Closed Loop Supply Chain Design for Uncertain Carbon Regulations and Random Product Flows

该奖项的研究目标是制定和解决一个可处理的闭环供应链网络设计问题，其中包括：(a)对成本变化和可能的碳排放法规约束影响具有鲁棒性的设施配置；(b)适应需求和回报随机变化的各种运输方式的运输能力；(c)平衡收入、运输成本和排放相关成本的产品退货政策。该问题的难点在于设施投资和回报政策选择的离散性、寻求稳健设计所带来的非线性以及精确表示随机需求和回报数量所需的大量场景。通过限制运输能力对不确定碳政策参数的依赖，得到了具有随机子问题的设计问题的仿射可调鲁棒对应。研究任务是逼近这个鲁棒对应的离散解，并有效地减少需要考虑的随机场景的数量。如果成功，本研究的结果将有助于制造企业通过积极设计闭环供应链来保持全球竞争力，以预期可能的市场机制来控制碳排放，同时减轻与运输需求相关的不确定性。鲁棒优化和随机优化的创新结合将广泛适用于不确定性下的能力投资问题，这些不确定性取决于历史先例的存在和条件的可重复性。这项工作的结果和见解将有助于培养网络和随机优化方面的研究生以及工程经济分析方面的本科生。将为工程经济学课程开发一个相关的复杂的、结构不良的解决问题的经验，并将通过开源软件工具传播，以便在大量招生的环境中采用。

The research objective of this award is to formulate and solve a tractable closed-loop supply chain network design problem that includes: (a) facility configuration that is robust to variations in the cost and constraint implications of possible carbon emission regulations; (b) transportation capacities of various modes that accommodate random variations in demands and returns; and (c) a product return policy that balances the tradeoffs in revenues, transportation costs, and emission-related costs. The problem's difficulty arises from the discrete character of facility investment and return policy choices, the nonlinearity introduced by seeking a robust design, and the large number of scenarios required to accurately represent stochastic demand and return quantities. By restricting how the transportation capacities depend on the uncertain carbon policy parameters, an affinely adjustable robust counterpart is obtained for the design problem with stochastic subproblems. The research tasks are to approximate the discrete solution to this robust counterpart and to efficiently reduce the number of stochastic scenarios to be considered.If successful, the results of this research will help manufacturing firms to maintain global competitiveness by proactively designing their closed loop supply chains in anticipation of possible market mechanisms to control carbon emissions while mitigating the uncertainties associated with transportation demands. The innovative combination of robust and stochastic optimization will be widely applicable to capacity investment problems under uncertainties that differ fundamentally in character, depending on the existence of historical precedent and the repeatability of conditions. Results and insights derived from this work will enhance the training of graduate students in network and stochastic optimization as well as undergraduate students in engineering economic analysis. A related complex, ill-structured problem-solving experience will be developed for engineering economy courses and, to ease its adoption in large-enrollment settings, will be disseminated via open-source software tools.