TY - JOUR
T1 - A genetic algorithm-based approach to flexible flow-line scheduling with variable lot sizes
AU - Lee, In
AU - Sikora, Riyaz
AU - Shaw, Michael J.
N1 - Funding Information:
Manuscript received August 28, 1994; revised April 26, 1995 and November 12, 1995. This work was supported in part by grants to M. Shaw from the Decision, Risk, and Management Science Program, National Science Foundation (SBR 93-21011) and from the Manufacturing Research Center, University of Illinois.
PY - 1997
Y1 - 1997
N2 - Genetic Algorithms (GA's) have been used widely for such combinatorial optimization problems as the Traveling Salesman Problem (TSP), the Quadratic Assignment Problem (QAP), and job shop scheduling. In all of these problems there is usually a well defined representation which GA's use to solve the problem. In this paper, we present a novel approach for solving two related problems - lot-sizing and sequencing - concurrently using GA's. The essence of our approach lies in the concept of using a unified representation for the information about both the lot sizes and the sequence and enabling GA's to evolve the chromosome by replacing primitive genes with good building blocks. In addition, a simulated annealing procedure is incorporated to further improve the performance. We evaluate the performance of applying the above approach to flexible flow-line scheduling with variable lot sizes for an actual manufacturing facility, comparing it to such alternative approaches as pair-wise exchange improvement, tabu search, and simulated annealing procedures. The results show the efficacy of this approach for flexible flow-line scheduling.
AB - Genetic Algorithms (GA's) have been used widely for such combinatorial optimization problems as the Traveling Salesman Problem (TSP), the Quadratic Assignment Problem (QAP), and job shop scheduling. In all of these problems there is usually a well defined representation which GA's use to solve the problem. In this paper, we present a novel approach for solving two related problems - lot-sizing and sequencing - concurrently using GA's. The essence of our approach lies in the concept of using a unified representation for the information about both the lot sizes and the sequence and enabling GA's to evolve the chromosome by replacing primitive genes with good building blocks. In addition, a simulated annealing procedure is incorporated to further improve the performance. We evaluate the performance of applying the above approach to flexible flow-line scheduling with variable lot sizes for an actual manufacturing facility, comparing it to such alternative approaches as pair-wise exchange improvement, tabu search, and simulated annealing procedures. The results show the efficacy of this approach for flexible flow-line scheduling.
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U2 - 10.1109/3477.552184
DO - 10.1109/3477.552184
M3 - Article
C2 - 18255838
AN - SCOPUS:0031076889
VL - 27
SP - 36
EP - 54
JO - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
SN - 1083-4419
IS - 1
ER -