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67
VLSI cell placement techniques
 ACM Computing Surveys
, 1991
"... VLSI cell placement problem is known to be NP complete. A wide repertoire of heuristic algorithms exists in the literature for efficiently arranging the logic cells on a VLSI chip. The objective of this paper is to present a comprehensive survey of the various cell placement techniques, with emphasi ..."
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Cited by 76 (0 self)
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VLSI cell placement problem is known to be NP complete. A wide repertoire of heuristic algorithms exists in the literature for efficiently arranging the logic cells on a VLSI chip. The objective of this paper is to present a comprehensive survey of the various cell placement techniques, with emphasis on standard ce11and macro
Decomposition Algorithms for Stochastic Programming on a Computational Grid
 Computational Optimization and Applications
, 2001
"... . We describe algorithms for twostage stochastic linear programming with recourse and their implementation on a grid computing platform. In particular, we examine serial and asynchronous versions of the Lshaped method and a trustregion method. The parallel platform of choice is the dynamic, heter ..."
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Cited by 52 (7 self)
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. We describe algorithms for twostage stochastic linear programming with recourse and their implementation on a grid computing platform. In particular, we examine serial and asynchronous versions of the Lshaped method and a trustregion method. The parallel platform of choice is the dynamic, heterogeneous, opportunistic platform provided by the Condor system. The algorithms are of masterworker type (with the workers being used to solve secondstage problems), and the MW runtime support library (which supports masterworker computations) is key to the implementation. Computational results are presented on large sample average approximations of problems from the literature. 1.
Hybrid Benders Decomposition Algorithms in Constraint Logic Programming
 In Procs. of the 7th Intern. Conference on Principles and Practice of Constraint Programming  CP 2001
, 2001
"... Benders Decomposition is a form of hybridisation that allows linear programming to be combined with other kinds of algorithms. It extracts new constraints for one subproblem from the dual values of the other subproblem. This paper describes an implementation of Benders Decomposition, in the ECLiPSe ..."
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Cited by 20 (1 self)
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Benders Decomposition is a form of hybridisation that allows linear programming to be combined with other kinds of algorithms. It extracts new constraints for one subproblem from the dual values of the other subproblem. This paper describes an implementation of Benders Decomposition, in the ECLiPSe language, that enables it to be used within a constraint programming framework. The programmer is spared from having to write down the dual form of any subproblem, because it is derived by the system. Examples are used to show how problem constraints can be modelled in an undecomposed form. The programmer need only specify which variables belong to which subproblems, and the Benders Decomposition is extracted automatically. A class of minimal perturbation problems is used to illustrate how dierent kinds of algorithms can be used for the dierent subproblems. The implementation is tested on a set of minimal perturbation benchmarks, and the results are analysed.
Strong formulations for network design problems with connectivity requirements
 NETWORKS
, 1999
"... The network design problem with connectivity requirements (NDC) models a wide variety of celebrated combinatorial optimization problems including the minimum spanning tree, Steiner tree, and survivable network design problems. We develop strong formulations for two versions of the edgeconnectivity ..."
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Cited by 15 (1 self)
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The network design problem with connectivity requirements (NDC) models a wide variety of celebrated combinatorial optimization problems including the minimum spanning tree, Steiner tree, and survivable network design problems. We develop strong formulations for two versions of the edgeconnectivity NDC problem: unitary problems requiring connected network designs, and nonunitary problems permitting nonconnected networks as solutions. We (i) present a new directed formulation for the unitary NDC problem that is stronger than a natural undirected formulation, (ii) project out several classes of valid inequalitiespartition inequalities, oddhole inequalities, and combinatorial design inequalitiesthat generalize known classes of valid inequalities for the Steiner tree problem to the unitary NDC problem, and (iii) show how to strengthen and direct nonunitary problems. Our results provide a unifying framework for strengthening formulations for NDC problems, and demonstrate the strength and power of flowbased formulations for network design problems with connectivity requirements.
A Finite Branch and Bound Algorithm for TwoStage Stochastic Integer Programs
, 2000
"... This paper addresses a general class of twostage stochastic programs with integer recourse and discrete distributions. We exploit the structure of the value function of the second stage integer problem to develop a novel global optimization algorithm. The proposed scheme departs from those in the c ..."
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Cited by 15 (4 self)
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This paper addresses a general class of twostage stochastic programs with integer recourse and discrete distributions. We exploit the structure of the value function of the second stage integer problem to develop a novel global optimization algorithm. The proposed scheme departs from those in the current literature in that it avoids explicit enumeration of the search space while guaranteeing finite termination. Our computational results indicate superior performance of the proposed algorithm in comparison to the existing literature. Keywords: stochastic integer programming, branch and bound, finite algorithms. 1 Introduction Under the twostage stochastic programming paradigm, the decision variables of an optimization problem under uncertainty are partitioned into two sets. The first stage variables are those that have to be decided before the actual realization of the uncertain parameters. Subsequently, once the random events have presented themselves, further design or operational ...
