Pull policies may perform quite differently depending on the particular manufacturing system they must control. Hence, it is clear the necessity of having efficient performance evaluation models to select the best con...
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Pull policies may perform quite differently depending on the particular manufacturing system they must control. Hence, it is clear the necessity of having efficient performance evaluation models to select the best control policy in a specific context. This paper proposes a mathematical programming representation of the main pull control policies applied to single-product serial manufacturing systems. The proposed models simulate the pull controlled system in the sense that, if instantiated with the same parameter values as in a simulation model, their solution gives the same event sequence of the simulation. The proposed mathematical representation is also used for a formal comparison of the considered pull control policies. The new models presented in this paper can represent a base to build new efficient optimization algorithms for the design of pull controlled production systems.
A new integrated supply chain scheduling and vehicle routing problem is developed here. The objective is to minimize the total weighted tardiness and transportation costs, with respect to fixed costs of vehicles and t...
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A new integrated supply chain scheduling and vehicle routing problem is developed here. The objective is to minimize the total weighted tardiness and transportation costs, with respect to fixed costs of vehicles and travelling costs of the network. The problem is strong NP-Hard. A mixed integer linear programming and two solution approaches, where one exact procedure based on Branch-and-Bound (B&B) algorithm, and one meta heuristic genetic algorithm (GA) are proposed to solve this problem. Computational experiments are run for both small and large-scale analyses. The results of small-scale analysis indicate that the proposed B&B algorithm provides a more efficient performance, with respect to both number of optimally-solved problems and run times, in comparison with that of the CPLEX software. The results indicate the capability of meta-heuristic GA, in solving real-life large-scale problems in an efficient manner.
In this paper we consider a class of bin packing problems from the literature having the following distinctive feature: items may be fragmented at a price. Problems of this kind arise in diverse application fields lik...
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In this paper we consider a class of bin packing problems from the literature having the following distinctive feature: items may be fragmented at a price. Problems of this kind arise in diverse application fields like logistics and telecommunications, and have already been extensively tackled from an approximation point of view. We focus on the case in which splitting produces no overhead, a fixed number of bins is given and the number of fragments or fragmentations needs to be minimized. We first investigate the theoretical properties of the problem. Then we elaborate on them to devise mathematical programming models and algorithms, yielding both exact optimization algorithms and effective heuristics. An extensive experimental campaign proves that our approach is very effective, and highlights which features make an instance computationally harder to solve. (C) 2013 Elsevier Ltd. All rights reserved.
To solve a classical ill-conditioned problem in the sense of Hadamard as the initial Cauchy problem for a biharmonic operator after some a priori estimates, a posteriori estimates are evaluated using three different m...
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To solve a classical ill-conditioned problem in the sense of Hadamard as the initial Cauchy problem for a biharmonic operator after some a priori estimates, a posteriori estimates are evaluated using three different methods of minimization such as: linear programming, least squares and a recursive projection algorithm for least squares. Numerical comparisons will be made on these three methods.
The goal of simultaneous feature selection and outlier detection is to determine a sparse linear regression vector by fitting a dataset possibly affected by the presence of outliers. The problem is well-known in the l...
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The goal of simultaneous feature selection and outlier detection is to determine a sparse linear regression vector by fitting a dataset possibly affected by the presence of outliers. The problem is well-known in the literature. In its basic version it covers a wide range of tasks in data analysis. Simultaneously performing feature selection and outlier detection strongly improves the application potential of regression models in more general settings, where data governance is a concern. To trigger this potential, flexible training models are needed, with more parameters under control of decision makers. The use of mathematical programming, although pertinent, is scarce in this context and mostly focusing on the least -squares setting. Instead we consider the least absolute deviation criterion, proposing two mixedinteger linear programs, one adapted from existing studies, and the other obtained from a disjunctive programming argument. We show theoretically and computationally that the disjunctive -based formulation is better in terms of both continuous relaxation quality and integer optimality convergence. We experimentally benchmark against existing methodologies from the literature. We identify the characteristics of contamination patterns, in which mathematical programming is better than state-of-the-art algorithms in combining prediction quality, sparsity and robustness against outliers. Additionally, the mathematical programming approaches allow the decision maker to directly control parameters like the number of features or outliers to tolerate, those based on least absolute deviations performing best. On real world datasets, where privacy is a concern, our approach compares well to state-of-the-art methods in terms of accuracy, being at the same time more flexible.
Results of an exhaustive survey of mathematical programming in the Netherlands held in 1982 are presented and, where applicable, compared to a survey held in 1976. It appears that the growth rate has levelled off, tha...
