A public system is faced with a large amount of practical issues which have to be solved to make the services offered to the citizens more efficient. This paper deals with the common problem of costs reduction in the ...
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A public system is faced with a large amount of practical issues which have to be solved to make the services offered to the citizens more efficient. This paper deals with the common problem of costs reduction in the organization of the municipal household refuse collection. First a linear model for the standard problem is applied to the data of the city of Brescia. Then a generalization of the model based on the modern concept of separate refuse collection is presented and examples obtained on data for the city of Brescia are given, assuming the separate collection of two and three different types of refuse. In both cases the solutions of the models give an insight for a more efficient management of the refuse service. (C) 1995 Elsevier Science Ltd. All rights reserved.
Lower and upper bounds on the union probability for N events are derived in terms of the individual and pairwise event probabilities by solving a linear program with variables. The bounds, which can be efficiently det...
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Lower and upper bounds on the union probability for N events are derived in terms of the individual and pairwise event probabilities by solving a linear program with variables. The bounds, which can be efficiently determined, are shown to be optimal when and are always sharper than recent optimal bounds which use slightly less information. Their competitive sharpness is also illustrated via numerical comparisons with state-of-the-art bounds in the literature.
linear programming is a tool that has yet to reach its full potential in power system engineering. To illustrate in a tutorial style how it is currently being applied and how its use evolved, applications are outlined...
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linear programming is a tool that has yet to reach its full potential in power system engineering. To illustrate in a tutorial style how it is currently being applied and how its use evolved, applications are outlined in three areas: generation scheduling, loss minimization through allocation of reactive power supply, and planning of capital investments in generation equipment. The applications include not only linear programming but also its extensions to integer and quadratic programming and to the use of Benders and Dantzig-Wolfe decomposition techniques. The planning issues discussed show the limitations of traditional engineering economics to power system planning. This occurs when them is a spread between the interest rates for lending and for borrowing funds and also when investment funds have limits and thus are rationed. The result of this review is the recommendation that power system planning models should incorporate financial flows with the linear programming approach to capital budgeting originally formulated in 1963 by H. M. Weingartner. The need for such an approach is illustrated in the appendix with examples of how capital market conditions can upset the type of engineering economic decision making currently used in planning models. The Lagrangian relaxation method, which can extend computational feasibility for linear and integer programming, is also described in the appendix.
This research is about developing a decision support system (DSS) for the distribution of grapes and grape must in a Chilean cooperative, Cooperativa Agricola Pisquera Elqui Limitada (CAPEL). CAPEL is dedicated to pro...
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This research is about developing a decision support system (DSS) for the distribution of grapes and grape must in a Chilean cooperative, Cooperativa Agricola Pisquera Elqui Limitada (CAPEL). CAPEL is dedicated to producing and distributing several beverages such as sparkling wines, beers, energy drinks, rum, and pisco. This work aims to support the grinding-related transport stage through a linear programming based DSS, in order to find the optimal use of the transport demand in a network based on source and destination plants during the harvest season. To achieve this aim, an operational research (OR) model that feeds the DSS is developed, whose objective function seeks to minimize the total transport cost. The decision variables define the grape cargo to be transported from a source plant to a destination plant. The OR model uses constraints such as transportation demand, grinding capacity, maximum storage, and available grape in plants. The model succeeded in reducing the total transport costs by 14% for the 2017 season of the pisco-making process, meaning approximately savings of 59 million Chilean pesos.
A novel optimum extreme learning machines (ELM) construction method was proposed. We define an extended covering matrix with smooth function, relax the objective and constraints to formulate a more general linear prog...
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A novel optimum extreme learning machines (ELM) construction method was proposed. We define an extended covering matrix with smooth function, relax the objective and constraints to formulate a more general linear programming method for the minimum sphere set covering problem. We call this method linear programming minimum sphere set covering (LPMSSC). We also present a corresponding kernelized LPMSSC and extended LPMSSC with non-Euclidean L1 and L-infinity metric. We then propose to apply the LPMSSC method to ELM and propose a data dependent ELM (DDELM) algorithm. We can obtain compact ELM for pattern classification via LPMSSC. We investigate the performances of the proposed method through UCI benchmark data sets. (c) 2007 Elsevier B.V. All rights reserved.
