Modeling and solving the multi-objective energy-efficient production planning and scheduling with imperfect maintenance activities

PurposeThe purpose of this study is to propose a new mathematical model that integrates strategic decision-making with tactical-operational decision-making in order to optimize production and scheduling decisions.Design/methodology/approachThis study presents a multi-objective optimization framework...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of quality in maintenance engineering Jg. 30; H. 1; S. 26 - 50
Hauptverfasser: Rastgar, Iman, Rezaeian, Javad, Mahdavi, Iraj, Fattahi, Parviz
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bradford Emerald Publishing Limited 23.02.2024
Emerald Group Publishing Limited
Schlagworte:
Computing time
Corporate objectives
Cost control
Decision making
Design optimization
Energy consumption
Energy efficiency
Genetic algorithms
Heuristic methods
Integer programming
Linear programming
Maintenance
Management decisions
Manufacturing
Mathematical models
Multiple objective analysis
Pareto optimization
Particle swarm optimization
Preventive maintenance
Production planning
Production scheduling
Scheduling
Sorting algorithms
Integration of production planning and scheduling
Energy consumption
Green scheduling
Hybrid flow-shop
Imperfect maintenance
IMOHS
ISSN:1355-2511, 1758-7832
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:PurposeThe purpose of this study is to propose a new mathematical model that integrates strategic decision-making with tactical-operational decision-making in order to optimize production and scheduling decisions.Design/methodology/approachThis study presents a multi-objective optimization framework to make production planning, scheduling and maintenance decisions. An epsilon-constraint method is used to solve small instances of the model, while new hybrid optimization algorithms, including multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm, multi-objective harmony search and improved multi-objective harmony search (IMOHS) are developed to address the high complexity of large-scale problems.FindingsThe computational results demonstrate that the metaheuristic algorithms are effective in obtaining economic solutions within a reasonable computational time. In particular, the results show that the IMOHS algorithm is able to provide optimal Pareto solutions for the proposed model compared to the other three algorithms.Originality/valueThis study presents a new mathematical model that simultaneously determines green production planning and scheduling decisions by minimizing the sum of the total cost, makespan, lateness and energy consumption criteria. Integrating production and scheduling of a shop floor is critical for achieving optimal operational performance in production planning. To the best of the authors' knowledge, the integration of production planning and maintenance has not been adequately addressed.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1355-2511
1758-7832
DOI:10.1108/JQME-10-2022-0068