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.

为了提高供应链的运营效率和竞争优势，集成生产和分销近年来受到越来越多的关注。本文着重于考虑工厂资格和第三方物流（3PL）的新型集成生产和分销调度问题（IPDSP）。在此问题中，产品首先在多个分布式混合流水车间（HFS）中生产，然后分批交付给客户。为了满足生产和分销实践，某些产品只能在分布式HFS的子集中制造，而某些成品的运输则外包给3PL提供者。考虑到IPDSP的NP硬度，三种快速启发式算法（基于CR的启发式算法，基于SLACK的启发式算法，以及基于EDD的启发式算法）和自适应的基于人类学习的遗传算法（AHLBGA）被提出，以最大程度地减少早期，拖延和交付成本之和。受人类学习行为的激励，AHLBGA将自适应学习算子与传统遗传算子集成在一起以生成候选解。这样的学习者进行社交学习，家庭学习和个体随机学习以改善后代个体。对小型和大型测试问题的计算实验表明了AHLBGA的优越性。这样的学习者进行社交学习，家庭学习和个体随机学习以改善后代个体。对小型和大型测试问题的计算实验表明了AHLBGA的优越性。这样的学习者进行社交学习，家庭学习和个体随机学习以改善后代个体。对小型和大型测试问题的计算实验表明了AHLBGA的优越性。