Lot Sizing and Scheduling Problem in Distributed Heterogeneous Hybrid Flow Shop and Learning-Driven Iterated Local Search Algorithm

Lot planning and production scheduling are two strong coupled sub-problems in the manufacturing process. The lot-streaming technique divides the products into several lots. The production scheduling determines the processing order of products. This paper focuses on the integration of lot sizing and scheduling problem in the distributed heterogeneous hybrid flow shop (DHHFSLSP) which considers determining the quantity and size of lots, factory assignment, machine selection, and order sequence. A learning-driven iterated local search algorithm (LDILS) is proposed for solving the DHHFSLP. Firstly, a framework of learning-driven trajectory-based meta-heuristics is proposed, where a learning engine is integrated to guide the state of searching. Then, an NEH-based constructive heuristic is proposed to generate a promising initial solution. Next, several lot sizing and scheduling searching operators are proposed. Based on these operators, ${Q}$ -learning is regarded as a learning engine to capture the searching state and choose a searching action. Finally, a restart mechanism is used to calibrate searching direction by copying the best solution found so far. A comprehensive experiment based on amounts of testing instances is conducted to investigate the effectiveness of initialization, ${Q}$ -learning framework, and searching operators. Compared to relevant algorithms, LDILS can solve the DHHFSLSP effectively and efficiently. Note to Practitioners—This paper studies a lot sizing and scheduling problem in the distributed heterogeneous hybrid flow shop, which are always faced by planners and production managers. The reasonable lot sizing plans and schedules can increase the efficiency of production. The model of this paper can be used in many real productions when meeting the following conditions, i.e. heterogeneous factories, flow shops, and parallel machines, and the jobs can be split into several sub-lots. This paper proposes a learning-driven iterated local search algorithm, which can obtain high-quality solutions for decision-makers. The experiment results demonstrate its high effectiveness and efficiency.