Recycling/Reuse Systems - Network Growth and Persistence

这笔赠款提供资金，用于发展方法和工具，以改善收集和处理、再循环/再使用网络中的材料和产品流动。收集系统的增长和毅力决策以及产品生命周期是整个系统流量潜力的关键决定因素，并将通过随机动态规划建模，采用分层方法计划战略收集网络增长，而较低层次决定网络运营和物流。长期系统的成功需要各种相互依赖的收集和处理代理之间的关系谈判，这些代理具有各自的利润目标，并且不愿意随着产品、运输/处理成本和市场的变化而泄露机密信息，因此将开发一种方法来确定均衡收购价格以及收集者和处理者之间的数学流分配机制。通过使用最先进的大规模鲁棒优化方法确定关键场景，将有助于理解不确定性对关键决策的潜在影响。这些方法将用于随着时间和产品生命周期发展对全球材料流动的更深层次的理解。与地毯和电子公司的互动将允许这些工具在各种环境中进行测试和改进。通过与公司、非营利组织和政府机构的合作，研究结果将有助于为不断增长的收集网络做出更好的决策，并将其与处理能力联系起来，并将其实际应用于地毯和电子工业。研究结果为复杂的供应链决策提供了知识库，不仅使问题表示更加丰富，而且为解决供应链设计背景下的大规模问题提供了新的方法。学生研究人员和工业伙伴将接触到对现代工业化社会具有重要意义的大量问题，以及一套可用于广泛决策问题的技术。

The grant provides funding to develop methodology and tools to improve the material and product flows in collection and processing recycling/reuse networks. Collection system growth and perseverance decisions along with product lifecycles are key determinants of overall system flow potential, and will be modeled via stochastic dynamic programming with an hierarchical approach to plan strategic collection network growth while lower levels determine network operation and logistics. Long term system success requires relationship negotiation among various interdependent collection and processing agents with individual profit objectives and unwillingness to reveal confidential information as products, transportation/processing costs, and markets shift, so a methodology will be developed to determine equilibrium acquisition prices as well as mathematical flow allocation mechanisms between tiers of collectors and processors. Understanding the potential impact of uncertainty on key decisions will be facilitated by determining pivotal scenarios using state of the art methodology for large scale robust optimization. These approaches will be used to develop a deeper understanding of global material flows over time and product lifecycles. Interactions with the carpet and electronic companies will allow these tools to be tested and refined in a variety of contexts.Working with companies, non-profits, and government agencies, the results will enable better decision making for growing collection networks and connecting them with processing capabilities, with practical application to the carpet and electronics industries. The results contribute to the knowledge base for complex supply chain decision-making, both by making the problem representation richer and by developing new approaches for solving large-scale problems in the context of supply chain design. Student researchers and industrial partners will gain exposure to a rich class of problems of significant importance to modern industrialized societies and a set of techniques that can be brought to bear on a wide range of decision-making problems.