利用拉格朗日弛豫在不确定需求下定位氢气生产设施:以挪威为例。

IF 1.3 Q3 SOCIAL SCIENCES, MATHEMATICAL METHODS Computational Management Science Pub Date : 2023-01-01 DOI:10.1007/s10287-023-00445-3
Šárka Štádlerová, Sanjay Dominik Jena, Peter Schütz
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引用次数: 2

摘要

氢被认为是运输部门脱碳的解决方案,是满足巴黎协定要求的重要一步。尽管未来几年氢的需求预计会增加,但随着时间的推移,确切的需求水平仍然是不确定性的主要来源。我们研究了氢气生产工厂的地点和时间问题,以满足不确定的未来客户需求。我们将该问题表述为一个两阶段随机多周期的设施选址和产能扩张问题。第一阶段的决策与生产工厂的位置和初始产能有关,必须在了解客户需求之前做出。它们包括为所选的容量水平选择具有分段线性凸短期成本函数的模块化容量。在第二阶段,做出有关产能扩张和需求分配的决策。考虑到公式的复杂性,我们使用拉格朗日分解启发式来解决这个问题。我们的方法能够在几个小时内找到足够高质量的解,即使对于商业求解器来说太大的实例也是如此。我们将我们的模型应用于挪威的一个案例,并为交通部门设计相应的氢基础设施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Using Lagrangian relaxation to locate hydrogen production facilities under uncertain demand: a case study from Norway.

Hydrogen is considered a solution to decarbonize the transportation sector, an important step to meet the requirements of the Paris agreement. Even though hydrogen demand is expected to increase over the next years, the exact demand level over time remains a main source of uncertainty. We study the problem of where and when to locate hydrogen production plants to satisfy uncertain future customer demand. We formulate our problem as a two-stage stochastic multi-period facility location and capacity expansion problem. The first-stage decisions are related to the location and initial capacity of the production plants and have to be taken before customer demand is known. They involve selecting a modular capacity with a piecewise linear, convex short-term cost function for the chosen capacity level. In the second stage, decisions regarding capacity expansion and demand allocation are taken. Given the complexity of the formulation, we solve the problem using a Lagrangian decomposition heuristic. Our method is capable of finding solutions of sufficiently high quality within a few hours, even for instances too large for commercial solvers. We apply our model to a case from Norway and design the corresponding hydrogen infrastructure for the transportation sector.

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来源期刊
Computational Management Science
Computational Management Science SOCIAL SCIENCES, MATHEMATICAL METHODS-
CiteScore
1.90
自引率
11.10%
发文量
13
期刊介绍: Computational Management Science (CMS) is an international journal focusing on all computational aspects of management science. These include theoretical and empirical analysis of computational models; computational statistics; analysis and applications of constrained, unconstrained, robust, stochastic and combinatorial optimisation algorithms; dynamic models, such as dynamic programming and decision trees; new search tools and algorithms for global optimisation, modelling, learning and forecasting; models and tools of knowledge acquisition. The emphasis on computational paradigms is an intended feature of CMS, distinguishing it from more classical operations research journals. Officially cited as: Comput Manag Sci
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