Solving multi-objective master production schedule problem using memetic algorithm
A master production schedule (MPS) need find a good, perhaps optimal, plan for maximize service levels while minimizing inventory and resource usage. However, these are conflicting objectives and a tradeoff to reach acceptable values must be made. Therefore, several techniques have been proposed to...
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2020-05-01.
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| LEADER | 02666 am a22003133u 4500 | ||
|---|---|---|---|
| 001 | ijeecs20518_13693 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Sadiq, Shireen S. |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Abdulazeez, Adnan Mohsin |e author |
| 700 | 1 | 0 | |a Haron, Habibollah |e author |
| 245 | 0 | 0 | |a Solving multi-objective master production schedule problem using memetic algorithm |
| 260 | |b Institute of Advanced Engineering and Science, |c 2020-05-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/20518 | ||
| 520 | |a A master production schedule (MPS) need find a good, perhaps optimal, plan for maximize service levels while minimizing inventory and resource usage. However, these are conflicting objectives and a tradeoff to reach acceptable values must be made. Therefore, several techniques have been proposed to perform optimization on production planning problems based on, for instance, linear and non-linear programming, dynamic-lot sizing and meta-heuristics. In particular, several meta- heuristics have been successfully used to solve MPS problems such as genetic algorithms (GA) and simulated annealing (SA). This paper proposes a memetic algorithm to solve multi-objective master production schedule (MOMPS). The proposed memetic algorithm combines the evolutionary operations of MA (such as mutation and Crossover) with local search operators (swap operator and inverse movement operator) to improve the solutions of MA and increase the diversity of the population). This algorithm has proved its efficiency in solving MOMPS problems compared with the genetic algorithm and simulated annealing. The results clearly showed the ability of the algorithm to evaluate properly how much, when and where extra capacities (overtime) are permitted so that the inventory can be lowered without influencing the level of service. | ||
| 540 | |a Copyright (c) 2019 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Genetic algorithm; Master production schedule; Memetic algorithm; Multi-objective optimization; Simulated annealing | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 18, No 2: May 2020; 938-945 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v18.i2 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/20518/13693 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/20518/13693 |z Get fulltext |