Many methods for solving mixed integer programming problems are based either on primal or on dual decomposition, which yield, respectively, a Benders decomposition algorithm and an implicit enumeration algorithm with ...
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Many methods for solving mixed integer programming problems are based either on primal or on dual decomposition, which yield, respectively, a Benders decomposition algorithm and an implicit enumeration algorithm with bounds computed via Lagrangean relaxation. These methods exploit either the primal or the dual structure of the problem. We propose a new approach, cross decomposition, which allows exploiting simultaneously both structures. The development of the cross decomposition method captures profound relationships between primal and dual decomposition. It is shown that the more constraints can be included in the Langrangean relaxation (provided the duality gap remains zero), the fewer the Benders cuts one may expect to need. If the linear programming relaxation has no duality gap, only one Benders cut is needed to verify optimality.
We consider the problem of deleting bad influential observations (outliers) in linear regression models. The problem is formulated as a Quadratic mixed integer programming (QMIP) problem, where penalty costs for disca...
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We consider the problem of deleting bad influential observations (outliers) in linear regression models. The problem is formulated as a Quadratic mixed integer programming (QMIP) problem, where penalty costs for discarding outliers are used into the objective function. The optimum solution defines a robust regression estimator called penalized trimmed squares (PTS). Due to the high computational complexity of the resulting QMIP problem, the proposed robust procedure is computationally suitable for small sample data. The computational performance and the effectiveness of the new procedure are improved significantly by using the idea of epsilon-Insensitive loss function from support vectors machine regression. Small errors are ignored, and the mathematical formula gains the sparseness property. The good performance of the epsilon-Insensitive PTS (IPTS) estimator allows identification of multiple outliers avoiding masking or swamping effects. The computational effectiveness and successful outlier detection of the proposed method is demonstrated via simulated experiments.
We describe FATCOP, a new parallel mixedinteger program solver written in PVM. The implementation uses the Condor resource management system to provide a virtual machine composed of otherwise idle computers. The solv...
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We describe FATCOP, a new parallel mixedinteger program solver written in PVM. The implementation uses the Condor resource management system to provide a virtual machine composed of otherwise idle computers. The solver differs from previous parallel branch-and-bound codes by implementing a general purpose parallel mixed integer programming algorithm in an opportunistic multiple processor environment, as opposed to a conventional dedicated environment. It shows how to make effective use of resources as they become available while ensuring the program tolerates resource retreat. The solver performs well on test problems arising from real applications and is particularly useful for solving long running hard mixed integer programming problems.
The gas distribution problem is an important and complex problem for the production in the iron and steel enterprises. To solve the problem, it is very important to establish the mathematical model and find solution m...
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The gas distribution problem is an important and complex problem for the production in the iron and steel enterprises. To solve the problem, it is very important to establish the mathematical model and find solution method for the model. The characteristic of the gas distribution in the production of iron and steel is dynamic, discrete and continuous, which leads to have to do a lot of work to establish a mathematical model with a high dimension. The paper proposes a mixed integer programming mathematical model. The model takes minimizing the cost sum as the objective including the gas emission cost, the operation cost and electricity generation benefit. It also defines the discrete and continuous variables and constrain conditions according to the complex gas applied characteristics related to main process demand, gas holder level, boiler burner switching, synthesis process and cross process. The model is solved by the IBM ILOG CPlex Optimizer software tools with the actual production data in the different time periods. By comparing and analyzing the optimized distribution with actual distribution, the proposed mathematical model is more in accordance with the actual gas applied characteristics. When the fluctuation of gas holder level is smaller, the gas holder level tends to be the center, the gas emission is zero, and the total cost is minimized. According to this solution, the gas distribution effect can meet the demands of stability, security and economy of gas distribution in the actual iron and steel production.
This paper represents an integration of mixed integer programming (MIP) and Constraint Logic programming (CLP) which, like MIP, tightens bounds rather than adding constraints during search. The integrated system combi...
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This paper represents an integration of mixed integer programming (MIP) and Constraint Logic programming (CLP) which, like MIP, tightens bounds rather than adding constraints during search. The integrated system combines components of the CLP system ECLiPSe [7] and the MIP system CPLEX [5], in which constraints can be handled by either one or both components. Our approach is introduced in three stages. Firstly, we present an automatic transformation which maps CLP programs onto such CLP programs that any disjunction is eliminated in favour of auxiliary binary variables. Secondly, we present improvements of this mapping by using a committed choice operator and translations of pre-defined non-linear constraints. Thirdly, we introduce a new hybrid algorithm which reduces the solution space of the problem progressively by calling finite domain propagation of ECLiPSe as well as dual simplex of CPLEX. The advantages of this integration are illustrated by efficiently solving difficult optimisation problems like the Hoist Scheduling Problem [23] and the Progressive Party Problem [27].
