TY - JOUR
T1 - Application of a modified GA, ACO and a random search procedure to solve the production scheduling of a case study bakery
AU - Hecker, Florian T.
AU - Stanke, Marc
AU - Becker, Thomas
AU - Hitzmann, Bernd
N1 - Funding Information:
This research has been funded by CSM Deutschland GmbH. Mr. Hermann-Josef Michaelis (DialogMarketing GmbH) supported the collection and availability of the production data. The utilized production data were provided by a baking company in Frankfurt/Main, Germany. This research was not further influenced besides the financial and data collection support.
PY - 2014/10/1
Y1 - 2014/10/1
N2 - Based on the constraints and frame conditions given by the real processes the production in bakeries can be modelled as a no-wait permutation flow-shop, following the definitions in scheduling theory. A modified genetic algorithm, ant colony optimization and a random search procedure were used to analyse and optimize the production planning of a bakery production line that processes 40 products on 26 production stages. This setup leads to 8.2 × 10 47 different possible schedules in a permutation flow-shop model and is thus not solvable in reasonable time with exact methods. Two objective functions of economical interest were analysed, the makespan and the total idle time of machines. In combination with the created model, the applied algorithms proved capable to provide optimized results for the scheduling operation within a predefined runtime of 15 min, reducing the makespan by up to 8.6% and the total idle time of machines by up to 23%.
AB - Based on the constraints and frame conditions given by the real processes the production in bakeries can be modelled as a no-wait permutation flow-shop, following the definitions in scheduling theory. A modified genetic algorithm, ant colony optimization and a random search procedure were used to analyse and optimize the production planning of a bakery production line that processes 40 products on 26 production stages. This setup leads to 8.2 × 10 47 different possible schedules in a permutation flow-shop model and is thus not solvable in reasonable time with exact methods. Two objective functions of economical interest were analysed, the makespan and the total idle time of machines. In combination with the created model, the applied algorithms proved capable to provide optimized results for the scheduling operation within a predefined runtime of 15 min, reducing the makespan by up to 8.6% and the total idle time of machines by up to 23%.
KW - Ant colony optimization
KW - Bakery production planning
KW - Evolutionary algorithms
KW - Flow-shop scheduling
KW - Modified Genetic Algorithm
UR - https://www.scopus.com/pages/publications/84899688920
U2 - 10.1016/j.eswa.2014.03.047
DO - 10.1016/j.eswa.2014.03.047
M3 - Article
AN - SCOPUS:84899688920
SN - 0957-4174
VL - 41
SP - 5882
EP - 5891
JO - Expert Systems with Applications
JF - Expert Systems with Applications
IS - 13
ER -