An Improved Fruit Fly Optimization Algorithm for Solving Lot-streaming Flow-shop Scheduling Problem

An improved fruit fly optimization algorithm (iFOA) is proposed in this paper for solving the lot-streaming flow-shop scheduling problem (LSFSP) with equal-size sub-lots. In the proposed iFOA, a solution is represented as two vectors to determine the splitting of jobs and the sequence of the sub-lots simultaneously. Based on the encoding scheme, three kinds of neighborhoods are developed for generating new solutions. To well balance the exploitation and exploration, two main search procedures are designed within the evolutionary search framework of the iFOA, including the neighborhood-based search (smell-vision-based search) and the global cooperation-based search. Finally, numerical testing results are provided, and the comparisons demonstrate the effectiveness of the proposed iFOA for solving the LSFSP.