Coordinated scheduling of production and transportation in a two-stage assembly flowshop
Coordinated scheduling of production and transportation in a two-stage assembly flowshop
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DOI：
10.1080/00207543.2016.1193246
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发表时间：
2016-06
影响因子：
9.2
通讯作者：
K. Wang;W. Ma;H. Luo;H. Qin
中科院分区：
文献类型：
--
作者：
K. Wang;W. Ma;H. Luo;H. Qin
为了提高供应链的整体绩效，生产和分销阶段之间的协调最近受到越来越多的关注。研究了两阶段装配流水车间环境下的生产与运输协调调度问题。在这个问题中，产品部件首先在两阶段装配流水线中生产和装配，然后将完成的最终产品批量交付给客户。考虑到该调度问题的NP-难性质，提出了两种快速启发式算法（基于SPT的启发式算法和基于LPT的启发式算法）和一种新的混合元启发式算法（HGA-OVNS），以最小化到达客户的平均时间和总交货成本的加权和。为了将搜索过程引导到更有希望的区域，所提出的HGA-OVNS将遗传算法与可变邻域搜索（VNS）相结合来生成后代个体。此外，为了提高VNS的有效性，基于对立的学习（OBL）被应用于建立一些新的相反的邻域结构。在一组随机生成的算例上对所提出的算法进行了验证，计算结果表明了HGA-OVNS在解的质量上的优越性。
To enhance the overall performance of supply chains, coordination among production and distribution stages has recently received an increasing interest. This paper considers the coordinated scheduling of production and transportation in a two-stage assembly flowshop environment. In this problem, product components are first produced and assembled in a two-stage assembly flowshop, and then completed final products are delivered to a customer in batches. Considering the NP-hard nature of this scheduling problem, two fast heuristics (SPT-based heuristic and LPT-based heuristic) and a new hybrid meta-heuristic (HGA-OVNS) are presented to minimise the weighted sum of average arrival time at the customer and total delivery cost. To guide the search process to more promising areas, the proposed HGA-OVNS integrates genetic algorithm with variable neighbourhood search (VNS) to generate the offspring individuals. Furthermore, to enhance the effectiveness of VNS, the opposition-based learning (OBL) is applied to establish some novel opposite neighbourhood structures. The proposed algorithms are validated on a set of randomly generated instances, and the computation results indicate the superiority of HGA-OVNS in quality of solutions.