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王恺等:流程工业中的两类典型问题—置换流水车间和装配流水车间生产与运输协调调度优化

  作为我国国民经济发展的重要支柱产业,流程工业的产值约占全国工业总产值的66%。流程工业生产与运输协调调度由于产品种类多样且交期严格等特点已成为制造业和学术界公认的科学难题,常规的优化调度方法难以有效求解。近年来王恺及课题组从调度理论、优化方法、关键技术和工程应用等四个视角,对流程工业中具有普适性的关键生产与运输协调调度问题进行了研究。置换流水车间(Permutation Flow Shop)和装配流水车间(Assembly Flow Shop)是我国流程工业的两类典型生产环境,广泛存在于汽车、船舶、装备制造、电子信息产品等制造企业。置换流水车间具有多工序串行加工的特点,而装配流水车间支持并行加工、统一装配。针对上述两类典型生产环境下的生产与运输协调调度问题,下面两篇论文紧密结合相关问题的复杂性、优化模型的特殊性、解的结构特征,分别提出了能够在短时间内求出高精度近似解的智能优化算法。研究成果可以帮助我国流程工业企业提升综合竞争能力和市场适应能力。

  Title:Permutation Flow Shop Scheduling With Batch Delivery to Multiple Customers in Supply Chains

  Authors:王恺; HaoLuo ; Feng Liu ; XiaohangYue

  AbstractRapid 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.

  Keywordsbatch delivery, genetic algorithm (GA), permutation flow shop, supply chain, teaching–learning-based optimization (TLBO), variable neighborhood search (VNS)

  该文20177月在线发表于IEEE Transactions on Systems, Man, and Cybernetics: Systems期刊创刊于1960年,为学院B类奖励期刊,2016-20175年影响因子为2.81

  Title: Coordinated scheduling of production and transportation in a two-stage assembly flowshop

  Authors:王恺;马文琼(硕士生); HaoLuo ; HuQin

  AbstractTo 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.

  Keywordsscheduling; assembly flowshop; genetic algorithms; variable neighbourhood search; opposition-based learning

  该文201611月发表于International Journal of Production Research, 54(22), 6891-6911。该期刊创刊于1961年,为学院B类奖励期刊2016-20175年影响因子为2.388

发布时间:2017-11-03 浏览人数:
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