A mathematical model based on mixed integer programming is presented in this paper for the passive shimming of magnet in magnetic resonance imaging(MRI) *** this model,the magnetic field inhomogeneity tolerance and th...
详细信息
A mathematical model based on mixed integer programming is presented in this paper for the passive shimming of magnet in magnetic resonance imaging(MRI) *** this model,the magnetic field inhomogeneity tolerance and the central value of the magnetic field after shimming are programmed together with the volume of each shim piece as the variables,which increases the degree of freedom and guarantees a better *** magnetic field inhomogeneity tolerance after shimming with a weighting coefficient and the total volume of shim pieces are both contained in the objective function of the *** assigning different values to the weighting coefficient in the objective function,different shimming plans with different emphases can be obtained.A simulation test has been carried out on a small permanent magnet with frame *** solutions are given and compared in this paper,which indicates that a practical shimming plan can be obtained quickly by solving this model.
Particle Swarm Optimization (PSO) for mixed integer programming problems is proposed. PSO is mainly a method to find a global or quasi-minimum for a nonlinear and nonconvex optimization problem, and there have been fe...
详细信息
Particle Swarm Optimization (PSO) for mixed integer programming problems is proposed. PSO is mainly a method to find a global or quasi-minimum for a nonlinear and nonconvex optimization problem, and there have been few studies into optimization problems with discrete decision variables. In this paper, we present the treatment of discrete variables. To treat discrete decision variables as a penalty function, it is possible to treat all decision variables as a continuous decision variable. As a result, the penalty parameter for the penalty function is needed. In this paper, we also present how to determine the penalty parameter for the penalty function. Through mathematical and structural optimization problems, we examine the validity of PSO for the mixed decision variables. (c) 2006 Wiley Periodicals, Inc.
A typical railroad hump yard contains multiple layers of complex operations. The railcars coming with inbound trains through the yard need to be humped into different classification tracks according to the destination...
详细信息
A typical railroad hump yard contains multiple layers of complex operations. The railcars coming with inbound trains through the yard need to be humped into different classification tracks according to the destination, and then assembled to generate the desired outbound trains. During this complex procedure, the processing time of railcars and various resource constraints at different railroad yard facilities could significantly affect the overall performance of yard operations, individually and in combination. It is theoretically challenging to represent a large number of practical operation rules through tractable mathematical programming models. This paper first presents a time-expanded multi-layer network flow model to describe the connection between different layers of yard operations. A mixed integer programming model is developed to optimize the overall performance by jointly considering tightly interconnected facilities. We adopt a cumulative flow count representation to model the spatial capacity constraints in terms of the number of railcars in classification yards. A novel lot-sizing modeling framework and related valid inequality formulations are introduced to model the assembling jobs for outbound trains. We also develop an aggregated flow assignment model and earliest due date-based heuristic rules to determine the humping jobs sequence for reducing the search space. Numerical experiments are conducted to examine the solution quality and computational efficiency under different types of formulation strategies. (C) 2015 Elsevier Ltd. All rights reserved.
This paper proposes a methodology to deal with the fault section estimation problem in electrical power systems. The main motivation for this study lies in the fact that operators of control centers usually are subjec...
详细信息
This paper proposes a methodology to deal with the fault section estimation problem in electrical power systems. The main motivation for this study lies in the fact that operators of control centers usually are subject to information overload during great contingencies. The fault diagnosis is formulated as an optimization problem and solved through two stages: classification of events at equipment level and the fault section estimation. The first stage consists of a heuristic based on Bayes' theorem that provide the input to the second stage where a mixed integer programming model is solved by commercial package in order to determine the faulted section. Several fault scenarios from a real Southern Brazilian subsystem were considered to validate the methodology. The results show that the proposed method can correctly identify the faulted section even in case of multiple faults or in case of improper operation of protective devices. (C) 2015 Elsevier Ltd. All rights reserved.
A trust-region-based derivative free algorithm for solving bound constrained mixedinteger nonlinear programs is developed in this paper. The algorithm is proven to converge to a local minimum after a finite number of...
详细信息
A trust-region-based derivative free algorithm for solving bound constrained mixedinteger nonlinear programs is developed in this paper. The algorithm is proven to converge to a local minimum after a finite number of function evaluations. In addition, an improved definition of local minima of mixedinteger programs is proposed. Computational results showing the effectiveness of the derivative free algorithm are presented.
