The role of some factors is not completely clear as an input or an output in many real applications of Data Envelopment Analysis (DEA). In other words, a factor can be used as an input by some Decision-Making Units (D...
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The role of some factors is not completely clear as an input or an output in many real applications of Data Envelopment Analysis (DEA). In other words, a factor can be used as an input by some Decision-Making Units (DMUs) while it may play an output role in other DMUs. This type of factors are called flexible factors. To improve the models for determining the type of flexible factors, two models were presented in this article in which flexible factors are added to be used in the best situation. In this way, factors in both output and input roles will optimize productivity with maximum efficiency without any change in the productivity level. The proposed models showed more consistency to achieve the desired goals in DEA as compared to similar models. Eventually, the proposed models were compared with existing models through a numerical example. (C) 2019 Elsevier Ltd. All rights reserved.
We present a computational method for performing structural translation, which has been studied recently in the context of analyzing the steady states and dynamical behavior of mass-action systems derived from biochem...
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We present a computational method for performing structural translation, which has been studied recently in the context of analyzing the steady states and dynamical behavior of mass-action systems derived from biochemical reaction networks. Our procedure involves solving a binary linear programming problem where the decision variables correspond to interactions between the reactions of the original network. We call the resulting network a reaction-to-reaction graph and formalize how such a construction relates to the original reaction network and the structural translation. We demonstrate the efficacy and efficiency of the algorithm by running it on 508 networks from the European Bioinformatics Institutes' BioModels database. We also summarize how this work can be incorporated into recently proposed algorithms for establishing mono- and multistationarity in biochemical reaction systems.
Purpose This study aims to show how cost savings can be achieved through optimizing the scheduling of e-commerce enablements. The University of California is one of the largest, most prestigious public education and r...
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Purpose This study aims to show how cost savings can be achieved through optimizing the scheduling of e-commerce enablements. The University of California is one of the largest, most prestigious public education and research systems in the world, yet diminished state support is driving the search for system-wide cost savings. Design/methodology/approach This study documents the preparation for and rollout of an e-procurement system across a subset of campuses. A math programing tool was developed for prioritizing the gradual rollout to generate the greatest expected savings subject to resource constraints. Findings The authors conclude by summarizing the results of the rollout, discussing lessons learned and their benefit to decision-makers at other public institutions. Originality/value The pilot program comprising three campuses has been predicted to yield $1.2m in savings over a one-year period;additional sensitivity analysis with respect to savings, project timelines and other rollout decisions illustrate the robustness of these findings.
Several studies have found methods of optimizing order allocation and trim loss in paper industry. However, the resulting solution may not be adoptable due to some widths of stocked rolls exceed or less than those req...
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ISBN:
(纸本)9781538679722
Several studies have found methods of optimizing order allocation and trim loss in paper industry. However, the resulting solution may not be adoptable due to some widths of stocked rolls exceed or less than those required for acceptable scheduling. To meet actual customer demand and better suitability of results, a new decision model has been presented which can be used for simultaneous cutting of master reels and stocked rolls with limitation on cutting blades at winder. The objective is to fulfill customer need using minimum possible number of master reels and stocked rolls. The problem is solved by simplex method which generally leads to non-integer optimal solution. Phase-II is solved by a new method to make the final solution integer using binary programming. Mathematical and decision models are given and a numerical example has been solved to illustrate the applicability of proposed method.
In this work, we propose a novel convex dual approach to the multidimensional dimensional assignment problem, which is an NP-hard binary programming problem. It is shown that the proposed dual approach is equivalent t...
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In this work, we propose a novel convex dual approach to the multidimensional dimensional assignment problem, which is an NP-hard binary programming problem. It is shown that the proposed dual approach is equivalent to the Lagrangian relaxation method in terms of the best value attainable by the two approaches. However, the pure dual representation is not only more elegant, but also makes the theoretical analysis of the algorithm more tractable. In fact, we obtain a su cient and necessary condition for the duality gap to be zero, or equivalently, for the Lagrangian relaxation approach to nd the optimal solution to the assignment problem with a guarantee. Also, we establish a mild and easy-to-check condition, under which the dual problem is equivalent to the original one. In general cases, the optimal value of the dual problem can provide a satisfactory lower bound on the optimal value of the original assignment problem. We then extend the purely dual formulation to handle the more general multidimensional assignment problem. The convex dual representation is derived and its relationship to the Lagrangian relaxation method is investigated once again. Also, we discuss the condition under which the duality gap is zero. It is also pointed out that the process of Lagrangian relaxation is essentially equivalent to one of relaxing the binary constraint condition, thus necessitating the auction search operation to recover the binary constraint. Furthermore, a numerical algorithm based on the dual formulation along with a local search strategy is presented. Finally, the newly proposed algorithm is shown to outperform the Lagrangian relaxation method in a number of multi-target tracking simulations.
