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

Alessio Trivella (), Selvaprabu Nadarajah (), Stein-Erik Fleten, Denis Mazieres () and David Pisinger ()
Additional contact information
Alessio Trivella: Institute for Transport Planning and Systems, ETH Zurich, 8092 Zurich Switzerland;
Selvaprabu Nadarajah: College of Business Administration, University of Illinois at Chicago, Chicago, IL 60607;
Denis Mazieres: Department of Economics, Mathematics and Statistics, Birkbeck University of London, London WC1E 7HX, United Kingdom;
David Pisinger: Department of Management Engineering, Technical University of Denmark, 2800 Kongens Lyngby, Denmark

Manufacturing & Service Operations Management, 2021, vol. 23, issue 2, 311-330

Abstract: Problem definition : Merchant commodity and energy production assets operate in markets with volatile prices and exchange rates. Plant closures adversely affect societal entities beyond the specific plant being shut down, 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. Academic/practical relevance : Our social commerce perspective toward managing shutdown decisions deviates from the commonly used asset value maximization objective in merchant operations. Identifying operating policies that delay or decrease the likelihood of a shutdown without incurring a significant asset value loss supports socially responsible plant shutdown decisions. Methodology : We formulate a constrained Markov decision process to manage shutdown decisions and limit the probability of future plant closures. We provide theoretical support for approximating this intractable model using unconstrained stochastic dynamic programs with modified shutdown costs and explore two classes of operating policies. Our first policy leverages anticipated regret theory, and the second policy generalizes, using machine learning, production-margin heuristics used in practice. We compute the former and latter policies using a least squares Monte Carlo method and combining this method with binary classification, respectively. Results : Anticipated-regret policies possess desirable asymptotic properties absent in classification-based policies. On instances created using real data, anticipated-regret and classification-based policies outperform practice-based production-margin strategies. Significant reductions in shutdown probability and delays in plant closures are possible while incurring small asset value losses. Managerial implications : A plant’s operating flexibility provides an effective lever to balance the social objective to reduce closures and the financial goal to maximize asset value. Adhering to both objectives requires combining short-term commitments with external stakeholders to avoid shutdown with longer-term internal efforts to reduce the probability of plant closures.

Keywords: 311-330 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:23:y:2021:i:2:p:311-330

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