A Set partitioning heuristic for the generalized assignment problem
Author: Van Wassenhove, Luk N. ; Cattrysse, Dirk ; Salomon, MarcINSEAD Area: Technology and Operations Management Series: Working Paper ; 91/40/TM Publisher: Fontainebleau : INSEAD, 1991.Language: EnglishDescription: 10 p.Type of document: INSEAD Working Paper Online Access: Click here Abstract: 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| Item type | Current location | Collection | Call number | Status | Date due | Barcode | Item holds |
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Digital Library | Available | BC000903 |
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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