Integrated production and distribution scheduling in distributed hybrid flow shops

• Published in: Memetic computing Vol. 13; no. 2; pp. 185 - 202
• Main Authors: Qin, Hu, Li, Tao, Teng, Yi, Wang, Kai
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2021; Springer Nature B.V
• Subjects: Adaptive algorithms; Adaptive learning; Applications of Mathematics; Artificial Intelligence; Bioinformatics; Complex Systems; Control; Customer satisfaction; Engineering; Genetic algorithms; Heuristic; Logistics management; Machine learning; Mathematical and Computational Engineering; Mechatronics; Production scheduling; Regular Research Paper; Robotics; Supply chains; Scheduling; Factory eligibility; Production and distribution; Distributed hybrid flow shop; Third-party logistics; Genetic algorithm
• ISSN: 1865-9284; 1865-9292
• DOI: 10.1007/s12293-021-00329-6

To improve the operational efficiency and competitive advantage of supply chains, integrated production and distribution has attracted an increasing attention in recent years. This paper focuses on a novel integrated production and distribution scheduling problem (IPDSP) with consideration of factory eligibility and third-party logistics (3PL). In this problem, products are firstly produced in a number of distributed hybrid flow shops (HFS) and then delivered to a customer in batches. To satisfy the production and distribution practice, some products can only be manufactured in a subset of distributed HFSs, and the transportation of some finished products is outsourced to a 3PL provider. Considering the NP-hardness of IPDSP, three fast heuristics (CR-based heuristic, SLACK-based heuristic, and EDD-based heuristic) and an adaptive human-learning-based genetic algorithm (AHLBGA) are proposed to minimize the sum of earliness, tardiness and delivery costs. Motivated by human learning behaviours, AHLBGA integrates an adaptive learning operator with traditional genetic operators to generate candidate solutions. Such learning operator performs social learning, family learning, and individual random learning to improve offspring individuals. The computational experiments on small-sized and large-sized test problems show the superiority of AHLBGA.