Minimizing total weighted tardiness on a batch-processing machine with incompatible job families and job ready times

2007 ◽  
Vol 46 (1) ◽  
pp. 131-151 ◽  
Author(s):  
Mary E. Kurz ◽  
Scott J. Mason
Keyword(s):  
Batch Processing ◽  
Total Weighted Tardiness ◽  
Weighted Tardiness ◽  
Processing Machine ◽  
Batch Processing Machine

Minimizing Total Weighted Tardiness on Parallel Batch-Processing Machine Scheduling Problems with Varying Machine Capacities

2011 ◽  
Vol 110-116 ◽  
pp. 3906-3913 ◽  
Author(s):  
Fuh Der Chou ◽  
Hui Mei Wang
Keyword(s):  
Heuristic Algorithms ◽  
Batch Processing ◽  
Total Weighted Tardiness ◽  
Mixed Integer ◽  
Due Dates ◽  
Weighted Tardiness ◽  
Processing Machine ◽  
Release Times ◽  
Mip Model ◽  
Batch Processing Machine

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.

Single Batch-Processing Machine Scheduling Problem with Fuzzy Due-Dates

2016 ◽  
pp. 751-769 ◽  
Author(s):  
Sadegh Niroomand ◽  
Ali Mahmoodirad ◽  
Saber Molla-Alizadeh-Zavardehi
Keyword(s):  
Programming Model ◽  
Batch Processing ◽  
Total Weighted Tardiness ◽  
Mixed Integer ◽  
Test Problems ◽  
Due Date ◽  
Processing Machine ◽  
Batch Processing Machine ◽  
Scale Test ◽  
Single Batch

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.

scholarly journals Hybrid Metaheuristics for Solving a Fuzzy Single Batch-Processing Machine Scheduling Problem

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
S. Molla-Alizadeh-Zavardehi ◽  
R. Tavakkoli-Moghaddam ◽  
F. Hosseinzadeh Lotfi
Keyword(s):  
Batch Processing ◽  
Total Weighted Tardiness ◽  
Mixed Integer ◽  
Neighborhood Search ◽  
Test Problems ◽  
Hybrid Metaheuristics ◽  
Due Date ◽  
Processing Machine ◽  
Batch Processing Machine ◽  
Single Batch

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.

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