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Time and cost tradeoff for distributed data processing
 Computers ind. Engng
, 1989
"... AbstractAn important design issue in distributed data processing systems is to determine optimal data distribution. The problem requires a tradeoff between time and cost. For instance, quick response time conflicts with low cost. The paper addresses the data distribution problem in this conflictin ..."
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Cited by 3 (1 self)
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AbstractAn important design issue in distributed data processing systems is to determine optimal data distribution. The problem requires a tradeoff between time and cost. For instance, quick response time conflicts with low cost. The paper addresses the data distribution problem in this conflicting environment. A formulation of the problem as a nonlinear program is developed. An algorithm employing a simple search procedure is presented, which gives an optimal data distribution. An example is solved to illustrate the method.
A Very LargeScale Neighborhood Search Algorithm for the MultiResource Generalized Assignment Problem
, 2004
"... this paper, we considered the multiresource generalized assignment problem and proposed a tabu search algorithm in which a sophisticated neighborhood called the chained shift neighborhood is used. It was confirmed through computational comparisons on benchmark instances that the method is e#ective, ..."
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Cited by 3 (2 self)
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this paper, we considered the multiresource generalized assignment problem and proposed a tabu search algorithm in which a sophisticated neighborhood called the chained shift neighborhood is used. It was confirmed through computational comparisons on benchmark instances that the method is e#ective, especially for type D and E instances, which are known to be very di#cult
A Stochastic Programming Approach to ResourceConstrained Assignment Problems
, 2004
"... We address the resourceconstrained generalizations of the assignment problem with uncertain resource capacities, where the resource capacities have an unknown distribution that can be sampled. We propose three stochastic programmingbased formulations that can be used to solve this problem, and pro ..."
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We address the resourceconstrained generalizations of the assignment problem with uncertain resource capacities, where the resource capacities have an unknown distribution that can be sampled. We propose three stochastic programmingbased formulations that can be used to solve this problem, and provide exact and approximate solution techniques for the resulting models. We also present numerical results for a large set of numerical problems. The results indicate that the solutions obtained using the stochastic programming approaches perform significantly better than those obtained using expected values of capacities in a deterministic solution strategy. In addition, stochasticprogrammingbased approximations are computationally as efficient as deterministic techniques. 1
The Uncapacitated Facility Location Problem: Some Applications in Scheduling and Routing
, 2006
"... AbstractThe uncapacitated facility location problem (UFLP) represents a particular structure in integer linear program, and has widespread applications in real life. In this paper, the applicability of UFLPmodel is explored in problems arising in nonlocational context. Three seemingly unrelated pr ..."
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AbstractThe uncapacitated facility location problem (UFLP) represents a particular structure in integer linear program, and has widespread applications in real life. In this paper, the applicability of UFLPmodel is explored in problems arising in nonlocational context. Three seemingly unrelated problems from the area of scheduling and routing are chosen for the purpose and the reported works in which their relationship with the UFLP has been studied are reviewed. These problems are found to have structures similar to a UFLP, and based on this, computationally competitive solution procedures could be developed for them. The study shows that several important problems, quite diverse in application, share common structures with the UFLP, and identification of this commonality can be beneficial from both modeling as well as algorithmic development points of view.
The Generalized Assignment Problem and Its Generalizations
"... The generalized assignment problem is a classical combinatorial optimization problem that models a variety of real world applications including flexible manufacturing systems [6], facility location [11] and vehicle routing problems [2]. Given n jobs J = {1, 2,..., n} and m agents I = {1, 2,..., m}, ..."
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The generalized assignment problem is a classical combinatorial optimization problem that models a variety of real world applications including flexible manufacturing systems [6], facility location [11] and vehicle routing problems [2]. Given n jobs J = {1, 2,..., n} and m agents I = {1, 2,..., m}, the goal is to determine a minimum cost assignment subject to assigning each job to exactly one agent and satisfying a resource constraint for each agent. Assigning job j to agent i incurs a cost of cij and consumes an amount aij of resource, whereas the total amount of the resource available at agent i is bi. An assignment is a mapping σ: J → I, where σ(j) = i means that job j is assigned to agent i. Then the generalized assignment problem (GAP) is formulated as follows: minimize cost(σ) = ∑ subject to j∈J σ(j)=i j∈J c σ(j), j