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• W2050274364 abstract "This work presents Lagrangean/surrogate relaxation to the problem of maximum profit assignment of n tasks to m agents (n > m), such that each task is assigned to only one agent subject to capacity constraints on the agents. The Lagrangean/surrogate relaxation combines usual Lagrangean and surrogate relaxations relaxing first a set of constraints in the surrogate way. Then, the Lagrangean relaxation of the surrogate constraint is obtained and approximately optimized (one-dimensional dual). The Lagrangean/surrogate is compared with the usual Lagrangean relaxation on a computational study using a large set of instances. The dual bounds are the same for both relaxations, but the Lagrangean/surrogate can give improved local bounds at the application of a subgradient method, resulting in less computational times. Three relaxations are derived for the problem. The first relaxation considers a vector of multipliers for the capacity constraints, the second for the assignment constraints and the other for the Lagrangean decomposition constraints. Relaxation multipliers are used with efficient constructive heuristics to find good feasible solutions. The application of a Lagrangean/surrogate approach seems very promising for large scale problems." @default.
• W2050274364 created "2016-06-24" @default.
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• W2050274364 date "1999-04-01" @default.
• W2050274364 modified "2023-09-26" @default.
• W2050274364 title "Lagrangean/surrogate relaxation for generalized assignment problems" @default.
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• W2050274364 doi "https://doi.org/10.1016/s0377-2217(98)00038-1" @default.
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