A Discrete Artificial Immune System Algorithm for the Lot Streaming Flow Shop Scheduling Problem

This paper attempts to present the challenge of scheduling n jobs–m machines in the flow shop scheduling environment. In this scheduling, lot streaming is a method used to divide up multiple sublots to allow functions to intersect across multiple production systems. The purpose of this work is to reduce the time and save the manufacturing cost. In recent times, researchers have used bright heuristics to explain flow shop difficulties on a lot streaming problem. In this work, artificial immune system (AIS) algorithm is used to solve the lot streaming concept in the flow shop scheduling environment with the objective of minimizing the makespan time. In addition, a nature ability to change to suit different conditions for generating the neighborhood antibodies based on the inverse and pairwise mutation. The results obtained by this algorithm are compared with the standard benchmark instances. This proposed algorithm is capable and establishes to be a superior problem-solving technique for this flow shop scheduling with lot streaming.