Joint scheduling of hybrid flow-shop with limited automatic guided vehicles: A hierarchical learning-based swarm optimizer

Abstract

Transportation system in workshop is essential for high-efficient production scheduling. Due to the limited transportation resources, the joint scheduling of production and transportation has emerged as a pivotal issue in modern manufacturing. This paper investigates a joint scheduling of hybrid flow-shop with limited automatic guided vehicles (HFSP-LAGV), which extends the classical hybrid flow-shop scheduling by considering the limited number of the AGVs on the transportation resources. To solve such problem, a mixed integer linear programming (MILP) model is firstly built to formulate HFSP-LAGV. Then, a hierarchical learning-based swarm optimizer (HLSO) is proposed. An encoding and decoding method based on three dispatch rules is proposed. The framework of HLSO comprises a pyramid-based layering strategy, an inter-layer learning and an intra-layer learning. The pyramid-based layering strategy divides the swarm into several layers. In the inter-layer learning, the individuals in higher layers guide the evolution of individuals in lower layers to achieve the exploration of global area. In the intra-layer learning, an offline Q-learning-based local search is designed to implement the self-learning of elite individuals in higher layer to intensify the exploitation of the local area. A Q-learning model that has been pre-trained offline is used to guide the selection of appropriate operator of local search. Experimental results reveal the effectiveness of the designs and the superiority of HLSO over several well-performing methods on solving HFSP-LAGV.