This paper proposes a mixed integer nonlinear programming (MINLP) approach to measure the system performances of multiple-channel queueing models with imprecise data. The main idea is to transform a multiple-channel q...
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This paper proposes a mixed integer nonlinear programming (MINLP) approach to measure the system performances of multiple-channel queueing models with imprecise data. The main idea is to transform a multiple-channel queue with imprecise data to a family of conventional crisp multiple-channel queues by applying the alpha-cut approach in fuzzy theory. On the basis of Phi-cut representation and the extension principle, two pairs of parametric MINLP are formulated to describe the family of crisp multiple-channel queues, via which the membership functions of the performance measures are derived. To demonstrate the validity of the proposed procedure, a real-world case of multiple-channel fuzzy queue is investigated successfully. Since the performance measures are expressed by membership functions rather than by crisp values, the fuzziness of input information is completed conserved. Thus, the results obtained from the proposed approach can represent the system more accurately, and more information is provided for system design in practice.
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 are concerned with a problem in which a firm or franchise enters a market by locating new facilities where there are existing facilities belonging to a competitor The firm aims at finding the location and attractiv...
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We are concerned with a problem in which a firm or franchise enters a market by locating new facilities where there are existing facilities belonging to a competitor The firm aims at finding the location and attractiveness of each facility to be opened so as to maximize its profit The competitor on the other hand can react by adjusting the attractiveness of its existing facilities with the objective of maximizing its own profit The demand is assumed to be aggregated at certain points in the plane and the facilities of the firm can be located at predetermined candidate sites We employ Huff's gravity-based rule in modeling the behavior of the customers where the fraction of customers at a demand point that visit a certain facility is proportional to the facility attractiveness and Inversely proportional to the distance between the facility site and demand point We formulate a bilevel mixed-integernonlinearprogramming model where the firm entering the market is the leader and the competitor is the follower In order to find the optimal solution of this model we convert It Into an equivalent one-level mixedintegernonlinear program so that it can be solved by global optimization methods Apart from reporting computational results obtained on a set of randomly generated instances we also compute the benefit the leader firm derives from anticipating the competitor s reaction of adjusting the attractiveness levels of its facilities The results on the test Instances indicate that the benefit is 58 33% on the average (C) 2010 Elsevier B V All rights reserved
In this paper, we present an access network design problem with end-to-end quality of service (QoS) requirement. The problem can be conceptualized as a two-level hierarchical location-allocation problem on the tree to...
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In this paper, we present an access network design problem with end-to-end quality of service (QoS) requirement. The problem can be conceptualized as a two-level hierarchical location-allocation problem on the tree topology with nonlinear side constraints. The objective function of the nonlinearmixedintegerprogramming model minimizes the total cost of switch and fiber cable, while satisfying demand within the prescribed level of QoS. By exploiting the inherent structure of the nonlinear QoS constraints, we develop linearization techniques for finding an optimal solution. Also, we devise an effective exact optimal algorithm within the context of disjunctive constraint generation. We present promising computational results that demonstrate the effectiveness of the proposed solution procedure. (C) 2014 Elsevier Ltd. All rights reserved.
This work considers nonconvex mixed integer nonlinear programming where nonlinearity comes from the presence of the two-dimensional euclidean norm in the objective or the constraints. We build from the euclidean norm ...
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This work considers nonconvex mixed integer nonlinear programming where nonlinearity comes from the presence of the two-dimensional euclidean norm in the objective or the constraints. We build from the euclidean norm piecewise linearization proposed by Camino et al. (Comput. Optim. Appl. https://***/10.1007/s10589-019-00083-z, 2019) that allows to solve such nonconvex problems via mixed-integer linear programming with an arbitrary approximation guarantee. Theoretical results are established that prove that this linearization is able to satisfy any given approximation level with the minimum number of pieces. An extension of the piecewise linearization approach is proposed. It shares the same theoretical properties for elliptic constraints and/or objective. An application shows the practical appeal of the elliptic linearization on a nonconvex beam layout mixed optimization problem coming from an industrial application.
