Abstract
Lot streaming is a technique used to split the processing of lots into several sublots (transfer batches) to allow the overlapping of operations in a multistage manufacturing systems thereby shortening the production time (makespan). The objective of this paper is to minimize the makespan and total flow time of n-job, m-machine lot-streaming problem in a flow shop with variable size sublots and also to determine the optimal sublot size. In recent times researchers are concentrating and applying intelligent heuristics to solve Flow shop problems with lot streaming. In this research, Improved Sheep Flock Heredity algorithm (ISFHA) and Artificial Bee Colony (ABC) algorithms are used to solve the problem. The results obtained by the proposed algorithms are also compared with the performance of other worked out traditional heuristics. The computational results show that the identified algorithms are more efficient, effective and better than the algorithms already tested for this problem.
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