Structured Data-Driven Operating Policies for Commodity Storage

43 Pages Posted: 14 Jun 2019

See all articles by Christian Mandl

Christian Mandl

Technische Universität München (TUM)

Selvaprabu Nadarajah

University of Illinois at Chicago - College of Business Administration

Stefan Minner

Technische Universität München (TUM) - TUM School of Management

Srinagesh Gavirneni

Cornell University - Samuel Curtis Johnson Graduate School of Management

Date Written: June 3, 2019

Abstract

Storage assets are critical for temporal trading of commodities under volatile prices. State-of-the-art methods for managing storage such as the reoptimization heuristic (RH), which are part of commercial software, approximate a Markov decision process (MDP) assuming full information regarding the state and the stochastic commodity price process and hence suffer from informational inconsistencies with observed price data and structural inconsistencies with the true optimal policy, which are both components of generalization error. Based on extensive backtests, we find that this error can lead to significantly suboptimal RH policies and qualitatively different performance compared to the known near-optimality and behavior of RH in the full-information setting. We develop a forward-looking data-driven approach (DDA) to learn policies and overcome generalization error. This approach extends standard (backward-looking) DDA in two ways: (i) it uses financial-market features and estimates of future pro ts as part of the training objective, which typically includes past pro ts alone; and (ii) it enforces structural properties of the optimal policy. To elaborate, DDA trains parameters of bang-bang and base-stock policies, respectively, by solving linear-and mixed-integer programs, thereby extending known DDAs that parameterize decisions as functions of features without enforcing policy structure. We backtest the performance of DDA and RH on six major commodities from 2000 to 2017 with features constructed using Thomson Reuters and Bloomberg data. DDA significantly improves RH on real data, with base-stock structure needed to realize this improvement. Our research advances the state-of-the-art for storage operations and suggests modifications to commercial software to handle generalization error.

Keywords: commodity storage, price uncertainty, generalization error, data-driven optimization

Suggested Citation

Mandl, Christian and Nadarajah, Selvaprabu and Minner, Stefan and Gavirneni, Srinagesh, Structured Data-Driven Operating Policies for Commodity Storage (June 3, 2019). Available at SSRN: https://ssrn.com/abstract=3398119 or http://dx.doi.org/10.2139/ssrn.3398119

Christian Mandl (Contact Author)

Technische Universität München (TUM) ( email )

Arcisstrasse 21
Munich, DE 80333
Germany

Selvaprabu Nadarajah

University of Illinois at Chicago - College of Business Administration ( email )

601 South Morgan Street
Chicago, IL 60607
United States

Stefan Minner

Technische Universität München (TUM) - TUM School of Management ( email )

Arcisstrasse 21
München, 80333
Germany

HOME PAGE: http://www.log.wi.tum.de

Srinagesh Gavirneni

Cornell University - Samuel Curtis Johnson Graduate School of Management ( email )

Ithaca, NY 14853
United States

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