The purpose of this paper is to develop a linear programming methodology for solving multiattribute group decision making problems using intuitionistic fuzzy (IF) sets. In this methodology, IF sets are constructed to ...
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The purpose of this paper is to develop a linear programming methodology for solving multiattribute group decision making problems using intuitionistic fuzzy (IF) sets. In this methodology, IF sets are constructed to capture fuzziness in decision information and decision making process. The group consistency and inconsistency indices are defined on the basis of pairwise comparison preference relations on alternatives given by the decision makers. An IF positive ideal solution (IFPIS) and weights which are unknown a priori are estimated using a new auxiliary linear programming model, which minimizes the group inconsistency index under some constraints. The distances of alternatives from the IFPIS are calculated to determine their ranking order. Moreover, some properties of the auxiliary linear programming model and other generalizations or specializations are discussed in detail. Validity and applicability of the proposed methodology are illustrated with the extended air-fighter selection problem and the doctoral student selection problem. (C) 2010 Elsevier Inc. All rights reserved.
In this paper, the RCPSP (resource constrained project scheduling problem) is solved using a linear programming model. Each activity may or may not be preemptive. Each variable is associated to a subset of independent...
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In this paper, the RCPSP (resource constrained project scheduling problem) is solved using a linear programming model. Each activity may or may not be preemptive. Each variable is associated to a subset of independent activities (anti-chains). The properties of the model are first investigated. In particular, conditions are given that allow a solution of the linear program to be a feasible schedule. From these properties, an algorithm based on neighbourhood search is derived. One neighbour solution is obtained through one Simplex pivoting, if this pivoting preserves feasibility. Methods to get out of local minima are provided. The solving methods are tested on the PSPLIB instances in a preemptive setting and prove efficient. They are used when preemption is forbidden with less success, and this difference is discussed. (c) 2006 Elsevier B.V. All rights reserved.
Recent work by Han and Van Roy [Han J, Van Roy B (2011) Control of diffusions via linear programming. Infanger G, ed. Stochastic programming: The State of the Art, in Honor of George B. Dantzig (Springer, New York), 3...
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Recent work by Han and Van Roy [Han J, Van Roy B (2011) Control of diffusions via linear programming. Infanger G, ed. Stochastic programming: The State of the Art, in Honor of George B. Dantzig (Springer, New York), 329-354] introduced a linear programming technique to compute good suboptimal solutions to high-dimensional control problems in a diffusion-based setting. Their problem formulation worked with finite horizon problems where the horizon, T, is an exponentially distributed random variable. We extend their approach to finite horizon problems with a fixed horizon T. We also apply these techniques to dynamic portfolio optimization problems and then simulate the resulting policies to obtain lower bounds on the optimal value functions. We also use these policies in conjunction with convex duality methods designed for portfolio optimization problems to construct upper bounds on the optimal value functions. In our numerical experiments we find that the primal and dual bounds are very close, and so we conclude, for these problems at least, that the linear programming approach performs very well.
This paper deals with the problem of stabilizing linear discrete-time systems under state and control linear constraints using linear programming techniques. linear state constraints describe a polyhedron in the state...
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This paper deals with the problem of stabilizing linear discrete-time systems under state and control linear constraints using linear programming techniques. linear state constraints describe a polyhedron in the state space so that the problem considered is to make such a polyhedron positively invariant while the control does not violate its constraints. For this, necessary and sufficient conditions are given for the existence of a solution of the problem in terms of polyhedron's vertices and directions. These conditions are described by a set of linear constraints and, following the approach introduced by Vassilaki et al., they can be solved using linear programming techniques. The objective function proposed here turns out to be a natural one when describing the constraints in terms of polyhedron's vertices and directions.
We propose online decision strategies for time-dependent sequences of linear programs which use no distributional and minimal geometric assumptions about the data. These strategies are obtained through Vovk's aggr...
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We propose online decision strategies for time-dependent sequences of linear programs which use no distributional and minimal geometric assumptions about the data. These strategies are obtained through Vovk's aggregating algorithm which combines recommendations from a given strategy pool. We establish an average-performance bound for the resulting solution sequence. (C) 2006 Elsevier B.V. All rights reserved.
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