This paper investigates an integrated scheduling of production and distribution activities in the supply chain where both machine deterioration and learning effects have been consequently addressed. Manufacturer aims ...
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This paper investigates an integrated scheduling of production and distribution activities in the supply chain where both machine deterioration and learning effects have been consequently addressed. Manufacturer aims to minimize the total weighted completion time, while a distributor focuses on reducing shipping times with batch delivery by using capacitated vehicles. The aim of this problem is to minimize the sum of weighted completion times plus total delivery times. First, a mixed integer linear programming model is proposed. Then for a special case, a branch and bound algorithm is developed with utilizing the structural features of the problem. In order to solve large-scale instances of the general problem in a short/reasonable time, a simulated annealing algorithm is provided. Computational results show that the proposed heuristic techniques have high efficiency to achieve the optimal solution, and that they are useful to solve large sizes of the problems at a short time. Finally, by providing a real-life case of wax manufacturing and its distribution system, it is shown that the application of integrated decisions can significantly reduce costs imposed on the firms.
Wind turbine blades are usually made in batches and then matched in pairs or triples to form the rotor. This paper considers matching with the aim of avoiding or reducing the need for further adjustment of, say, the b...
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Wind turbine blades are usually made in batches and then matched in pairs or triples to form the rotor. This paper considers matching with the aim of avoiding or reducing the need for further adjustment of, say, the blade mass. Matching is considered optimal when it minimises the sum of the squared differences in the chosen matching parameter for all blade pairs or triples in a batch. It is proved that use of a simple parameter such as blade mass, or centre of mass, leads to optimal matching by ordering in terms of that parameter. More complex matching based on, say, minimising the eccentricity of the centre of mass, causes the parameter for one blade to depend on at least one other blade. Then ordering does not necessarily produce the optimal matching but, in all cases considered, it comes very close. A branch and bound algorithm is developed for complex matching and is shown to provide the optimal matching in a realistic time for batches of at least 20 blades.
In this article we develop a bounding procedure which is applied in an algorithm for determining the number of back-up components to include in a series system. Operating only when original units fail, back-up compone...
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In this article we develop a bounding procedure which is applied in an algorithm for determining the number of back-up components to include in a series system. Operating only when original units fail, back-up components increase system reliability. They also add weight, take up space, and cost money. Maximizing the reliability of nuclear power plants, ships, aircraft, life support systems and electronic instruments leads to this class of resource allocation problem. Because the procedure requires only that the rate of failure of individual components remains constant or increases with time, the algorithm can accommodate a wide variety of failure probability distributions. In applications, the failure probability for various types of components might belong to different families and this can be readily handled by the algorithm presented.
We examine a non-cyclic scheduling problem of a wet station that performs cleaning processes for removing residual contaminants on wafer surfaces. Several chemical and rinse baths, and multiple robots for transporting...
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We examine a non-cyclic scheduling problem of a wet station that performs cleaning processes for removing residual contaminants on wafer surfaces. Several chemical and rinse baths, and multiple robots for transporting jobs are linearly combined in a wet station. A wet station in a fab tends to have different types of jobs. Therefore, it is realistic to consider non-cyclic release of jobs into a wet station. We therefore examine a non-cyclic scheduling problem of a wet station that determines the task sequence of each robot so as to minimize the makespan of a given sequence of different jobs. We develop an efficient branch and bound procedure by examining the scheduling problem. To do this, we first develop a Petri net model for the scheduling problem. By identifying deadlock prevention conditions from the Petri net model, we eliminate partial solutions in advance that eventually will lead to a deadlock. By examining the feasible transition firings or state transition behavior of the Petri net model, we branch only feasible partial solutions or nodes that correspond to feasible state transitions or transition firings. We also develop a tight lower bound based on the bottleneck workload of the baths. We prove computational efficiency of the branch and bound procedure for practical problems.
Generalizations of the branch and bound method and of the Piyavskii method for solution of stochastic global optimization problems are considered. These methods employ the concept of a tangent minorant of an objective...
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Generalizations of the branch and bound method and of the Piyavskii method for solution of stochastic global optimization problems are considered. These methods employ the concept of a tangent minorant of an objective function as a source of global information about the function. Calculus of tangent minorants is developed.
Let G(N;A) be a connected, undirected and weighted network with node set N and edge set A. Suppose that there is an available budget to spend on removing edges and there is a removal cost associated with each edge. Th...
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Let G(N;A) be a connected, undirected and weighted network with node set N and edge set A. Suppose that there is an available budget to spend on removing edges and there is a removal cost associated with each edge. The most vital edges problem is to find a set of edges such that the total removal cost is not greater than the available budget and whose removal from G(N;A) results in the greatest increase in the total weight of a minimum spanning tree. We show that this problem is NP-hard and propose a branch and bound algorithm to solve it.
This paper addresses a non-myopic sensor-scheduling problem of how to select and assign active sensors for trading off the tracking accuracy and the radiation risk, where the radiation risk is incurred by the fact tha...
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This paper addresses a non-myopic sensor-scheduling problem of how to select and assign active sensors for trading off the tracking accuracy and the radiation risk, where the radiation risk is incurred by the fact that the emission energy originating from active sensors for target tracking can be intercepted by the enemy target. This problem is formulated as a mixed partially observable Markov decision process (POMDP) composed of a continuous-state POMDP for target tracking and a discrete-state POMDP for emission control. Based on the idea of foresight optimization, the long-term accuracy reward is evaluated by the combination of unscented transformation sampling and Kalman filtering, whereas the long-term radiation cost is derived from hidden Markov model filter. Because the problem can be converted into a decision tree, a branch and bound algorithm is developed for problem solution. A simulation example illustrates the effectiveness of our approach.
Hyperspectral reflectance imaging data are analyzed for poultry skin tumor detection. We consider selecting only a few wavebands from hyperspectral data for potential use in a real-time multispectral camera. To do thi...
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Hyperspectral reflectance imaging data are analyzed for poultry skin tumor detection. We consider selecting only a few wavebands from hyperspectral data for potential use in a real-time multispectral camera. To do this, we improve our prior tumor detection system by employing our new adaptive branch and bound algorithm and a support vector machine classifier. Our HS analysis is useful since it provides a guideline for selection of the specific wavelengths for best tumor detection (feature selection). Experimental results demonstrate that our optimal adaptive branch and bound algorithm is significantly faster than other versions of the branch and bound algorithm. We compare the performance of our feature selection algorithm to that of a feature extraction algorithm and show that using our feature selection algorithm gives a better tumor detection rate and a lower false alarm rate. (C) 2009 Elsevier Ltd. All rights reserved.
This paper is concerned with a portfolio optimization problem under concave and piecewise constant transaction cost. We formulate the problem as nonconcave maximization problem under linear constraints using absolute ...
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This paper is concerned with a portfolio optimization problem under concave and piecewise constant transaction cost. We formulate the problem as nonconcave maximization problem under linear constraints using absolute deviation as a measure of risk and solve it by a branch and bound algorithm developed in the field of global optimization. Also, we compare it with a more standard 0-1 integer programming approach. We will show that a branch and bound method elaborating the special structure of the problem can solve the problem much faster than the state-of-the integer programming code.
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