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A Set partitioning heuristic for the Generalized Assignment Problem

Author: Van Wassenhove, Luk N. ; Cattrysse, Dirk ; Salomon, MarcINSEAD Area: Technology and Operations ManagementIn: European Journal of Operational Research, vol. 46, no. 1, May 1990 Language: EnglishDescription: p. 38-47.Type of document: INSEAD ArticleNote: Please ask the Library for this articleAbstract: This paper discusses a heuristic for the generalized assignment problem (GAP). The objective of GAP is to minimize the costs of assigning 'J' jobs to 'M' capacity constrained machines, such that each job is assigned to exactly one machine. The problem is known to be NP-hard, and it is hard from a computational point of view as well. The heuristic proposed here is based on column generation techniques, and yields both upper and lower bounds. On a set of relatively hard test problems the heuristic is able to find solutions that are on average within 0.13% from optimally
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This paper discusses a heuristic for the generalized assignment problem (GAP). The objective of GAP is to minimize the costs of assigning 'J' jobs to 'M' capacity constrained machines, such that each job is assigned to exactly one machine. The problem is known to be NP-hard, and it is hard from a computational point of view as well. The heuristic proposed here is based on column generation techniques, and yields both upper and lower bounds. On a set of relatively hard test problems the heuristic is able to find solutions that are on average within 0.13% from optimally

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