Removing small artificial barriers that hinder upstream migrations of fish is a major problem in riparian habitat restoration. Because of budgetary limitations, it is necessary to prioritize barrier removal and repair...
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Removing small artificial barriers that hinder upstream migrations of fish is a major problem in riparian habitat restoration. Because of budgetary limitations, it is necessary to prioritize barrier removal and repair decisions. These have usually been based on scoring and ranking procedures, which, although simple to use, can be very inefficient in terms of increasing the amount of accessible instream habitat. We develop a novel decision-making approach, based on integerprogramming techniques, which optimizes repair and removal decisions. Results show based on real datasets of barrier culverts located in Washington State that scoring and ranking is over 25% below the optimum on average and a full 100% below in the worst case, producing no net habitat gain whatsoever. This is compared to a dynamic programming method that was able to find optimal solutions in less than a second, even for problems with up to several hundred variables, and a heuristic method, which found solutions with less than a 1% average optimality gap in even less time.
A definition of the discrete filled function is given in this paper. Based on the definition, a discrete filled function is proposed. Theoretical properties of the proposed discrete filled function are investigated, a...
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A definition of the discrete filled function is given in this paper. Based on the definition, a discrete filled function is proposed. Theoretical properties of the proposed discrete filled function are investigated, and an algorithm for discrete global optimization is developed from the new discrete filled function. The implementation of the algorithms on several test problems is reported with satisfactory numerical results. (c) 2006 Elsevier B.V. All rights reserved.
In this paper, we formulate an optimal weight design problem of a gear for a constrained bending strength of gear, tortional strength of shafts and each gear dimension as a nonlinear integer programming (NIP) problema...
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In this paper, we formulate an optimal weight design problem of a gear for a constrained bending strength of gear, tortional strength of shafts and each gear dimension as a nonlinear integer programming (NIP) problemand solve it directly by keeping the nonlinear constraint by using an improved genetic algorithm (GA). We discuss the efficency of the proposed method. (C) 1998 Elsevier Science Ltd. All rights reserved.
Recently, Rahman and Sarker [1] formulated a nonlinear cost function to include the costs of inventories, ordering, shipping and deliveries for a manufacturing system where raw materials enter into the assembly line f...
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Recently, Rahman and Sarker [1] formulated a nonlinear cost function to include the costs of inventories, ordering, shipping and deliveries for a manufacturing system where raw materials enter into the assembly line from two different channels. In order to find the best integer solutions to the variables, they proposed an algorithm that uses the branch and bound concept. The authors claim that their algorithm finds an optimal solution. In this paper, we present a simple and better heuristic algorithm that provides better solutions with a lower number of evaluations than Rahman and Sarker's algorithm. (C) 2013 Elsevier Inc. All rights reserved.
The purpose of this note is to present a smooth penalty formulation for integerprogramming. By adopting the proposed logarithmic-exponential penalty function, we are able to transform an inequality constrained intege...
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The purpose of this note is to present a smooth penalty formulation for integerprogramming. By adopting the proposed logarithmic-exponential penalty function, we are able to transform an inequality constrained integerprogramming problem into an equivalent unconstrained problem with a smooth objective function when choosing an appropriate penalty parameter. We show that this penalty formulation preserves the convexity for convex integerprogramming problems. (C) 1999 Elsevier Science Ltd. All rights reserved.
The rupture degree of an incomplete connected graph G is defined by r(G) = max{omega(G - X) - vertical bar X vertical bar - tau (G - X) : X subset of V (G), omega(G - X) > 1}, where omega(G - X) is the number of co...
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The rupture degree of an incomplete connected graph G is defined by r(G) = max{omega(G - X) - vertical bar X vertical bar - tau (G - X) : X subset of V (G), omega(G - X) > 1}, where omega(G - X) is the number of components of G - X and tau (G - X) is the order of a largest component of G - X. In Li and Li [5] and Li et al. (2005)[4], it was shown that the rupture degree can be well used to measure the vulnerability of networks. In this paper, the maximum and minimum networks with prescribed order and the rupture degree are obtained. Finally, we determine the maximum rupture degree graphs with given order and size. Crown Copyright (C) 2010 Published by Elsevier Ltd. All rights reserved.
A discrete filled function method is developed in this paper to solve discrete global optimization problems over "strictly pathwise connected domains." Theoretical properties of the proposed discrete filled ...
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A discrete filled function method is developed in this paper to solve discrete global optimization problems over "strictly pathwise connected domains." Theoretical properties of the proposed discrete filled function are investigated and a solution algorithm is proposed. Numerical experiments reported in this paper on several test problems with up to 200 variables have demonstrated the applicability and efficiency of the proposed method.
A nonlinear integer programming model for the optimal design of a series/parallel reliability system is presented, together with an enumeration algorithm for its solution and an example. The algorithm is based on an e...
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A nonlinear integer programming model for the optimal design of a series/parallel reliability system is presented, together with an enumeration algorithm for its solution and an example. The algorithm is based on an efficient procedure for solving the continuous relaxation of the mathematical model. (C) 2001 Elsevier Science Inc. All rights reserved.
作者：
Bretthauer, KMShetty, BSyam, SIndiana Univ
Kelley Sch Business Dept Operat & Decis Technol Bloomington IN 47405 USA Texas A&M Univ
Lowry Mays Coll Business Dept Informat & Operat Management College Stn TX 77843 USA Marquette Univ
Coll Business Adm Dept Management Milwaukee WI 53201 USA
We present an algorithm for solving a specially structured nonlinearinteger resource allocation problem. This problem was motivated by a capacity planning study done at a large Health Maintenance Organization in Texa...
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We present an algorithm for solving a specially structured nonlinearinteger resource allocation problem. This problem was motivated by a capacity planning study done at a large Health Maintenance Organization in Texas. Specifically, we focus on a class of nonlinear resource allocation problems that involve the minimization of a convex function over one general convex constraint, a set of block diagonal convex constraints, and bounds on the integer variables. The continuous variable problem is also considered. The continuous problem is solved by taking advantage of the structure of the Karush-Kuhn-Tucker (KKT) conditions. This method for solving the continuous problem is then incorporated in a branch and bound algorithm to solve the integer problem. Various reoptimization results, multiplier bounding results, and heuristics are used to improve the efficiency of the algorithms. We show how the algorithms can be extended to obtain a globally optimal solution to the nonconvex version of the problem. We further show that the methods can be applied to problems in production planning and financial optimization. Extensive computational testing of the algorithms is reported for a variety of applications on continuous problems with up to 1,000,000 variables and integer problems with up to 1000 variables. (C) 2003 Wiley Periodicals, Inc.
We consider a new approach for the maximum-entropy sampling problem (MESP) that is based on bounds obtained by maximizing a function of the form ldet M(x) over linear constraints, where M(x) is linear in the n-vector ...
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We consider a new approach for the maximum-entropy sampling problem (MESP) that is based on bounds obtained by maximizing a function of the form ldet M(x) over linear constraints, where M(x) is linear in the n-vector x. These bounds can be computed very efficiently and are superior to all previously known bounds for MESP on most benchmark test problems. A branch-and-bound algorithm using the new bounds solves challenging instances of MESP to optimality for the first time.
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