A multi-objective invasive weed optimization algorithm for robust aggregate production planning under uncertain seasonal demand

This paper addresses a robust multi-objective multi-period aggregate production planning (APP) problem based on different scenarios under uncertain seasonal demand. The main goals are to minimize the total cost including in-house production, outsourcing, workforce, holding, shortage and employment/u...

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Veröffentlicht in:Computing Jg. 101; H. 6; S. 499 - 529
Hauptverfasser: Goli, Alireza, Tirkolaee, Erfan Babaee, Malmir, Behnam, Bian, Gui-Bin, Sangaiah, Arun Kumar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Vienna Springer Vienna 01.06.2019
Springer Nature B.V
Schlagworte:
Artificial Intelligence
Classification
Computer Appl. in Administrative Data Processing
Computer Communication Networks
Computer Science
Demand
Employment
Genetic algorithms
Goal programming
Information Systems Applications (incl.Internet)
Integer programming
Linear programming
Mathematical models
Mixed integer
Multiple objective analysis
Optimization
Optimization algorithms
Production planning
Production scheduling
Robustness (mathematics)
Software Engineering
Sorting algorithms
Taguchi methods
NSGA-II
68Txx
90B30
Robust optimization
Aggregate production planning
90C59
Uncertain seasonal demand
Multi-objective invasive weed optimization algorithm (MOIWO)
90B50
ISSN:0010-485X, 1436-5057
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Zusammenfassung:This paper addresses a robust multi-objective multi-period aggregate production planning (APP) problem based on different scenarios under uncertain seasonal demand. The main goals are to minimize the total cost including in-house production, outsourcing, workforce, holding, shortage and employment/unemployment costs, and maximize the customers’ satisfaction level. To deal with demand uncertainty, robust optimization approach is applied to the proposed mixed integer linear programming model. A goal programming method is then implemented to cope with the multi-objectiveness and validate the suggested robust model. Since APP problems are classified as NP-hard, two solution methods of non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective invasive weed optimization algorithm (MOIWO) are designed to solve the problem. Moreover, Taguchi design method is implemented to increase the efficiency of the algorithms by adjusting the algorithms’ parameters optimally. Finally, several numerical test problems are generated in different sizes to evaluate the performance of the algorithms. The results obtained from different comparison criteria demonstrate the high quality of the proposed solution methods in terms of speed and accuracy in finding optimal solutions.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0010-485X
1436-5057
DOI:10.1007/s00607-018-00692-2