We consider control design for positive compartmental systems in which each compartment's outflow rate is described by a concave function of the amount of material in the compartment. We address the problem of det...
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We consider control design for positive compartmental systems in which each compartment's outflow rate is described by a concave function of the amount of material in the compartment. We address the problem of determining the routing of material between compartments to satisfy time-varying state constraints while ensuring that material reaches its intended destination over a finite time horizon. We give sufficient conditions for the existence of a time-varying state-dependent routing strategy which ensures that the closed-loop system satisfies basic network properties of positivity, conservation and interconnection while ensuring that capacity constraints are satisfied, when possible, or adjusted if a solution cannot be found. These conditions are formulated as a linear programming problem. Instances of this linear programming problem can be solved iteratively to generate a solution to the finite horizon routing problem. Results are given for the application of this control design method to an example problem. Published by Elsevier Ltd.
This paper presents a hybrid algorithm of linear programming (LP), max-min ant system, and local search for solving large instances of the k-covering problem (SCkP). This algorithm exploits the LP-relaxation solution ...
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This paper presents a hybrid algorithm of linear programming (LP), max-min ant system, and local search for solving large instances of the k-covering problem (SCkP). This algorithm exploits the LP-relaxation solution by classifying the columns, based on their reduced costs, into three sets, such that two of these sets have the columns that need to be included or excluded from any solution while ants search the third set, the selection set, to construct their feasible solutions. Moreover, to choose high-quality columns from the selection set, ants rely on heuristic information derived from the rows' dual costs, which we obtain from the LP-relaxation solution as well. To benchmark our algorithm, we solve a set of 135 instances and compare the results with those of the state-of-the-art algorithm, in addition to the best-known solutions obtained using a branch and bound algorithm. Our algorithm shows superior results in terms of solution quality and computation time. Moreover, it can identify two new best-known solutions. (C) 2016 Elsevier Ltd. All rights reserved.
Component deployment is a combinatorial optimisation problem in software engineering that aims at finding the best allocation of software components to hardware resources in order to optimise quality attributes, such ...
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Component deployment is a combinatorial optimisation problem in software engineering that aims at finding the best allocation of software components to hardware resources in order to optimise quality attributes, such as reliability. The problem is often constrained because of the limited hardware resources, and the communication network, which may connect only certain resources. Owing to the non-linear nature of the reliability function, current optimisation methods have focused mainly on heuristic or metaheuristic algorithms. These are approximate methods, which find near-optimal solutions in a reasonable amount of time. In this paper, we present a mixed integer linear programming (MILP) formulation of the component deployment problem. We design a set of experiments where we compare the MILP solver to methods previously used to solve this problem. Results show that the MILP solver is efficient in finding feasible solutions even where other methods fail, or prove infeasibility where feasible solutions do not exist.
In this paper we address the problem of representing the continuous but non-convex set of non dominated points of a multi-objective linear programme by a finite subset of such points. We prove that a related decision ...
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In this paper we address the problem of representing the continuous but non-convex set of non dominated points of a multi-objective linear programme by a finite subset of such points. We prove that a related decision problem is NP-complete. Moreover, we illustrate the drawbacks of the known global shooting, normal boundary intersection and normal constraint methods concerning the coverage error and uniformity level of the representation by examples. We propose a method which combines the global shooting and normal boundary intersection methods. By doing so, we overcome their limitations, but preserve their advantages. We prove that our method computes a set of evenly distributed non-dominated points for which the coverage error and the uniformity level can be guaranteed. We apply this method to an optimisation problem in radiation therapy and present illustrative results for some clinical cases. Finally, we present numerical results on randomly generated examples. (C) 2016 Elsevier B.V. All rights reserved.
In recent years, the number of direct flights between Taiwan and mainland China has grown rapidly, as charter flights have been turned into regular flights. This important issue has prompted airport ground handling se...
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In recent years, the number of direct flights between Taiwan and mainland China has grown rapidly, as charter flights have been turned into regular flights. This important issue has prompted airport ground handling service (AGHS) companies in Taiwan to enhance convenient services for passengers and to invest in airport logistics center expansion plans (ALCEP) to broaden the AGHS market. Due to their budgetary restrictions, AGHS companies need to outsource many of their services to contractors to implement these plans. This study proposes an ALCEP solution procedure to guide AGHS companies in adjusting their priority goals and selecting the best contractor according to their needs. This proposed procedure successfully solves the ALCEP problem and facilitates the assignment of contractors by considering both qualitative and quantitative methods. (C) 2016 Elsevier B.V. All rights reserved.
