低碳视角下易腐品动态经济批量问题的鲁棒运作策略研究

In the background of low-carbon economy, enterprises that mange the deteriorating product are facing dual challenges from the constraint of carbon emission regulations and the lack of uncertain demand information. It is necessary for the decision-making to adopt the operational policy with robustness to reduce the total cost and carbon emissions. Motivated by such practical challenges, this research program studies the impacts of carbon emission regulations and lack of demand information on operational policy using robust optimization, duality theory, computational complexity and orthogonal test method. First, we will formulate dynamic lot-sizing models for deteriorating product under carbon emission regulations and study the inventory strategies for several cases that the uncertain demand distribution in each period belongs to uncertainty sets such as the interval, ellipsoid and polyhedral sets. The objective of each model is to minimize the worst-case expected total cost. We study the joint inventory and emission reduction policies when the uncertain demand in each period depends on the emission reduction technology level. Second, we formulate a factor-based demand function for deteriorating product in which demand in each demand is affinely dependent on several uncertain factors. Using the Affinely Adjustable Robust Counterpart(AARC) method, we study the inventory strategy with robustness. We further formulate a dynamic lot-sizing model with dual sourcing for deteriorating product and use the AARC method to solve the inventory policy to minimize the worst-case expected cost. Finally, we illustrate each theoretical model empirically and use orthogonal test to perform a robust sensitive analysis on several key parameters. The contributions of this research program to the existing literature are that we consider impacts of deterioration, carbon emission regulation and lack of uncertain demand information on firm's operational strategy. We will investigate the robustness of each model considered in this research program and focus on find the operational policy with achieving low cost and carbon emissions.

在低碳经济背景下，经营易腐类产品的企业面临碳政策约束和需求信息缺失的双重挑战，因此这些企业常采取能够同时降低成本和碳排放的鲁棒运作策略。本项目基于易腐品动态经济批量模型，综合运用鲁棒优化、对偶理论、计算复杂性理论和正交试验等方法分析碳政策和需求信息缺失对企业多周期运作决策的影响。首先，当各周期的需求分布隶属于不同不确定集时，考虑易腐品经营企业在碳政策下的库存策略，然后进一步分析其联合库存和减排投资策略；其次，考虑基于因素的需求函数，利用仿射可调节鲁棒方法研究易腐品的多周期库存策略，并分析该模型解的保守程度，然后，进一步考虑企业的双资源采购策略；最后，基于案例研究对理论结果进行例证，同时利用正交试验的方法找出对企业运作决策具有鲁棒性的参数集。本项目的创新之处是综合考虑产品变质、碳政策和信息缺失对企业运作决策的影响，研究重点则是寻找能够实现低成本和低排放的鲁棒运营策略。

在低碳减排和已知部分市场信息双重挑战下，采取具有鲁棒性的运营策略是助力企业实现双碳目标的关键问题之一。对此，本项目综合运用鲁棒优化、正交实验、均值方法和条件风险值等相关理论与方法重点分析碳政策规制下易腐品供应链企业的运营策略。首先，对于需求信息部分已知的情形，构建不同碳政策规制下的分布式鲁棒优化模型，重点从降低解的保守程度的角度探讨了低碳减排对供应链企业运营策略的影响。其次，研究易腐品供应链企业在碳政策下的联合价格和减排投资策略，进一步基于正交实验检验了供应链运营和协调策略的鲁棒性。然后，从企业决策者具有风险厌恶属性的视角，分析不同碳政策下供应链企业的联合减排和订购策略，包括研究了渠道权力对碳限额与交易政策下风险厌恶型供应链运营策略的影响。最后，构建不同碳政策下复杂供应链优化模型，系统研究了供应链联合生产和回收策略、双渠道运营策略以及多产品联合定价和可持续投入策略。主要研究结果表明：与极大极小期望利润准则相比，乐观系数决策准则能够降低碳政策规制下企业鲁棒性运营策略的保守程度；存在具有鲁棒性的协调策略使得碳限额与交易政策规制下的易腐品供应链实现高利润和低排放；风险厌恶型企业决策者投资减排技术，能够使得碳政策下供应链的经济和环境绩效得到改善；在回收法规和碳限额政策双重规制下，再制造企业尽管能够有效降低碳排放，但其利润也有可能会减少。上述研究结果主要发表和录用在EJOR, IJPE、AOR,TRE等主流期刊。本项目得到的相关研究结论能够为政府制定碳参数和企业运营决策提供一定的参考依据和理论支撑。