In this paper, a generalized network optimization model is developed for a complex blood supply chain including a regionalized blood bank system. This system consist of collection sites, testing and processing facilit...
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In this paper, a generalized network optimization model is developed for a complex blood supply chain including a regionalized blood bank system. This system consist of collection sites, testing and processing facilities, storage facilities, distribution centers, as well as points of demand (hospitals). To keep the network in contradiction of the uncertainty, a consolidated approach based on a recently developed stochastic robust approach is extended. An accelerated stochastic Benders decomposition algorithm is proposed to solve the problem modeled in this paper. To speed up the convergence of the solution algorithm, valid inequalities are introduced to get better quality lower bounds. Numerical illustrations are given to verify the mathematical formulation and also to show the benefits of using the stochastic robust approach. At the end, the performance improvements attained by the valid inequalities and the Pareto-optimal cuts are demonstrated in a real-world application.
The paper analyzes convergence conditions of the method of observed mean under nonstandard conditions, where dependent observations of random parameters are used and probabilistic optimization functions may be discont...
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The paper analyzes convergence conditions of the method of observed mean under nonstandard conditions, where dependent observations of random parameters are used and probabilistic optimization functions may be discontinuous indicators. For the case of dependent observations, large deviation type theorems for approximate optimal values and solutions are established.
This work addresses a tactical planning problem faced by a forestry firm, deciding which timber units to harvest and what roads to build to obtain the greatest possible benefits. We include uncertainty in prices by me...
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This work addresses a tactical planning problem faced by a forestry firm, deciding which timber units to harvest and what roads to build to obtain the greatest possible benefits. We include uncertainty in prices by means of utility theory. This enables solutions to be found that the firm finds preferable to those obtained when risk aversion is ignored and makes it possible to design insurance contracts that benefit the firm while also being attractive to an insurer. Two types of contract are designed;one dependent on the firm's operating result and the other independent of it. Metrics are then developed to quantify the benefits conferred by a contract, demonstrating that the latter contract type dominates the former. These results are then illustrated by applying them to a simplified planning problem of a forest owned by the Chilean forestry operator Millalemu.
Extended Linear-Quadratic programming (ELQP) problems were introduced by Rockafellar and Wets for various models in stochastic programming and multistage optimization. Several numerical methods with linear convergence...
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Extended Linear-Quadratic programming (ELQP) problems were introduced by Rockafellar and Wets for various models in stochastic programming and multistage optimization. Several numerical methods with linear convergence rates have been developed for solving fully quadratic ELQP problems, where the primal and dual coefficient matrices are positive definite. We present a two-stage sequential quadratic programming (SQP) method for solving ELQP problems arising in stochastic programming. The first stage algorithm realizes global convergence and the second stage algorithm realizes superlinear local convergence under a condition called B-regularity. B-regularity is milder than the fully quadratic condition;the primal coefficient matrix need not be positive definite. Numerical tests are given to demonstrate the efficiency of the algorithm. Solution properties of the ELQP problem under B-regularity are also discussed.
There is a great deal of interest in addressing humanitarian logistics due to the need for emergency services in the case of disaster. Controlling both operational and disruption uncertainties in the emergency managem...
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There is a great deal of interest in addressing humanitarian logistics due to the need for emergency services in the case of disaster. Controlling both operational and disruption uncertainties in the emergency management is one of challenging topics lately to propose a robust plan for humanitarian logistics. Designing a robust and resilient humanitarian relief chain networks under both operational and disruptive risks can ensure the delivery of the essential supplies to beneficiaries. In this paper, a humanitarian logistic network design with multiple central warehouses and local distribution centres in an integrated manner is addressed by a novel scenario-based possibilistic-stochastic programming approach. The main real-life application of the proposed methodology is to consider the transportation network's routes after an earthquake to provide a plan against uncertainty in whole levels of supply chain along with its availability. To this end, a real case study of Mazandaran province in the north of Iran is provided to validate our methodology as well as a comprehensive discussion and managerial insights are concluded from the results.
stochastic programming is concerned with practical procedures for decision making under uncertainty, by modelling uncertainties and risks associated with decision in a form suitable for optimization. The field is deve...
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stochastic programming is concerned with practical procedures for decision making under uncertainty, by modelling uncertainties and risks associated with decision in a form suitable for optimization. The field is developing rapidly with contributions from many disciplines such as operations research, probability and statistics, and economics. A stochastic linear program with recourse can equivalently be formulated as a convex programming problem. The problem is often large-scale as the objective function involves an expectation, either over a discrete set of scenarios or as a multi-dimensional integral. Moreover, the objective function is possibly nondifferentiable. This paper provides a brief overview of recent developments on smooth approximation techniques and Newton-type methods for solving two-stage stochastic linear programs with recourse, and parallel implementation of these methods. A simple numerical example is used to signal the potential of smoothing approaches. (C) 2000 Elsevier Science Ltd. All rights reserved.
This article presents a model to assist decision makers in the logistics of a flood emergency. The model attempts to optimize inventory levels for emergency supplies as well as vehicles' availability, in order to ...
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This article presents a model to assist decision makers in the logistics of a flood emergency. The model attempts to optimize inventory levels for emergency supplies as well as vehicles' availability, in order to deliver enough supplies to satisfy demands with a given probability. A spatio-temporal stochastic process represents the flood occurrence. The model is approximately solved with sample average approximation. The article presents a method to quantify the impact of the various intervening logistics parameters. An example is provided and a sensitivity analysis is performed. The studied example shows large differences between the impacts of logistics parameters such as number of products, number of periods, inventory capacity and degree of demand fulfillment on the logistics cost and time. This methodology emerges as a valuable tool to help decision makers to allocate resources both before and after a flood occurs, with the aim of minimizing the undesirable effects of such events. (C) 2014 Elsevier Ltd. All rights reserved.
The estimation of Levy process has received a lot of attention in recent years. Evidence of this is the extensive amount of literature concerning this problem which can be classified in two categories: the nonparametr...
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The estimation of Levy process has received a lot of attention in recent years. Evidence of this is the extensive amount of literature concerning this problem which can be classified in two categories: the nonparametric approach, and the parametric approach. In this paper, we shall concentrate on the latter, and in particular the parameters will be estimated within a stochastic programming framework. To be more specific, the first derivative of the characteristic function and its empirical version shall be used in objective function. Furthermore, the parameter estimates are recursively estimated by making use of a modified extended Kalman filter (MEKF). Some properties of the parameter estimates are studied. Finally, a number of simulations will be carried out and the results are presented and discussed.
This paper gives a rigorous definition of a stage, usable for dynamic stochastic programs with both recourse and probabilistic constraints. Algebraic modelling languages can make use of this definition for automatic c...
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This paper gives a rigorous definition of a stage, usable for dynamic stochastic programs with both recourse and probabilistic constraints. Algebraic modelling languages can make use of this definition for automatic consistency checks. (C) 2004 Elsevier B.V. All rights reserved.
An optimal reliability level of a composite power system should correspond to a system expansion scheme which minimizes the total cost, including operation and maintenance cost, system expansion cost, and customer out...
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An optimal reliability level of a composite power system should correspond to a system expansion scheme which minimizes the total cost, including operation and maintenance cost, system expansion cost, and customer outage cost. At this optimal reliability level, the value of service is maximized. A stochastic programming approach for identifying the required optimal reliability level and the corresponding expansion scheme is proposed in this paper. This approach employs linear programming and Benders' decomposition algorithm. It has been applied to two sample systems and the results are presented.
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