| 项目编号: | 1510787
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| 项目名称: | UNS: Improved Risk Mitigation Strategies for Industrial Process Scheduling |
| 作者: | Chrysanthos Gounaris
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| 承担单位: | Carnegie-Mellon University
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| 批准年: | 2014
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| 开始日期: | 2015-09-01
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| 结束日期: | 2018-08-31
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| 资助金额: | USD310223
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| 资助来源: | US-NSF
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| 项目类别: | Standard Grant
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| 国家: | US
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| 语种: | 英语
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| 特色学科分类: | Engineering - Chemical, Bioengineering, Environmental, and Transport Systems
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| 英文关键词: | pso
; risk mitigation methodology
; aro
; financial risk
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| 英文摘要: | Gounaris, 1510787
This 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. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/93587
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| Appears in Collections: | 影响、适应和脆弱性
气候减缓与适应
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| Recommended Citation: | |
Chrysanthos Gounaris. UNS: Improved Risk Mitigation Strategies for Industrial Process Scheduling. 2014-01-01.
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