A GENETIC ALGORITHM-BASED APPROACH TO FLEXIBLE FLOW-LINE SCHEDULING WITH VARIABLE LOT SIZES

Genetic Algorithms (GA's) have been used widely for such combinatorial optimization problems as the Traveling Salesman Problem (TSP), the Qu adratic Assignment Problem (QAP), and job shop scheduling, In all of t hese 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-concurrent ly using GA's. The essence of our approach lies in the concept of usin g a unified representation for the information about both the lot size s and the sequence and enabling GA's to evolve the chromosome by repla cing primitive genes with good building blocks, In addition, a simulat ed annealing procedure is incorporated to further improve the performa nce. We evaluate the performance of applying the above approach to fle xible how-line scheduling with variable lot sizes for an actual manufa cturing facility, comparing it to such alternative approaches as pair- wise exchange improvement, tabu search, and simulated annealing proced ures, The results show the efficacy of this approach for flexible flow -line scheduling.