Models and Algorithms for Integrated Multi-Stage Production/Distribution Systems

该赠款为在确定性和随机性环境中对几类多阶段供应链进行严格分析提供资金。 将为最佳生产和分配政策的结构分析开发新的模型。 这些模型将被用作开发新的，高效的和强大的物流的基础，用于集成的生产和运输规划。 这些模型和物流将被扩展到各种相对复杂的环境，包括设置具有多阶段的串行生产，多组件装配网络，多类运输成本函数，多类需求，和制造到库存和制造到订单的生产策略。 在所有阶段都朝着同一目标努力的集中式环境中探索这些模型之后，将考虑每个阶段都努力实现自己目标的分散式环境。 将探讨权力下放造成的效率损失，并将建立协调机制。这些模型和算法将通过工业数据进行验证，以检验见解的适用性和算法的有效性。如果成功，本研究的结果将有助于加深对现代供应链中集成生产和运输计划的理解，特别是有助于解释典型制造业供应链中有限能力和分销规模经济的影响。 这种洞察力，以及将开发的算法，将导致这些供应链中更有效和更有效的决策，并最终导致更复杂的现实世界模型的有效分析。 最后，在这项研究中开发的见解将被纳入一个案例研究和教学模块。

This grant provides funding for a rigorous analysis of several classes of multi-stage supply chains in both deterministic and stochastic settings. Novel models will be developed for the structural analysis of optimal production and distribution policies. These models will be used as a foundation for the development of new, efficient, and robust heuristics for integrated production and transportation planning. These models and heuristics will be extended to a variety of relatively complex environments, including settings featuring multi-stage serial production, multi-component assembly networks, multi-class transportation cost functions, multi-class demand, and make-to-stock and make-to-order production strategies. After exploring these models in a centralized setting in which all stages are working toward the same objective, decentralized settings where each stage strives to achieve its own objective will be considered. Loss of efficiency due to decentralization will be explored, and coordination mechanisms will be developed. These models and algorithms will be validated with industrial data, in order to test the applicability of insights and the effectiveness of algorithms.If successful, the results of this research will lead to a deeper understanding of integrated production and transportation planning in modern supply chains, and in particular, will help to explain the impact of finite capacity and distribution economies of scale in typical manufacturing supply chains. This insight, as well as the algorithms which will be developed, will lead to more efficient and effective decision-making in these supply chains, and will ultimately lead to effective heuristics for even more complex real-world models. Finally, the insights developed in this research will be incorporated into a case study and a teaching module.