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Results of an exhaustive survey of mathematical programming in the Netherlands held in 1982 are presented and, where applicable, compared to a survey held in 1976. It appears that the growth rate has levelled off, that about half of the largest one hundred industrial firms in the Netherlands now apply MP and that the users are quite satisfied with LP programs except for input, output and documentation. [ABSTRACT FROM AUTHOR]
This paper surveys the use of mathematical programming models for controlling environmental quality. The scope includes air, water, and land quality, stemming from the first works in the 1960s. It also includes integr...
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This paper surveys the use of mathematical programming models for controlling environmental quality. The scope includes air, water, and land quality, stemming from the first works in the 1960s. It also includes integrated models, generally that are economic equilibrium models which have an equivalent mathematical program or use mathematical programming to compute a fixed point. A primary goal of this survey is to identify interesting research avenues for people in mathematical programming with an interest in applying it to help control our environment with as little economic sacrifice as possible.
In 1965 Helmut Lerchs and Ingo Grossmann presented to the mining community an algorithm to find the optimum design for an open pit mine. In their words, "the objective is to design the contour of a pit so as to m...
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In 1965 Helmut Lerchs and Ingo Grossmann presented to the mining community an algorithm to find the optimum design for an open pit mine. In their words, "the objective is to design the contour of a pit so as to maximize the difference between total mine value of the ore extracted and the total extraction cost of ore and waste". They modeled the problem in graph theoretic terms and showed that an optimal solution of the ultimate pit problem is equivalent to finding the maximum closure of their graph based model. In this paper, we develop a network flow algorithm based on the dual to solve the same problem. We show how this algorithm is closely related to Lerchs and Grossmann's and how the steps in their algorithm can be viewed in mathematical programming terms. This analysis adds insight to the algorithm of Lerchs and Grossmann and shows where it can be made more efficient. As in the case Lerchs and Grossmann, our algorithm allows us to use very efficient data structures based on graphs that represent the data and constraints. (C) 1998 Elsevier Science B.V.
This paper presents two complementary mathematical programming based approaches for the accurate safety assessment of semirigid elastoplastic frames under quasistatic loads. The inelastic behavior of the flexible conn...
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This paper presents two complementary mathematical programming based approaches for the accurate safety assessment of semirigid elastoplastic frames under quasistatic loads. The inelastic behavior of the flexible connections and material plasticity are accommodated through piecewise linearized nonlinear yield surfaces. As is necessary for this class of structures, geometric nonlinearity is taken into account. Moreover, only a 2nd-order geometric approximation is included as this is sufficiently accurate for practical structures. The work described has a twofold contribution. First, we develop an algorithm that can robustly and efficiently process the complete (path-dependent) nonholonomic response of the structure in a stepwise (path-independent) holonomic fashion. The governing formulation is cast in mixed static-kinematic variables and lead:;naturally to what is known in the mathematical programming literature as a mixed complementarity problem (MCP). The novelty of the proposed algorithm is that it processes the MCP directly without using some iterative (and often cumbersome) predictor-corrector procedure. Second, in the spirit of simplified analyses, the classical limit analysis approach is extended to compute the limit load multiplier under the simultaneous influence of joint flexibility, material and geometric nonlinearities, and limited ductility. Our formulation is an instance of the challenging class of optimization problems known as a mathematical program with equilibrium constraints (MPEC). Various nonlinear programming based algorithms are proposed to solve the MPEC. Finally, four numerical examples, concerning practical structures and benchmark cases, are provided to illustrate application of the analyses as well as to validate the accuracy and robustness of the proposed schemes. (C) 2010 Elsevier Ltd. All rights reserved.
Fuzzy regression models are widely used to investigate the relationship between explanatory and response variables for many decision-making applications in fuzzy environments. To include more fuzzy information in obse...
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Fuzzy regression models are widely used to investigate the relationship between explanatory and response variables for many decision-making applications in fuzzy environments. To include more fuzzy information in observations, this study uses intuitionistic fuzzy numbers (IFNs) to characterize the explanatory and response variables in formulating intuitionistic fuzzy regression (IFR) models. Different from traditional solution methods, such as the least-squares method, in this study, mathematical programming problems are built up based on the criterion of least absolute deviations to establish IFR models with intuitionistic fuzzy parameters. The proposed approach has the advantages that the model formulation is not limited to the use of symmetric triangular IFNs and the signs of the parameters are determined simultaneously in the model formulation process. The prediction performance of the obtained models is evaluated in terms of similarity and distance measures. Comparison results of the performance measures indicate that the proposed models outperform an existing approach.
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