Managing Shutdown Decisions in Merchant Commodity and Energy Production: A Social Commerce Perspective

41 Pages Posted: 13 Sep 2017 Last revised: 15 May 2018

Alessio Trivella

Technical University of Denmark - Management Engineering

Selvaprabu Nadarajah

University of Illinois at Chicago - College of Business Administration

Stein-Erik Fleten

Norwegian University of Science and Technology (NTNU)

Denis Mazieres

University of London - Economics, Mathematics and Statistics

David Pisinger

Technical University of Denmark - Management Engineering

Date Written: May 1, 2018

Abstract

Merchant commodity and energy production assets operate in markets with volatile prices and exchange rates. Producers can choose in each period between production, production suspension, and permanent shutdown. Plant closures, however, adversely affect societal entities beyond the specific plant being shutdown such as the parent company and the local community. Motivated by an aluminum producer, we study if mitigating these hard-to-assess broader impacts of a shutdown is financially viable using the plant's operating flexibility. Our social commerce perspective towards managing shutdown decisions deviates from the commonly used asset value maximization objective in merchant operations. We formulate a high-dimensional constrained Markov decision process to manage shutdown decisions. We approximate this intractable model using unconstrained stochastic dynamic programs and define operating policies that account for preferences to delay and reduce shutdowns. Our first policy leverages anticipated regret theory in behavioral psychology while the second policy generalizes production margin heuristics used in practice using machine learning. We compute the former and latter policies using a least squares Monte Carlo method and combining this method with binary classification, respectively. We also propose a reoptimization heuristic to simplify the anticipated-regret policy. We show that anticipated-regret policies possess desirable asymptotic properties absent in classification- and reoptimization-based policies. On instances created using real data, anticipated-regret and classification-based policies outperform practice-based production margin strategies and, to a lesser extent, reoptimization. Specifically, the former policies decrease the shutdown probability by 25% and, in addition, delay shutdown decisions by an average of 4 years for a 4% asset value loss. Our operating policies show that unaccounted social costs amounting to a few percent of the maximum asset value can justify delaying or avoiding the use of a plant's shutdown option by adapting its operating flexibility in our application. Thus, taking a social commerce perspective towards managing a plant's operating flexibility appears financially viable.

Suggested Citation

Trivella, Alessio and Nadarajah, Selvaprabu and Fleten, Stein-Erik and Mazieres, Denis and Pisinger, David, Managing Shutdown Decisions in Merchant Commodity and Energy Production: A Social Commerce Perspective (May 1, 2018). Available at SSRN: https://ssrn.com/abstract=3034869 or http://dx.doi.org/10.2139/ssrn.3034869

Alessio Trivella

Technical University of Denmark - Management Engineering ( email )

Produktionstorvet 424
room 043
Kgs. Lyngby, 2800
Denmark

Selvaprabu Nadarajah (Contact Author)

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

601 South Morgan Street
Chicago, IL 60607
United States

Stein-Erik Fleten

Norwegian University of Science and Technology (NTNU) ( email )

Trondheim NO-7491
Norway

Denis Mazieres

University of London - Economics, Mathematics and Statistics ( email )

United States

David Pisinger

Technical University of Denmark - Management Engineering ( email )

Produktionstorvet 424
room 043
Kgs. Lyngby, 2800
Denmark

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