• Journal of Korean Institute of Industrial Engineers
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• v.27 no.1
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• pp.25-29
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• 2001

We present a two dimensional linear programming knapsack problem with the extended GUB constraint. The presented problem is an extension of the cardinality constrained linear programming knapsack problem. We identify some new properties of the problem and derive a solution algorithm based on the parametric analysis for the knapsack right-hand-side. The solution algorithm has a worst case time complexity of order O($n^2logn$). A numerical example is given.

• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.3
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• pp.95-104
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• 2001

In this paper, we consider a maximin version of the linear programming knapsack problem with extended generalized upper bound (GUB) constraints. We solve the problem efficiently by exploiting its special structure without transforming it into a standard linear programming problem. We present an O(n$^3$) algorithm for deriving the optimal solution where n is the total number of problem variables. We illustrate a numerical example.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.3
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• pp.258-263
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• 2011

We study on a generalization of the two dimensional linear programming knapsack problem with the extended GUB constraint, which was presented in paper Won(2001). We identify some new parametric properties of the generalized problem and derive a solution algorithm based on the identified parametric properties. The solution algorithm has a worst case time complexity of order O($n^2logn$), where n is the total number of variables. We illustrate a numerical example.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.3
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• pp.1-9
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• 1997

We present an extension of the well-known generalized upper bound (GUB) constraint and consider a linear knapsack problem with both the extended GUB constraints and the simple upper bound (SUB) constraints. An efficient algorithm of order O($n^2log n$) is developed by exploiting structural properties and applying binary search to ordered solution sets, where n is the total number of variables. A numerical example is presented.