Selva: Publications

Selva Nadarajah [selvan@uic.edu]

My research addresses challenges at the interface of operations and finance arising in the energy industry using reinforcement learning and optimization. I am in particular interested in the operations of commodity and energy conversion assets (e.g., storage, transport, and production), their valuation and risk management, the role of corporations in the clean energy transition, and the social impact of this transition.

Influenced by my energy research and work with industry, I am passionate about making reinforcement learning and optimization methods easier to adopt by reducing the effort needed to deploy them effectively across different applications. To this end, I develop methods that embed solve intelligence and are capable of adapting over time and to instance-specific information in a problem class.

(click on paper title to view abstract)

Submitted Journal Papers

Energy and the Environment

Decarbonizing buildings via energy demand response and deep reinforcement learning: The deployment value of supervisory planning and guardrails. (with D. Jang, L. Spangher, C. Spanos). [pdf]

Energy demand response is projected to be critically important in decarbonizing the grids of the future. We propose an agent to communicate ``artificial" price signals (i.e., prices for each hour) to workers as part of an office building demand response program, where workers react to this price signal by modifying their energy consumption to off-peak and less carbon intensive hours of the day. The efficacy of this agent depends on its ability to simultaneously learn how workers respond to price signals and choose them such that the building's energy costs with respect to a time-of-use utility rate is reduced. High energy costs can hinder deployment if the agent's price signals are sub-optimal when learning energy consumption. We assess the value of deep reinforcement learning (RL) technology in mitigating this deployment hurdle. Specifically, we explore the value of combining: (i) a model-free deep RL method that can learn by posting price signals to actual workers, (ii) a supervisory ``planning model'' that provides a synthetic learning environment, and (iii) a guardrail method that determines whether a price should be posted to real workers or the planning environment for feedback. This combination results in a risk-aware deep RL controller that uses the planning model for learning when it thinks that a price is very likely to incur high energy costs in the real environment. In a medium-sized office building, our risk-aware controller eliminates $175,000 in initial investment, decreases by 30% the energy cost, and reduces emissions by 32% (106,000 tons of CO2) compared to using the time-of-use rate as the price signal. In contrast, existing model-free and model-based deep RL algorithms are unable to overcome initial learning costs. Our results bode well for risk-aware deep RL facilitating the deployment of building demand response.
A review of the operations literature on real options in energy. (with N. Secomandi). [pdf; xlsx table; Invited review article; Under second round review at European Journal of Operational Research]

Real option models maximize the estimated market value of operational assets, exploiting the flexibility that decision makers have in managing these assets. Inspired by the valuation of financial options, their natural domains of application feature high levels of market or operational risk. The existence of financial markets that trade instruments associated with the inputs or outputs of the underlying processes facilitates the use of these models. The energy industry has thus received a substantial amount of attention within the real option and operations (management/research) communities. The extant work lacks a review of the operations literature on energy real options. We present a synthesis of this literature considering review categories that pertain to the phenomena it studies and the tools it uses to conduct its analysis. Further, we outline potential research directions.
Least squares Monte Carlo and pathwise optimization for merchant energy production. (with B. Yang, N. Secomandi). [ssrn; Under revision for third round review at Operations Research]

We study merchant energy production modeled as a compound switching and timing option. The resulting Markov decision process is intractable. Least squares Monte Carlo combined with information relaxation and duality is a state-of-the-art reinforcement learning methodology to obtain operating policies and optimality gaps for related models. Pathwise optimization is a competing technique developed for optimal stopping settings, in which it typically provides superior results compared to this approach, albeit with a larger computational effort. We apply these procedures to merchant energy production. Employing pathwise optimization requires methodological extensions. We use principal component analysis and block coordinate descent in novel ways to respectively precondition and solve the ensuing ill-conditioned and large scale linear program, which even a cutting-edge commercial solver is unable to handle directly. Both techniques yield near optimal operating policies on realistic ethanol production instances. However, at the cost of both considerably longer run times and greater memory usage, pathwise optimization leads to substantially tighter dual bounds compared to least squares Monte Carlo, even when specified in a simple fashion, complementing it in this case. Thus, it plays a critical role in obtaining small optimality gaps. Our numerical observations on the magnitudes of these bound improvements differ from what is currently known. This research has potential relevance for other commodity merchant operations contexts and motivates additional algorithmic work in the area of pathwise optimization.

