UNS: Improved Risk Mitigation Strategies for Industrial Process Scheduling

UNS：改进工业流程调度的风险缓解策略

• 批准号: 1510787
• 负责人: Chrysanthos Gounaris
• 金额: $ 31.02万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1510787&HistoricalAwards=false
• 关键词: UNS; Improved; Risk; Mitigation; Strategies

Gounaris, 1510787This project addresses the mitigation of risk in the context of Process Scheduling Optimization (PSO) in view of uncertainty in problem parameters. The term PSO refers to a family of decision-making problems that are prevalent in the chemical process industries and which are typically embedded in the Manufacturing Execution System supervising a plant's operations. The archetypal setting is the one where a set of limited available resources (e.g., equipment, personnel, raw materials, utilities) needs to be coordinated ("scheduled") along a time horizon so as to meet a number of production goals. Identifying optimal solutions, and sometimes even obtaining a single feasible solution, of a PSO instance is generally a challenging task. This is due to the various compounding combinatorial complexities involved, including complexities stemming from the plant's topology (flowsheet), complicated production recipes, or other operational restrictions (market-related, regulatory, etc.). The objective in PSO is typically the maximization of profit ("produce as much as you can within a limited amount of time") or the minimization of makespan ("produce a fixed amount as soon as possible"), though additional objectives, such as the balancing of resource utilization load or the minimization of environmental footprint, can also be considered.The technical objective is to develop an Adjustable Robust Optimization (ARO) framework for the systematic treatment of uncertainty in PSO. PSO involves the coordination of limited available resources along a time horizon so as to meet a number of production goals. Identifying optimal solutions of a PSO instance is generally a challenging task, further complicated by the fact that it is of practical interest that such production management systems take into account uncertainties in input data, since failure to do so may lead to solutions that are infeasible or highly suboptimal. This project applies ARO, a risk mitigation methodology extending the paradigm of Robust Optimization (RO) that seeks to optimize the problem in view of a "worst-case" scenario, as dictated by an uncertainty set. But unlike RO, which results in a static, "here-and-now" solution that is often overly conservative, ARO results in a more flexible--and generally more profitable--solution policy by adjusting the decisions on the actual realizations of the uncertain parameters that have already occurred and been observed by the time of the decision. Effective algorithms to mitigate technical and financial risk in the process industries can play an important role in the competitiveness, product quality and sustainability of the U.S. manufacturing base. Exploiting efficiencies in process operations limits environmental impact as well as promotes occupational health and safety. Adopting these innovations could provide tangible benefits to individual companies by materializing efficiencies in their utilization of process equipment, raw materials and personnel. This could be particularly useful for small companies, which cannot readily develop an "in-house" framework suitable to their setting. There is also the potential to enhance products of software vendors in the sector of manufacturing and enterprise resource planning. Potential educational benefits will be in generating material for a relevant course and creating an educationally-focused PSO-themed software applet. All students will receive training in production management, optimization methods and algorithms, uncertainty quantification and analysis, and scientific computation.

Gounaris，1510787鉴于问题参数的不确定性，该项目解决了过程调度优化（PSO）背景下的风险缓解问题。PSO一词是指一系列决策问题，这些问题在化学过程工业中普遍存在，并且通常嵌入在监督工厂操作的制造执行系统中。原型设置是一组有限的可用资源（例如，设备、人员、原材料、公用设施）需要沿着时间范围进行协调（“调度”），以便满足多个生产目标。识别PSO实例的最优解，有时甚至获得单个可行解，通常是一项具有挑战性的任务。这是由于所涉及的各种复合组合复杂性，包括源自工厂拓扑结构（流程图）的复杂性、复杂的生产配方或其他操作限制（市场相关的、监管的等）。粒子群优化算法的目标通常是利润最大化（“在有限的时间内尽可能多地生产”）或最小化完工时间（“尽快生产固定数量”），但其他目标，如平衡资源利用负荷或最大限度地减少环境足迹，的技术目标是开发一个可调整的鲁棒优化（ARO）的框架，在PSO的不确定性的系统处理。PSO涉及在沿着时间范围内协调有限的可用资源，以满足若干生产目标。识别PSO实例的最优解通常是一项具有挑战性的任务，进一步复杂化的事实是，这是实际利益，这种生产管理系统考虑到输入数据中的不确定性，因为不这样做可能会导致不可行或高度次优的解决方案。该项目应用ARO，风险缓解方法扩展了鲁棒优化（RO）的范式，旨在优化问题的“最坏情况”的情况下，由不确定性集决定。但是，与RO不同，RO会导致静态的，“此时此地”的解决方案，通常过于保守，ARO会导致更灵活-通常更有利可图-通过调整决策的不确定参数的实际实现的决策，这些参数已经发生并在决策时观察到。有效的算法，以减轻技术和金融风险的过程工业可以发挥重要作用的竞争力，产品质量和可持续性的美国制造基地。利用工艺操作的效率限制了对环境的影响，并促进了职业健康和安全。采用这些创新可以通过提高工艺设备、原材料和人员的利用效率，为各个公司提供切实的好处。这对小公司特别有用，因为它们不能轻易地制定适合其环境的“内部”框架。在制造业和企业资源规划部门，也有可能加强软件供应商的产品。潜在的教育效益将是为相关课程生成材料，并创建一个以教育为重点的PSO主题软件小程序。所有学生将接受生产管理，优化方法和算法，不确定性量化和分析以及科学计算方面的培训。