When wood is to be utilized as a raw material for furniture, buildings etc., it must be dried from approximately 100% to 6% moisture content. This is achieved at least partly in a drying kiln. Heat for this purpose is...
详细信息
When wood is to be utilized as a raw material for furniture, buildings etc., it must be dried from approximately 100% to 6% moisture content. This is achieved at least partly in a drying kiln. Heat for this purpose is provided by electrical means, or by steam from boilers fired with wood chips or oil. By making a close examination of monitored values from an actual drying kiln it has been possible to optimize the use of steam and electricity using the so called mixed integer programming technique. Owing to the operating schedule for the drying kiln it has been necessary to divide the drying process in very short time intervals i.e., a number of minutes. Since a drying cycle takes about two or three weeks, a considerable mathematical problem is presented and this has to be solved.
This article presents a two-stage algorithm for piecewise affine (PWA) regression. In the first stage, a moving horizon strategy is employed to simultaneously estimate the model parameters and to classify the training...
详细信息
This article presents a two-stage algorithm for piecewise affine (PWA) regression. In the first stage, a moving horizon strategy is employed to simultaneously estimate the model parameters and to classify the training data by solving a small-size mixed-integer quadratic programming problem. In the second stage, linear multicategory separation methods are used to partition the regressor space. The framework of PWA regression is adapted to the identification of PWA AutoRegressive with eXogenous input (PWARX) models as well as linear parameter-varying (LPV) models. The performance of the proposed algorithm is demonstrated on an academic example and on two benchmark experimental case studies. The first experimental example concerns modeling the placement process in a pick-and-place machine, while the second one consists in the identification of an LPV model describing the input-output relationship of an electronic bandpass filter with time-varying resonant frequency.
This paper concerns an optimization problem over the efficient set of a multiobjective linear programming problem. We propose and solve an equivalent mixed integer programming (MIP) problem to compute an optimal solut...
详细信息
This paper concerns an optimization problem over the efficient set of a multiobjective linear programming problem. We propose and solve an equivalent mixed integer programming (MIP) problem to compute an optimal solution to the original problem. Compared with the previous MIP approach by Sun, the proposed approach relaxes a strong assumption and reduces the numbers of constraints and binary variables of the MIP problem. By conducting numerical experiments, we find that the proposed approach is more accurate and faster than the previous MIP approach. The proposed MIP problem can be efficiently solved with current state-of-the-art MIP solvers when the objective function is convex or linear.
mixed integer programming (MIP) formulations for scheduling problems can be classified based on the decision variables upon which they rely. In this paper, four different MIP formulations based on four different types...
详细信息
mixed integer programming (MIP) formulations for scheduling problems can be classified based on the decision variables upon which they rely. In this paper, four different MIP formulations based on four different types of decision variables are presented for various parallel machine scheduling problems. The goal of this research is to identify promising optimization formulation paradigms that can subsequently be used to either (1) solve larger practical scheduling problems of interest to optimality and/or (2) be used to establish tighter lower solution bounds for those under study. We present the computational results and discuss formulation efficacy for total weighted completion time and maximum completion time problems for the identical parallel machine case. (C) 2010 Elsevier Ltd. All rights reserved.
A railway signaling system is a complex and interdependent system which should ensure the safe operation of trains. We introduce and address a mixedinteger optimisation model for the preventive signal maintenance cre...
详细信息
A railway signaling system is a complex and interdependent system which should ensure the safe operation of trains. We introduce and address a mixedinteger optimisation model for the preventive signal maintenance crew scheduling problem in the Danish railway system. The problem contains many practical constraints, such as temporal dependencies between crew schedules, the splitting of tasks across multiple days, crew competency requirements and several other managerial constraints. We propose a novel hybrid framework using Constraint programming to generate initial feasible solutions to feed as 'warm start' solutions to a mixed integer programming solver for further improvement. We apply this hybrid framework to a section of the Danish rail network and benchmark our results against both direct application of a mixed integer programming solver and modelling the problem as a Constraint Optimisation Problem. Whereas the current practice of using a general purpose mixed integer programming solver is only able to solve instances over a two-week planning horizon, the hybrid framework generates good results for problem instances over an eight-week period. In addition, the use of a mixed integer programming solver to improve the initial solutions generated by Constraint programming is shown to be significantly superior to addressing the problem as a Constraint Optimisation Problem. (C) 2017 The Authors. Published by Elsevier B.V.
暂无评论