Integrated tactical planning under uncertainty in the supply chain of divergent-type production systems

本研究主要探讨在不确定性下供应链策略规划之稳健性。供应链中的战术计划包括在包括供应商、生产单位和配送中心的网络中对采购、生产、配送和销售活动的综合计划。发散型生产系统的特点是一对多的配方。这种生产系统通常面临不同类型的不确定性。传统的供应链计划工具是基于确定性优化模型的。确定性的方法提出的计划是不现实的，在未来的不确定事件的存在下，通常会导致减少需求履行承诺的服务水平相比。采用稳健的规划工具，可以导致显着的改善服务水平，资源利用率，采购和分销成本，环境影响等，这项研究计划的主要目标是开发高效的稳健的战术规划模型的供应链中存在的不确定性，发散型生产系统，使他们可以应用于工业。本研究的其他目标包括应用所提出的强大的战术规划工具，1）软木供应链，我们正面临着在日志的特性的非同质性，和2）在一个反向供应链，专注于回收的不确定条件下的寿命结束的产品。随机规划和鲁棒优化方法将被采用，以模拟供应链战术规划问题下的不确定性。基于精确和启发式方法的高效混合求解算法也将被提出来解决由此产生的模型。对于每个应用程序，我们的目标是分析和比较两个强大的战术规划工具所需的建模和计算工作量，估计的不确定性集的有效性，结果的质量，和实施的限制。虽然拟议的强大的战术规划工具是重要的加拿大工业从经济和社会的角度来看，这项研究计划也将推进文献的随机优化。

The proposed research program investigates the robustness in supply chain tactical planning in divergent type production systems faced with uncertainty. Tactical planning in a supply chain consists of integrated planning of procurement, production, distribution and sale activities in a network including suppliers, production units, and distribution centers. The divergent-type production systems are featured by the one-to-many recipes. Such production systems are faced usually with different types of uncertainties. The traditional supply chain planning tools are based on deterministic optimization models. The plans proposed by deterministic approaches are not realistic and robust in the presence of future uncertain events and will usually lead to decreased demand fulfillment comparing the promised service level. Adopting robust planning tools can lead to significant improvements in service level, resource utilization, procurement and distribution costs, environmental effects, etc. The main goal of this research program is to develop efficient robust tactical planning models for the supply chain of divergent-type production systems in the presence of uncertainties, such that they can be applied in industry. Other objectives of this research consist of application of the proposed robust tactical planning tool for 1) the softwood supply chain, where we are facing with the non-homogeneity in the characteristics of logs, and 2) in a reverse supply chain focused on the recovery of end-of-life products with uncertain conditions. Stochastic programming and robust optimization approaches will be adopted in order to model the supply chain tactical planning problem under uncertainty. Efficient hybrid solution algorithms based on exact and heuristic methods will also be proposed to solve the resulting models. For each application, we aim to analyze and compare the two robust tactical planning tools in terms of required modeling and computational effort, validity of estimated uncertainty sets, quality of results, and implementation limitations. Whilst the proposed robust tactical planning tools are important for Canadian industries from both economical and social points of view, this research program will also advance the literature of stochastic optimization.