This paper proposes a mechanism to fine-tune convex approximations of probabilistic reachable sets (PRS) of uncertain dynamic systems. We consider the case of unbounded uncertainties, for which it may be impossible to...
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This paper proposes a mechanism to fine-tune convex approximations of probabilistic reachable sets (PRS) of uncertain dynamic systems. We consider the case of unbounded uncertainties, for which it may be impossible to find a bounded reachable set of the system. Instead, we turn to find a PRS that bounds system states with high confidence. Our data-driven approach builds on a kernel density estimator (KDE) accelerated by a fast Fourier transform (FFT), which is customized to model the uncertainties and obtain the PRS efficiently. However, the non-convex shape of the PRS can make it impractical for subsequent optimal designs. Motivated by this, we formulate a mixed integer nonlinear programming (MINLP) problem whose solution result is an optimal n sided convex polygon that approximates the PRS. Leveraging this formulation, we propose a heuristic algorithm to find this convex set efficiently while ensuring accuracy. The algorithm is tested on comprehensive case studies that demonstrate its near-optimality, accuracy, efficiency, and robustness. The benefits of this work pave the way for promising applications to safety-critical, real-time motion planning of uncertain dynamic systems.
In this paper a new approach for the global solution of nonconvex MINLP (mixed integer nonlinear programming) problems that contain signomial (generalized geometric) expressions is proposed and illustrated. By applyin...
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In this paper a new approach for the global solution of nonconvex MINLP (mixed integer nonlinear programming) problems that contain signomial (generalized geometric) expressions is proposed and illustrated. By applying different variable transformation techniques and a discretization scheme a lower bounding convex MINLP problem can be derived. The convexified MINLP problem can be solved with standard methods. The key element in this approach is that all transformations are applied termwise. In this way all convex parts of the problem are left unaffected by the transformations. The method is illustrated by four example problems. (C) 2007 Elsevier B.V. All rights reserved.
In this paper, a new global optimization approach for the synthesis of heat exchanger networks is presented. The Synheat model by [Comp. Chem. Eng. 1 (1990) 1165] uses a promising superstructure that includes the most...
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In this paper, a new global optimization approach for the synthesis of heat exchanger networks is presented. The Synheat model by [Comp. Chem. Eng. 1 (1990) 1165] uses a promising superstructure that includes the most common heat exchanger structures, and optimizes utility costs, the number of units and heat exchanger areas simultaneously. With some minor modifications of the model, it is possible to apply a new global optimization strategy to the problem. The heart of the strategy is to convexify signomial terms, and create approximate convexified subproblems. If the Synheat model is extended in an appropriate way, the isothermal mixing assumption can be removed. Applying the new global optimization strategy to the model allowing non-isothermal mixing makes it possible to find truly optimal network configurations for the case of constant heat-capacity flow rates and heat transfer coefficients. Some examples to illustrate the global optimization strategy and to illustrate that the Synheat model can exclude optimal configurations due to the isothermal mixing assumption are also given in this paper. (C) 2002 Elsevier Science Ltd. All rights reserved.
An interval based superstructure approach for combining the synthesis of heat and mass exchanger networks is presented in this paper. The technique involves combining the interval based MINLP superstructure (IBMS) for...
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An interval based superstructure approach for combining the synthesis of heat and mass exchanger networks is presented in this paper. The technique involves combining the interval based MINLP superstructure (IBMS) for the synthesis of heat exchanger networks (HENS) with that of mass exchanger networks (MENs). The two networks are made to interact through the lean stream in the mass exchange network. The new approach involves the use of the lean substream concept to explore potential mass exchange temperatures. An example which involves a one-lean stream mass exchange problem alongside regeneration and hot and cold utilities is presented. (C) 2009 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
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.
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