Reinforcement Learning and Optimization

Self-adapting network relaxations for weakly coupled Markov decision processes. (with A. Cire). [pdf; Under revision for second round review at Management Science]

Weakly coupled Markov decision processes (WDPs) arise in dynamic decision-making and reinforcement learning. These models are often high dimensional but decompose into smaller component MDPs when coupling constraints are relaxed. Lagrangian relaxations of WDPs that dualize linking constraints are commonly used to compute heuristic policies and (optimistic) bounds. While computationally appealing, this Lagrangian approach averages away combinatorial information embedded in the linking constraints. We present a class of network relaxations, dubbed feasibility network relaxations (FNRs), that embed an exact network encoding of the linking constraints into a linear programming flow model. We develop a procedure to obtain the minimally sized such relaxation, which we refer to as self-adapting FNR, as its size automatically adjusts to the structure of the linking constraints. We analyze this model to inform the approximation of WDPs: (i) self-adapting FNR provides (weakly) stronger bounds than standard Lagrangian relaxation and is polynomially sized for instances where the linking constraints admit a tractable network representation; and (ii) self-adapting FNR provides bounds and policies that match the approximate linear programming (ALP) approach but is substantially smaller in size, even polynomially sized when existing ALP formulations are exponentially large. We perform numerical experiments on constrained dynamic assortment and preemptive maintenance applications. Our results show that self-adapting FNR significantly improves on standard Lagrangian relaxation in terms of policy performance and/or bounds, although requiring more time, while being an order of magnitude faster than models based on alternative Lagrangian approaches and ALP, which are unsolvable in several instances.
Self-adapting robustness in demand learning. (with B. Chen, P. Pakiman, S. Jasin). [pdf; Under revision for resubmission to Operations Research]

We study dynamic pricing over a finite number of periods in the presence of demand model ambiguity. Departing from the typical no-regret learning environment, where price changes are allowed at any time, pricing decisions are made at pre-specified points in time and each price can be applied to a large number of arrivals. In this environment, which arises in retailing, a pricing decision based on an incorrect demand model can significantly impact cumulative revenue. We develop an adaptively-robust-learning (ARL) pricing policy that learns the true model parameters from the data while actively managing demand model ambiguity. It optimizes an objective that is robust with respect to a self-adapting set of demand models, where a given model is included in this set only if the sales data revealed from prior pricing decisions makes it ``probable''. As a result, it gracefully transitions from being robust when demand model ambiguity is high to minimizing regret when this ambiguity diminishes upon receiving more data. We characterize the stochastic behavior of ARL's self-adapting ambiguity sets and derive a regret bound that highlights the link between the scale of revenue loss and the customer arrival pattern. We also show that ARL, by being conscious of both model ambiguity and revenue, bridges the gap between a distributionally robust policy and a follow-the-leader policy, which focus on model ambiguity and revenue, respectively. We numerically find that the ARL policy, or its extension thereof, exhibits superior performance compared to distributionally robust, follow-the-leader, and upper-confidence-bound policies in terms of expected revenue and/or value at risk.
Self-guided approximate linear programs. (with P. Pakiman, N. Soheili, Q. Lin). [pdf; Under revision for third round review at Management Science]

Approximate linear programs (ALPs) are well-known models based on value function approximations (VFAs) to obtain heuristic policies and lower bounds on the optimal policy cost of Markov decision processes (MDPs). The ALP VFA is a linear combination of predefined basis functions that are chosen using domain knowledge and updated heuristically if the ALP optimality gap is large. We side-step the need for such basis function engineering in ALP -- an implementation bottleneck -- by proposing a sequence of ALPs that embed increasing numbers of random basis functions obtained via inexpensive sampling. We provide a sampling guarantee and show that the VFAs from this sequence of models converge to the exact value function. Nevertheless, the performance of the ALP policy can fluctuate significantly as more basis functions are sampled. To mitigate these fluctuations, we ``self-guide'' our convergent sequence of ALPs using past VFA information such that a worst-case measure of policy performance is improved. We perform numerical experiments on perishable inventory control and generalized joint replenishment applications, which, respectively, give rise to challenging discounted-cost MDPs and average-cost semi-MDPs. We find that self-guided ALPs (i) significantly reduce policy cost fluctuations and improve the optimality gaps from an ALP approach that employs basis functions tailored to the former application, and (ii) deliver optimality gaps that are comparable to a known adaptive basis function generation approach targeting the latter application. More broadly, our methodology provides application-agnostic policies and lower bounds to benchmark approaches that exploit application structure.