This paper deals with the problems of checking strong solvability and feasibility of linear interval equations, checking weak solvability of linear interval equations and inequalities, and finding control solutions of...
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This paper deals with the problems of checking strong solvability and feasibility of linear interval equations, checking weak solvability of linear interval equations and inequalities, and finding control solutions of linear interval equations. These problems are known to be NP-hard. We use some recently developed characterizations in combination with classical arguments to show that these problems can be equivalently stated as optimization tasks and provide the corresponding linear mixed 0-1 programming formulations. (C) 2008 Elsevier B.V. All rights reserved.
Traditionally, electric power plant capacities are determined after specific plant locations have been selected. Tn recent times the focus in the design of photovoltaic (PV) systems has been shifting to ones that oper...
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Traditionally, electric power plant capacities are determined after specific plant locations have been selected. Tn recent times the focus in the design of photovoltaic (PV) systems has been shifting to ones that operate in conjunction with electric grids. In this paper, a generation expansion problem involving the choice of locations and plant capacities over a specified planning horizon (years) is tackled. The problem is formulated as a mixed integer programming model and solved using a modified embedded Benders decomposition method. The model and the algorithm developed in this paper are used to solve a renewable energy system design problem in Ghana.
In the recent years, changing business conditions have triggered labor-intensive global manufacturers to consider relocating out of the Pearl River Delta of China, known as "The World's Factory.'' Thi...
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In the recent years, changing business conditions have triggered labor-intensive global manufacturers to consider relocating out of the Pearl River Delta of China, known as "The World's Factory.'' This article presents a multi-period mixed integer programming model for the problem of relocating a global manufacturing facility. The objective function of the model is to maximize total after-tax profit. The model addresses dynamic aspects of timing, including potential developments in business factors and the need for a gradual capacity transfer in order not to disrupt supply chain activities. The model application generates an optimal capacity transfer schedule and forecasts after-tax profits. In general, a stable exchange rate for the Chinese currency, renminbi (RMB), would make lower-cost areas of China more competitive. Also, a dramatic RMB appreciation would enhance the comparative advantage of Asian lower-cost countries. A rapid increase in oil prices would make locations near major markets more favorable in order to avoid high transportation costs.
This paper puts forward an integrated optimisation model that combines three distinct problems, namely berth allocation, quay crane assignment, and quay crane scheduling that arise in container ports. Each one of thes...
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This paper puts forward an integrated optimisation model that combines three distinct problems, namely berth allocation, quay crane assignment, and quay crane scheduling that arise in container ports. Each one of these problems is difficult to solve in its own right. However, solving them individually leads almost surely to sub-optimal solutions. Hence, it is desirable to solve them in a combined form. The model is of the mixed-integerprogramming type with the objective being to minimize the tardiness of vessels and reduce the cost of berthing. Experimental results show that relatively small instances of the proposed model can be solved exactly using CPLEX. Large scale instances, however, can only be solved in reasonable times using heuristics. Here, an implementation of the genetic algorithm is considered. The effectiveness of this implementation is tested against CPLEX on small to medium size instances of the combined model. Larger size instances were also solved with the genetic algorithm, showing that this approach is capable of finding the optimal or near optimal solutions in realistic times.
During the last decade, significant progress has been made in solving the Protein Threading Problem (PTP). However, all previous approaches to PTP only perform global sequence-structure alignment. This obvious limitat...
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During the last decade, significant progress has been made in solving the Protein Threading Problem (PTP). However, all previous approaches to PTP only perform global sequence-structure alignment. This obvious limitation is in clear contrast with the "world of sequences", where local sequence-sequence alignments are widely used to find functionally important regions in families of proteins. This paper presents a novel approach to PIP which allows to align a part of a protein structure onto a protein sequence in order to detect local similarities. We show experimentally that such local sequence-structure alignments improve the quality of the prediction. Our approach is based on mixed integer programming (MIP) which has been shown to be very successful in this domain. We describe five MIP models for local sequence-structure alignments, compare and analyze their performances by using ILOG CPLEX 10 solver on a benchmark of proteins. (C) 2010 Elsevier B.V. All rights reserved.
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