Approaches for supply chain planning and scheduling

形成一个供应链就是与合作伙伴形成联盟，以顺利和有竞争力地管理客户需求。随着供应链的增长（在产品、合作伙伴等方面），各种活动的计划和协调变得越来越复杂。此外，大多数供应链都增加了在线渠道（商店）来销售产品。这种趋势给本已复杂的供应链带来了更大的压力——网上顾客更喜欢订购种类较少的产品，期望提前交货，而且对价格非常敏感。为了管理组织的核心流程，各种功能，如运输、仓储、包装/物流等都被外包。顺利管理需求需要供需之间的协调，因此需要所有合作伙伴之间的协调。有时这种及时的协调或合作是不可能的，从而导致产品失败。我们的研究目标之一是分析产品召回风险，其对合作伙伴关系的影响以及管理此类风险的合适策略。在供应链的任何部分，计划上的任何小的中断都有可能破坏整个供应链。由于不确定性（需求、供应、产能等），供应链任何部分的规划都变得困难。数学规划技术通常用于这种情况。这些方法是复杂的，基于聚合的数据，因此最适合长期规划（在短期内，由于随机性的传播，有时很难执行所采用的计划）。由于计算成本更低，数据收集也更容易，因此可以在动态环境中做出更好的决策。在本提案中，我们的另一个主要研究目标是为短期运营规划开发面向数据的解决方案方法，为长期规划开发基于高级优化的方法。为了开发这些方法，我们确定了一些常见的应用问题。两名硕士和两名博士将对相关文献进行系统的回顾，并通过提供新的工具来丰富现有知识，以解决这些复杂的问题。根据问题的复杂性、问题的大小和使用情况，我们将开发和测试各种方法的组合。将通过出版物和讨论会传播最有效和最适当的办法。特别是，我们的主要目标是：1)为某些供应链规划问题开发数据驱动的优化方法。2)发展随机规划问题的数学规划方法。3)分析供应链合作伙伴关系，管理产品召回风险。4)通过研究论文、会议报告和案例研究传播工作。

Forming a supply chain is all about forming alliances with partners for managing the customer demand smoothly and competitively. As the supply chain grows (in terms of products, partners, etc.) the planning and coordination of various activities become more and more complex. In addition, most of the supply chains adding the online channels (stores) for selling the products. Such trend adding more pressure on the already complex supply chains - online customers prefer to order a small assortment of the products, expect an early delivery and are very price sensitive. In order to manage the core processes of the organization, various functions such as transportation, warehousing, packaging/logistics, etc. are outsourced. Managing the demand smoothly requires coordination between supply and demand and therefore coordination among all the partners. Sometimes such timely coordination or cooperation is not possible and that leads to product failure. One of our research goals is to analyze product recall risk, its impact on partnership and suitable strategies to manage such risks. Any small disruption in planning in any part of the supply chain has the potential to disrupt the whole supply chain. Planning at any part of the supply chain becomes difficult due to uncertainty (of demand, supply, capacity, etc.). Mathematical programming techniques are commonly used in such situations. These approaches are complex, based on aggregated data and thus most suitable for long term planning (in the shorter term, sometimes it is difficult to chase the adopted plan due to the spread of the randomness). As computing is more affordable and data gathering is easy, better decisions could be taken in a dynamic environment. In this proposal, our other major research goal to develop data-oriented solution approaches for short term operational planning and advanced optimization-based approaches for long term planning. In order to develop these approaches, we have identified a few common applied problems. Two MSc and two Ph.D. students will conduct a systematic review of the relevant literature and enrich the existing knowledge by contributing novel tools to solve such complex problems. Based on the problem complexity, problem size and usage we will develop and test a combination of approaches. Most effective and suitable approaches will be disseminated through publications and seminars. In particular, our main objectives are: 1) Develop data-driven optimization approaches for certain supply chain planning problems. 2) Develop mathematical programming approaches for stochastic planning problems. 3) Analyze supply chain partnerships for managing product recall risks. 4) Disseminate the work through research papers, conference presentations, and case studies.