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中国管理科学 ›› 2020, Vol. 28 ›› Issue (3): 162-173.doi: 10.16381/j.cnki.issn1003-207x.2020.03.017

• 论文 • 上一篇    下一篇

非平稳需求下考虑碳配额的供应链选址-库存模型与算法研究

吴江, 王旻轲, 谭涛, 张培文   

  1. 西南财经大学统计学院, 四川 成都 611130
  • 收稿日期:2018-10-23 修回日期:2019-03-13 出版日期:2020-03-20 发布日期:2020-04-08
  • 通讯作者: 吴江(1971-),男(汉族),四川仪陇人,西南财经大学统计学院管理科学与工程系,教授,博士生导师,研究方向:决策分析及企业管理,E-mail:99162264@qq.com. E-mail:99162264@qq.com
  • 基金资助:
    中央高校基本科研业务费专项资金资助项目(JBK2002011)

Modeling and Solving the Location-Inventory Problem with Nonstationary Demand Considering Carbon Cap-and-trade

WU Jiang, WANG Min-ke, TAN Tao, Zhang Pei-wen   

  1. School of Statistics, Southwestern University of Finance and Economics, Chengdu 611130, China
  • Received:2018-10-23 Revised:2019-03-13 Online:2020-03-20 Published:2020-04-08

摘要: 针对非平稳需求下考虑碳配额的多期、多需求情景的三级供应链选址-库存问题,构建了库存策略(tsS)下供应链运营期望收益最大化的两阶段选址-库存随机优化模型,依据供应链企业不同着眼点下的决策流程,提出了一种三步骤的分层级启发式算法,该算法包含了选址导向和需求导向的两种子问题序贯求解模式。数值算例验证了在不同问题规模及需求类型下算法求解的有效性,同时分析了供应链网络设计、各成本占比和运营收益对不同供应链成本结构、需求不确定性与碳配额的敏感性,并给出了管理上的启示。

关键词: 选址-库存问题, 碳配额, 两阶段随机优化模型, 三步骤分层级启发式算法

Abstract: Recently supply chain industry experiences a significant energy consumption growth drawing the attention of government, academia, and industry to its environmental impacts and the issue of low-carbon supply chain management. Supply chain businesses not only face the competitive market with uncertain demand but also realize the low-carbon competitiveness under the carbon emission compliance scenario. Thus, a green-focusedtwo-stage stochastic location-inventory model is required to integrate the inventory control decisions with the 3-stage network (i.e., supplier-DC-retailer) design decisions to deal with nonstationary demand, whose objective maximize the profits of the supply chain business including the sales profit and the low-carbon reward (i.e., trade extra carbon allowances).To be specific, given the allocated carbon-capΦcap, the carbon emissions associated with DC implementation (CEL), DC operations (CEO), and transportation (CET) are considered, then the carbon-cap difference is calculated as CEL+ CEO+ CETcap. Thenegative difference reflects the low-carbon emitter can sell extra allowances to obtain incomes; otherwise, buy emission allowances to comply with the carbon-cap. To make inventory control decisions under the (t,s,S)policy, we explicit the formulations of optimal parameters based on the Newsboy adjustment method and linearization technique. Because the problem is of the NP-hard, a three-step hierarchical matheuristic algorithm is proposed, which features different initial solution construction modes, simulated annealing (SA) and intensification after SA. The case study from a supply chain company in China facilitates the verification of model validation and algorithm effectiveness. After investigate the impact of cost structures, demand uncertainty, and different carbon-caps on supply chain network design, costs, and profits of the supply chain business, managerial insights include: (1) integrated decision-making at strategic and tactical levels in supply chain management is necessary, separating them can only reach to sub-optimal results thus higher costs. (2) When facing nonstationary demand, supply chain businesses open more DCs to enhance the level of service; while the back-ordering cost is high, supply chain businesses alsoopen more DCs to reduce the probability of out-of-stocks. (3) The emission trading system encourages enterprises to achieve low-carbon through better operations to obtain the low-carbon reward, which is the most flexible and beneficial emission regulation; however, the most suitable caps and carbon prices depend on the environmental targets and the economic benefits. As the climate change is at issue around the globe nowadays, this model and algorithm provide solutions to supply chain industry’s operations and emission compliance also cast light on future carbon emission regulation schemes to be implemented in China.

Key words: location-inventory problem, carbon emission allowance, two-stage stochastic programming, three-step hierarchical matheuristic

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