Algorithms and software for convex mixed integer nonlinear programs, IMA Volumes
"... Abstract. This paper provides a survey of recent progress and software for solving convex mixed integer nonlinear programs (MINLP)s, where the objective and constraints are defined by convex functions and integrality restrictions are imposed on a subset of the decision variables. Convex MINLPs have ..."
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Cited by 11 (2 self)
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Abstract. This paper provides a survey of recent progress and software for solving convex mixed integer nonlinear programs (MINLP)s, where the objective and constraints are defined by convex functions and integrality restrictions are imposed on a subset of the decision variables. Convex MINLPs have received sustained attention in recent years. By exploiting analogies to wellknown techniques for solving mixed integer linear programs and incorporating these techniques into software, significant improvements have been made in the ability to solve these problems. Key words. Mixed Integer Nonlinear Programming; Branch and Bound; AMS(MOS) subject classifications.
Computing robust basestock levels
 Discrete Optimization
"... This paper considers how to optimally set the basestock level for a single buffer when demand is uncertain, in a robust framework. We present a family of algorithms based on decomposition that scale well to problems with hundreds of time periods, and theoretical results on more general models. 1 ..."
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Cited by 10 (0 self)
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This paper considers how to optimally set the basestock level for a single buffer when demand is uncertain, in a robust framework. We present a family of algorithms based on decomposition that scale well to problems with hundreds of time periods, and theoretical results on more general models. 1
The N k Problem in Power Grids: New Models, Formulations and Numerical Experiments (extended version) 1
, 2008
"... Given a power grid modeled by a network together with equations describing the power flows, power generation and consumption, and the laws of physics, the socalled N − k problem asks whether there exists a set of k or fewer arcs whose removal will cause the system to fail. The case where k is small ..."
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Cited by 7 (1 self)
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Given a power grid modeled by a network together with equations describing the power flows, power generation and consumption, and the laws of physics, the socalled N − k problem asks whether there exists a set of k or fewer arcs whose removal will cause the system to fail. The case where k is small is of practical interest. We present theoretical and computational results involving a mixedinteger model and a continuous nonlinear model related to this question. 1
Strategic Capacity Planning In The Semiconductor Industry: A Stochastic Programming Approach
 Operations Research
, 1999
"... We study strategic capacity planning in the semiconductor industry. Working with a major US semiconductor manufacturer on the strategic configuration of their worldwide production capacities, we identify two unique characteristics of this problem as follows: (1) wafer demands and manufacturing capac ..."
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Cited by 6 (3 self)
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We study strategic capacity planning in the semiconductor industry. Working with a major US semiconductor manufacturer on the strategic configuration of their worldwide production capacities, we identify two unique characteristics of this problem as follows: (1) wafer demands and manufacturing capacity are both main sources of uncertainty, and (2) capacity planning must consider two distinct viewpoints: a product perspective concerning marketing and strategic demand management, and a process standpoint involving manufacturing, yield, and technology configuration. These two unique characteristics change, in a fundamental way, how strategic capacity planning problem should be approached. To describe this complex problem, we first formulate a multistage stochastic program with recourses where demand and capacity uncertainties are incorporated via a scenario structure. To reconcile the marketing and manufacturing perspectives to the problem, we consider a decomposition of the planning pro...
Logicbased Modeling and Solution of Nonlinear Discrete/Continuous Optimization Problems
"... This paper presents a review of advances in the mathematical programming approach to discrete/continuous optimization problems. We first present a brief review of MILP and MINLP for the case when these problems are modeled with algebraic equations and inequalities. Since algebraic representations ha ..."
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Cited by 4 (3 self)
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This paper presents a review of advances in the mathematical programming approach to discrete/continuous optimization problems. We first present a brief review of MILP and MINLP for the case when these problems are modeled with algebraic equations and inequalities. Since algebraic representations have some limitations such as difficulty of formulation and numerical singularities for the nonlinear case, we consider logicbased modeling as an alternative approach, particularly Generalized Disjunctive Programming (GDP), which the authors have extensively investigated over the last few years. Solution strategies for GDP models are reviewed, including the continuous relaxation of the disjunctive constraints. Also, we briefly review a hybrid model that integrates disjunctive programming and mixed integer programming. Finally, the global optimization of nonconvex GDP problems is discussed through a twolevel branch and bound procedure.