Migrating Birds Optimization for Lot-streaming flow shop scheduling problem

Abstract

This paper presents a novel migrating birds optimization (NMBO) algorithm for solving the lot-streaming flowshop scheduling problem with minimizing makespan. The proposed NMBO algorithm utilizes discrete job permutations to represent solutions, and applies multiple neighborhoods based on insert and swap operators to improve the leading solution. Two new crossover operators, i.e., similar job order with artificial chromosome crossover, and similar block order crossover are employed to obtain solutions for the rest migrating birds. An initialization scheme based on the problem-specific heuristics is presented to generate an initial population with a certain level of quality and diversity. A local search based on the insert neighborhood is embedded to improve the algorithm’s local exploitation ability. NMBO is compared with the existing discrete invasive weed optimization, estimation of distribution algorithm and modified MBO algorithms based on the well-known lot-streaming flow shop benchmark. The computational results and comparison demonstrate the superiority of the proposed NMBO algorithm for the lot-streaming flow shop scheduling problems with makespan criterion.