methods of entry such as series and parallel configurations instead of just having a mixed input configuration. This significantly improved relevant metrics such as processing time and operational costs. (C) 2019 Elsevier Ltd. All rights reserved.
Lot Quality Assurance Sampling (LQAS) plans are widely used for health monitoring purposes. We propose a systematic approach to design multiple-objective LQAS plans that meet user-specified type 1 and 2 error rates an...
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Lot Quality Assurance Sampling (LQAS) plans are widely used for health monitoring purposes. We propose a systematic approach to design multiple-objective LQAS plans that meet user-specified type 1 and 2 error rates and targets for selected diagnostic accuracy metrics. These metrics may include sensitivity, specificity, positive predictive value, and negative predictive value in high or low anticipated prevalence rate populations. We use mixed integer nonlinear programming (MINLP) tools to implement our design methodology. Our approach is flexible in that it can directly generate classic LQAS plans that control error rates only and find optimal LQAS plans that meet multiple objectives in terms of diagnostic metrics. We give examples, compare results with the classic LQAS and provide an application using a malaria outcome indicator survey in Mozambique.
We consider a hub-and-spoke network design problem with inter-hub economies-of-scale and hub congestion. We explicitly model the economies-of-scale as a concave piece-wise linear function and congestion as a convex fu...
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We consider a hub-and-spoke network design problem with inter-hub economies-of-scale and hub congestion. We explicitly model the economies-of-scale as a concave piece-wise linear function and congestion as a convex function. The problem is modeled as a nonlinearmixedinteger program that is difficult to solve directly since the objective function has both convex and concave nonlinear terms and hence finding an optimal solution may not be easy. A Lagrangian approach is proposed to obtain tight upper and lower bounds. The Lagrangian decomposition exploits the structure of the problem and decomposes it to convex and concave subproblems. Furthermore, we add some valid inequalities to accelerate the convergence rate of the Lagrangian heuristic. To tackle large instances, a Greedy Randomized Adaptive Search Procedure (GRASP) is developed. Both the Lagrangian heuristic and GRASP provide high-quality solutions within reasonable computational time. The optimal designs of hub-and-spoke networks with nonlinear inter-hub economies-of-scale and congestion at hub locations are analyzed in comparison with other models in the literature to demonstrate the significant impact of modeling nonlinearity in economies-of-scale and congestion simultaneously rather than independently.
Today's infrastructures are mainly designed heuristically using state-of-the-art simulation software and engineering approaches. However, due to complexity, only part of the restrictions and costs that show up dur...
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Today's infrastructures are mainly designed heuristically using state-of-the-art simulation software and engineering approaches. However, due to complexity, only part of the restrictions and costs that show up during the lifecycle can be taken into account. In this paper, we focus on a typical and important class of infrastructure problems, the design of high-pressure steam pipes in power plants, and describe a holistic approach taking all design, physical, and technical constraints and the costs over the full lifecycle into account. The problem leads to a large-scale mixed-integer optimization problem with partial differential equation (PDE) constraints which will be addressed hierarchically. The hierarchy consists of a combinatorial and a PDE-constrained optimization problem. The final design is evaluated with respect to damage, using beam models that are nonlinear with respect to kinematics as well as constitutive law. We demonstrate the success of our approach on a real-world instance from our industrial partner Bilfinger SE.
The mixed transportation mode based pipeline system plays a significant role in the supply chain of liquefied light hydrocarbon. Inefficient scheduling may lead to problems of frequent pump stoppage/restart, high ener...
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The mixed transportation mode based pipeline system plays a significant role in the supply chain of liquefied light hydrocarbon. Inefficient scheduling may lead to problems of frequent pump stoppage/restart, high energy consumption, and frequent pressure fluctuation. To avoid these problems, this paper implements the discrete-time representation to develop a multi-scenario and multi-objective mixed integer nonlinear programming model to minimize the pump operation cost and the number of the switching operation. In the model, several factors, such as transportation mode, inventory limits of injection stations, flow rate and pressure limits of pipeline segments, are taken into consideration. Additionally, the uncertainty of the liquefied light hydrocarbon production is described by scenarios. The improved augmented epsilon-constraint method is implemented to solve this model. Finally, the model is successfully applied to a multi-source single-sink liquefied light hydrocarbon pipeline system in Daqing oilfield in China. The experimental results show that the proposed model outperforms another two available models in profit, safety, and robustness aspects. (C) 2018 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
SYNBADm is a Matlab toolbox for the automated design of biocircuits using a model-based optimization approach. It enables the design of biocircuits with pre-defined functions starting from libraries of biological part...
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SYNBADm is a Matlab toolbox for the automated design of biocircuits using a model-based optimization approach. It enables the design of biocircuits with pre-defined functions starting from libraries of biological parts. SYNBADm makes use of mixedinteger global optimization and allows both single and multi-objective design problems. Here we describe a basic protocol for the design of synthetic gene regulatory circuits. We illustrate step-by-step how to solve two different problems: (1) the (single objective) design of a synthetic oscillator and (2) the (multi-objective) design of a circuit with switch-like behavior upon induction, with a good compromise between performance and protein production cost. less
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