Permutation Flow Shop Scheduling with Batch Delivery to Multiple Customers
Permutation Flow Shop Scheduling with Batch Delivery to Multiple Customers
复制标题
排列流水车间调度，批量交付给供应链中的多个客户
DOI：
10.1109/tsmc.2017.2720178
复制
发表时间：
2018
影响因子：
8.7
通讯作者：
Yue Xiaohang
中科院分区：
文献类型：
--
作者：
Wang Kai;Luo Hao;Liu Feng;Yue Xiaohang
生产环境的快速变化促使研究人员和工业制造商在供应链管理中协调生产和分销。本文研究了面向多客户批量交货的置换流车间调度问题。在这个问题中，产品首先在排列流车间生产，然后分批交付给多个客户。为了优化客户服务和配送成本之间的权衡，本文的目标是最小化延迟和批量交付的总成本。为了解决这类优化问题，提出了两种简单的启发式算法和一种新的元启发式算法（GA-TVNS）来确定综合生产和分销计划。GA-TVNS混合了遗传算法和可变邻域搜索（VNS），提供了更好的搜索空间探索和利用。此外，为了提高VNS的局部搜索能力，在经典的基于教-学优化的学校学习过程的基础上，设计了两种新的基于学习的邻域结构。小型和大型测试问题的计算实验表明，GA-TVNS在所有比较调度算法中性能最好。
Rapid changes in production environments have motivated researchers and industrial manufacturers to coordinate the production and distribution in supply chain management. This paper aims to address the permutation flow shop scheduling problem with batch delivery to multiple customers. In this problem, products are first manufactured in a permutation flow shop, and subsequently delivered to multiple customers in batches. To optimize the tradeoff between customer service and distribution cost, the objective of this paper is to minimize the total cost of tardiness and batch delivery. To deal with such optimization problem, two simple heuristics and a novel meta-heuristic (GA-TVNS) are developed to determine integrated production and distribution schedules. GA-TVNS hybridizes genetic algorithm and variable neighborhood search (VNS) to provide better exploration and exploitation in the search space. Moreover, to improve the local search of VNS, two new learning-based neighborhood structures are designed based on the classical school learning process of teaching–learning-based optimization. Computation experiments on both small-sized and large-sized test problems indicate that GA-TVNS performs the best among all the compared scheduling algorithms.