Bilevel linear programming (BLP) is a solution method for linear optimization problem with two sequential decision steps of the leader and the follower. In this paper, we assume that the follower's objective funct...
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ISBN:
(纸本)9781509049189
Bilevel linear programming (BLP) is a solution method for linear optimization problem with two sequential decision steps of the leader and the follower. In this paper, we assume that the follower's objective function is imprecise and can be represented by a fuzzy function, the BLP with the follower's fuzzy objective function (BLPwFFO). We apply the approach of necessity measure optimization to obtain the global optimal solution for the leader. This solution is not only secure but comprehensively reflects the follower's rational reaction. In the case that the follower's coefficient vector is defined by the convex polyhedron fuzzy set, our proposed BLPwFFO is formulated as a special kind of three-level programming problem. Because an optimal solution exists at a vertex of feasible region, we use the k-th best method to search for the global optimal solution. The numerical example is used to demonstrate our computational method.
With a common background and motivation, the main contributions of this paper are developed in two different directions. Firstly, we are concerned with functions, which are the maximum of a finite amount of continuous...
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With a common background and motivation, the main contributions of this paper are developed in two different directions. Firstly, we are concerned with functions, which are the maximum of a finite amount of continuously differentiable functions of n real variables, paying special attention to the case of polyhedral functions. For these max-functions, we obtain some results about outer limits of subdifferentials, which are applied to derive an upper bound for the calmness modulus of nonlinear systems. When confined to the convex case, in addition, a lower bound on this modulus is also obtained. Secondly, by means of a Karush-Kuhn-Tucker index set approach, we are also able to provide a point-based formula for the calmness modulus of the argmin mapping of linear programming problems, without any uniqueness assumption on the optimal set. This formula still provides a lower bound in linear semi-infinite programming. Illustrative examples are given.
Using Cholesky factorization, the dual face algorithm was described forsolving standard linear programming (LP) problems, as it would not be very suitablefor sparse computations. To dodge this drawback, this paper pre...
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Using Cholesky factorization, the dual face algorithm was described forsolving standard linear programming (LP) problems, as it would not be very suitablefor sparse computations. To dodge this drawback, this paper presents a variant usingGauss-Jordan elimination for solving bounded-variable LP problems.
Background: Prediction of de novo protein-protein interaction is a critical step toward reconstructing PPI networks, which is a central task in systems biology. Recent computational approaches have shifted from making...
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Background: Prediction of de novo protein-protein interaction is a critical step toward reconstructing PPI networks, which is a central task in systems biology. Recent computational approaches have shifted from making PPI prediction based on individual pairs and single data source to leveraging complementary information from multiple heterogeneous data sources and partial network structure. However, how to quickly learn weights for heterogeneous data sources remains a challenge. In this work, we developed a method to infer de novo PPIs by combining multiple data sources represented in kernel format and obtaining optimal weights based on random walk over the existing partial networks. Results: Our proposed method utilizes Barker algorithm and the training data to construct a transition matrix which constrains how a random walk would traverse the partial network. Multiple heterogeneous features for the proteins in the network are then combined into the form of weighted kernel fusion, which provides a new "adjacency matrix" for the whole network that may consist of disconnected components but is required to comply with the transition matrix on the training subnetwork. This requirement is met by adjusting the weights to minimize the element-wise difference between the transition matrix and the weighted kernels. The minimization problem is solved by linear programming. The weighted kernel fusion is then transformed to regularized Laplacian (RL) kernel to infer missing or new edges in the PPI network, which can potentially connect the previously disconnected components. Conclusions: The results on synthetic data demonstrated the soundness and robustness of the proposed algorithms under various conditions. And the results on real data show that the accuracies of PPI prediction for yeast data and human data measured as AUC are increased by up to 19 % and 11 % respectively, as compared to a control method without using optimal weights. Moreover, the weights learned by our me
The relevance of planning non-hierarchical supply chains has increased due to growing collaboration among industrial and logistic organizations once this planning approach aims to optimize the supply chain while prese...
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