A matheuristic approach for an integrated lot-sizing and scheduling problem with a period-based learning effect
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F25%3A00379539" target="_blank" >RIV/68407700:21730/25:00379539 - isvavai.cz</a>
Výsledek na webu
<a href="https://doi.org/10.1016/j.eswa.2024.126234" target="_blank" >https://doi.org/10.1016/j.eswa.2024.126234</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.eswa.2024.126234" target="_blank" >10.1016/j.eswa.2024.126234</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
A matheuristic approach for an integrated lot-sizing and scheduling problem with a period-based learning effect
Popis výsledku v původním jazyce
This research investigates a multi-product capacitated lot-sizing and scheduling problem incorporating a novel learning effect, namely the period-based learning effect. This is inspired by a real case in a core analysis laboratory under a job shop setting. Accordingly, a Mixed-Integer Linear Programming (MILP) model is extended based on the big-bucket formulation, optimizing the total tardiness and overtime costs. Given the complexity of the problem, a cutting plane method is employed to simplify the model. Afterward, three matheuristic methods based on the rolling horizon approach are devised, incorporating two lower bounds and a local search heuristic. Furthermore, a post-processing approach is implemented to incorporate lot-streaming possibility. Computational experiments demonstrate: 1) the simplified model performs effectively in terms of both solution quality and computational time; and 2) although the model encounters challenges with large-scale instances, the proposed matheuristic methods achieve satisfactory outcomes; and 3) it can be inferred that the complexity of the models and solution methods are independent of the learning effect; however, the value of learning effect may impact the performance of the lower bounds; 4) in manufacturing settings, where the lot-streaming is possible, incorporating post-processing can drastically improve the objective function; 5) the impact of the period-based learning effect in the results is significant, and the model’s sensitivity to time-based parameters (e.g., learning rate) is more than cost-based ones (e.g., tardiness cost).
Název v anglickém jazyce
A matheuristic approach for an integrated lot-sizing and scheduling problem with a period-based learning effect
Popis výsledku anglicky
This research investigates a multi-product capacitated lot-sizing and scheduling problem incorporating a novel learning effect, namely the period-based learning effect. This is inspired by a real case in a core analysis laboratory under a job shop setting. Accordingly, a Mixed-Integer Linear Programming (MILP) model is extended based on the big-bucket formulation, optimizing the total tardiness and overtime costs. Given the complexity of the problem, a cutting plane method is employed to simplify the model. Afterward, three matheuristic methods based on the rolling horizon approach are devised, incorporating two lower bounds and a local search heuristic. Furthermore, a post-processing approach is implemented to incorporate lot-streaming possibility. Computational experiments demonstrate: 1) the simplified model performs effectively in terms of both solution quality and computational time; and 2) although the model encounters challenges with large-scale instances, the proposed matheuristic methods achieve satisfactory outcomes; and 3) it can be inferred that the complexity of the models and solution methods are independent of the learning effect; however, the value of learning effect may impact the performance of the lower bounds; 4) in manufacturing settings, where the lot-streaming is possible, incorporating post-processing can drastically improve the objective function; 5) the impact of the period-based learning effect in the results is significant, and the model’s sensitivity to time-based parameters (e.g., learning rate) is more than cost-based ones (e.g., tardiness cost).
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2025
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Expert Systems with Applications
ISSN
0957-4174
e-ISSN
1873-6793
Svazek periodika
269
Číslo periodika v rámci svazku
4
Stát vydavatele periodika
NL - Nizozemsko
Počet stran výsledku
13
Strana od-do
"-"-"-"
Kód UT WoS článku
001414274200001
EID výsledku v databázi Scopus
2-s2.0-85214422889