Cooperative green supply chain management with greenhouse gas emissions and fuzzy demand
Cooperative green supply chain management with greenhouse gas emissions and fuzzy demand
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DOI：
10.1016/j.jclepro.2018.10.124
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发表时间：
2019-01
影响因子：
11.1
通讯作者：
J. Noh;Jongsoo Kim
中科院分区：
文献类型：
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作者：
J. Noh;Jongsoo Kim
近年来，人们对环境可持续供应链管理的担忧普遍增加。因此，供应链成员相互合作，签订有效的合同，通常被称为绿色供应链管理合同。本文的目的是研究在温室气体排放法规下，单一制造商和资源有限的多个零售商之间签订的几种产品的合同。每个零售商在有限的预算和仓库容量内定期订购产品。作为对订单的回应，制造商生产产品，并在检查后发货。对产品的需求可以是已知的，也可以具有一定的不确定性，这可以用模糊数需求来最好地表示。为了反映需求特性，本文引入了两种非线性整数规划模型，即CRISP模型和FUZZY模型。提出了一种遗传算法(GA)和混合遗传算法-模式搜索(HGAS)来求解该模型。数值实验表明，混合遗传算法比遗传算法具有更好的性能。还观察到，CRISP模型的平均总成本低于模糊模型。结果表明，该模型能够有效地评价合同执行情况，优化协同绿色供应链管理。
Concerns about environmentally sustainable supply chain management have increased widely in recent years. As a consequence, supply chain members have cooperated with one another to make efficient contracts, frequently called green supply-chain management contracts. The purpose of this paper is to investigate one such contract between a single manufacturer and multiple retailers with limited resources for several types of products under greenhouse-gas emission regulations. Each retailer orders the products regularly within a limited budget and warehouse capacity. In response to orders, the manufacturer produces products and ships them after inspections. Demand for the products can be either known or have some uncertainty, which can best be represented using fuzzy number demand. To reflect demand properties, this paper introduces two nonlinear integer programming models, a crisp model and a fuzzy model. A genetic algorithm (GA) and hybrid genetic algorithm-pattern search (HGAS) are developed to solve the models. Numerical experiments evaluating the efficiency of the algorithms showed that the HGAS method performed better than the GA. Also observed is that the crisp model's average total costs were lower than those of the fuzzy model. The results as a whole indicate that the models can evaluate the performance of contracts and optimize cooperative green supply chain management.