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

• Published in: Computing Vol. 101; no. 6; pp. 499 - 529
• Main Authors: Goli, Alireza, Tirkolaee, Erfan Babaee, Malmir, Behnam, Bian, Gui-Bin, Sangaiah, Arun Kumar
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 01.06.2019; Springer Nature B.V
• Subjects: 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

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...