Abstract
| Original language | English |
|---|---|
| Title of host publication | WSC 2019- 2019 Winter Simulation Conference |
| Editors | N. Mustafee, K.-H.G. Bae, S. Lazarova-Molnar, M. Rabe, C. Szabo, P. Haas, Y.-J. Son |
| Publication status | Accepted/In press - 2019 |
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A hybrid genetic algorithm for the k-bounded semi-online bin covering problem in batching machines. / Hundscheid, B.H.H.; Peeters, Kay; Adan, Jelle; Martagan, Tugce G.; Adan, Ivo J.B.F.
WSC 2019- 2019 Winter Simulation Conference. ed. / N. Mustafee; K.-H.G. Bae; S. Lazarova-Molnar; M. Rabe; C. Szabo; P. Haas; Y.-J. Son. 2019.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - A hybrid genetic algorithm for the k-bounded semi-online bin covering problem in batching machines
AU - Hundscheid, B.H.H.
AU - Peeters, Kay
AU - Adan, Jelle
AU - Martagan, Tugce G.
AU - Adan, Ivo J.B.F.
PY - 2019
Y1 - 2019
N2 - The semi-online bin covering problem is a NP-hard problem that occurs in a batching processes in a high-end poultry processing line. The objective is to form batches of items with minimal giveaway, which is the difference between the target and realized batch weight. The items in this process are allocated in the order of arrival, and the weight of the first set of items is assumed to be known. We develop a novel hybrid genetic algorithm, combining a genetic algorithm and several local search methods. Simulation experiments based on real-world data are performed to gain managerial insights. These simulations suggest that the proposed algorithm produces high quality solutions within a reasonable time limit.
AB - The semi-online bin covering problem is a NP-hard problem that occurs in a batching processes in a high-end poultry processing line. The objective is to form batches of items with minimal giveaway, which is the difference between the target and realized batch weight. The items in this process are allocated in the order of arrival, and the weight of the first set of items is assumed to be known. We develop a novel hybrid genetic algorithm, combining a genetic algorithm and several local search methods. Simulation experiments based on real-world data are performed to gain managerial insights. These simulations suggest that the proposed algorithm produces high quality solutions within a reasonable time limit.
M3 - Conference contribution
BT - WSC 2019- 2019 Winter Simulation Conference
A2 - Mustafee, N.
A2 - Bae, K.-H.G.
A2 - Lazarova-Molnar, S.
A2 - Rabe, M.
A2 - Szabo, C.
A2 - Haas, P.
A2 - Son, Y.-J.
ER -