Mathematical programming has been often used to optimize municipal solid waste management and transfer systems. The objective of this work was to develop a practical methodology to aid in the optimal design of a solid...
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Mathematical programming has been often used to optimize municipal solid waste management and transfer systems. The objective of this work was to develop a practical methodology to aid in the optimal design of a solid waste collection network in regions with well-specified boundaries. The objective function was a non-linear equation that minimized total collection cost. The cost comprised the capital and operating costs of: (i) the waste transfer stations, (ii) the waste collection vehicles, (iii) the semitrailers and tractors as well as the waste collection within a community, and the cost to haul the wastes to the transfer stations or to the landfills. The adjustable (decision) variables were binary variables that designated whether a path between two nodes is valid or not. binary variables were also used to designate whether a transfer station should be constructed or not. In this methodology, the waste production nodes and their waste production rates were specified. The locations of all candidate waste transfer stations were designated using two alternative GIS-based siting methodologies;the locations of the final nodes (landfills) were precisely specified too. The actual travel distances and times among all nodes were the main input variables. The model was developed in an Excel (R) spreadsheet and was applied to a Hellenic region that has 53 municipalities. The candidate transfer stations sited in the region were 47 and one or two landfills were present in the system. The optimal solution suggested that 47 and 6 municipalities should direct their wastes to 12 transfer stations and to 2 landfills, respectively. The 12 transfer stations should then transfer their wastes to their adjacent landfills. The optimal collection cost was (sic)42.4 t(-1). A sensitivity analysis concluded that fuel cost was the most sensitive parameter in the model. (C) 2011 Elsevier B.V. All rights reserved.
We present a method for finding solutions of large-scale binary programming problems where the calculation of derivatives is very expensive. We then apply this method to a topology optimization problem of weight minim...
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We present a method for finding solutions of large-scale binary programming problems where the calculation of derivatives is very expensive. We then apply this method to a topology optimization problem of weight minimization subject to compliance and buckling constraints. We derive an analytic expression for the derivative of the stress stiffness matrix with respect to the density of an element in the finite-element setting. Results are presented for a number of two-dimensional test *** (c) 2012 John Wiley & Sons, Ltd.
This thesis deals with one of the possible different approaches to solving nonlinear optimization problems by convertion to finding non-bounded extrema of function, where constrains are transfered to objective functio...
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This thesis deals with one of the possible different approaches to solving nonlinear optimization problems by convertion to finding non-bounded extrema of function, where constrains are transfered to objective function via penalty function. We will introduce exterior penalty function method and appropriate algorithm for solving this type for problems. The thesis also deals with exact penalty functions, which do not requires limit approximation of the penalty pa- rameter to infinity. Then we deal with integer binary nonlinear progamming, where several suitable penalty functions are presented to solve this type of pro- blem. In the numerical part, the thesis deals with the minimization of risk at the specifed minimum expected return on the sparse portfolio. We observe the effect of changing the penalty parameter on the results of ten different minimization problems calculating risk of sparsity portfolios. 1
Novel context out learning approach is discussed as possibility of using simple classifiers which is background of hidden class system. There are two ways how to perform final classification. Having a lot of hidden cl...
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Novel context out learning approach is discussed as possibility of using simple classifiers which is background of hidden class system. There are two ways how to perform final classification. Having a lot of hidden cl...
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Novel context out learning approach is discussed as possibility of using simple classifiers which is background of hidden class system. There are two ways how to perform final classification. Having a lot of hidden classes we can build their unions using binary optimization task. Resulting system has the best possible sensitivity over all output classes. Another way is to perform second level linear classification as referential approach. The presented techniques are demonstrated on traditional iris flower task.
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