SCHEDULING FOR A SINGLE SEMICONDUCTOR BATCH-PROCESSING MACHINE TO MINIMIZE TOTAL WEIGHTED TARDINESS

2008 ◽  
Vol 25 (2) ◽  
pp. 136-147 ◽  
Author(s):  
Fuh-Der Chou ◽  
Hui-Mei Wang
2011 ◽  
Vol 110-116 ◽  
pp. 3906-3913 ◽  
Author(s):  
Fuh Der Chou ◽  
Hui Mei Wang

This paper extends the study of Mathirajan et al. (Minimizing total weighted tardiness on a batch-processing machine with non-agreeable release times and due dates. Int. J. Adv. Manuf. Technol., 2010, doi: 10.1007/s00170-009-2342-y) to parallel batch-processing machine problems because these have not been examined to date. For the problem concerning compatible product families, job release times, non-identical job sizes, and varying machine capacities, we propose a mixed integer programming (MIP) model, and a number of simple dispatch-based heuristic and simulated annealing (SA) algorithms. Computational results revealed that the proposed SA is capable of obtaining similar solutions acquired by MIP within a short time. The SA algorithms outperform other heuristic algorithms with respect to solution quality.


Author(s):  
Sadegh Niroomand ◽  
Ali Mahmoodirad ◽  
Saber Molla-Alizadeh-Zavardehi

This paper focuses on a problem of minimizing total weighted tardiness of jobs in a real-world single batch-processing machine (SBPM) scheduling in existence of fuzzy due date. In this paper, first a fuzzy mixed integer linear programming model is developed. Then, due to the complexity of the problem, which is NP-hard, we design two hybrid metaheuristics called GA-VNS and VNS-SA applying the advantages of genetic algorithm (GA), variable neighborhood search (VNS) and simulated annealing (SA) frameworks. Besides, we propose three fuzzy earliest due date heuristics to solve the given problem. Through computational experiments with several random test problems, a robust calibration is applied on the parameters. Finally, computational results on different-scale test problems are presented to compare the proposed algorithms.


2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
S. Molla-Alizadeh-Zavardehi ◽  
R. Tavakkoli-Moghaddam ◽  
F. Hosseinzadeh Lotfi

This paper deals with a problem of minimizing total weighted tardiness of jobs in a real-world single batch-processing machine (SBPM) scheduling in the presence of fuzzy due date. In this paper, first a fuzzy mixed integer linear programming model is developed. Then, due to the complexity of the problem, which is NP-hard, we design two hybrid metaheuristics called GA-VNS and VNS-SA applying the advantages of genetic algorithm (GA), variable neighborhood search (VNS), and simulated annealing (SA) frameworks. Besides, we propose three fuzzy earliest due date heuristics to solve the given problem. Through computational experiments with several random test problems, a robust calibration is applied on the parameters. Finally, computational results on different-scale test problems are presented to compare the proposed algorithms.


Sign in / Sign up

Export Citation Format

Share Document