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

这笔赠款为在确定性和随机性环境下对几类多阶段供应链进行严格分析提供了资金。将开发新的模型，对最优生产和分配政策进行结构性分析。这些模型将被用作开发用于综合生产和运输计划的新的、高效的和健壮的启发式方法的基础。这些模型和启发式算法将扩展到各种相对复杂的环境，包括多阶段连续生产、多组件组装网络、多类别运输成本函数、多类别需求以及按库存和按订单生产的生产策略。在所有阶段都朝着相同的目标工作的集中环境中探索这些模型之后，将考虑每个阶段努力实现其自身目标的分散环境。将探讨权力下放造成的效率损失，并建立协调机制。这些模型和算法将通过工业数据进行验证，以检验洞察力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，特别是有助于解释有限能力和分销规模经济对典型制造业供应链的影响。这种洞察力以及将开发的算法将在这些供应链中导致更高效和有效的决策，并最终将导致对更复杂的现实世界模型的有效启发式方法。最后，本研究提出的见解将被纳入一个案例研究和一个教学模块。

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.