Accepted/Published Journal Papers

Energy and the Environment

Data-driven storage operations: Cross-commodity backtest and structured policies. (with C. Mandl, S. Minner, N. Gavirneni). [pdf; Forthcoming at Production and Operations Management]

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 profits as part of the training objective, which typically includes past profits 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.
Meeting corporate renewable power targets. (with A. Trivella, D. Mohseni-Taheri). [pdf; video; Received the 2021 Commodity and Energy Markets Association Best Paper Award and the 2020 INFORMS ENRE Young Researcher Prize; Forthcoming at Management Science]

Prominent companies have committed to procuring a percentage of their power demand from renewable sources by a future date in the face of uncertain power demand and stochastic power and renewable energy certificate (REC) prices. We study procurement portfolios based on two dominant strategies to achieve this target: long-term procurement of power and RECs at a fixed price using corporate power purchase agreements (CPPAs) and short-term purchases at volatile prices. We analyze a two-stage model to understand the behavior of procurement costs when using financial and physical CPPA variants employed in practice, which informs the structuring of these contracts. We subsequently formulate a Markov decision process (MDP) that optimizes the multi-stage procurement of power to reach and sustain a renewable procurement target. Our MDP is intractable because its action space is non-convex and its state space has high-dimensional endogenous and exogenous components. Although approximate methods to solve this MDP are limited, a procurement policy can be obtained using an easy-to-implement ``primal'' reoptimization strategy, which solves a deterministic model with stochastic quantities in the MDP replaced by forecasts. This approach does not, however, provide a lower bound on the optimal policy value. We propose a novel ``dual'' reoptimization heuristic which computes both procurement decisions and a lower bound while retaining the desirable implementation properties of primal reoptimization. On realistic instances, the dual reoptimization policy is near-optimal and outperforms policies from primal reoptimization and other heuristics. Our numerical results also highlight the benefits of using CPPA contracts to meet a renewable target.
Socially responsible merchant operations: Comparison of shutdown-averse CVaR and anticipated regret policies. (with A. Trivella). Operations Research Letters, 49(4), 2021. [pdf]

Commodity and energy production assets are managed as real options on market uncertainties. Social impacts of plant shutdowns incentivize balancing asset value with shutdown probability. We propose new shutdown-averse policies based on the popular dynamic conditional value-at-risk (CVaR). We analytically and numerically compare these policies to known shutdown-averse policies based on anticipated regret (AR). Our findings support the use of AR over CVaR to embed shutdown-aversion and the consideration of hybrid policies that are partially time-consistent but easily interpretable.
Managing shutdown decisions in merchant commodity and energy production: A social commerce perspective (with A. Trivella, S.E. Fleten, D. Mazieres, D. Pisinger). Manufacturing and Service Operations Management, 23 (2), 2021. [pdf]

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.
Merchant energy trading in a network (with N. Secomandi). Operations Research, 66(5), 2018. [pdf]

We formulate the merchant trading of energy in a network of storage and transport assets as a Markov decision process with uncertain energy prices, generalizing known models. Because of the intractability of our model, we develop heuristics and both lower and dual (upper) bounds on the optimal policy value estimated within Monte Carlo simulation. We achieve tractability using linear optimization, extending near optimal approximate dynamic programming techniques for the case of a single storage asset, versions of two of which are commercially available. We propose (i) a generalization of a deterministic reoptimization heuristic, (ii) an iterative version of the least squares Monte Carlo approach, and (iii) a perfect information dual bound. We apply our methods to a set of realistic natural gas instances. The combination of our reoptimization heuristic and dual bound emerges as a practical approach to nearly optimally solve our model. Our iterative least squares Monte Carlo heuristic is also close to optimal. Compared to our other heuristic, it exhibits slightly larger optimality gaps and requires some tuning, but is faster to execute in some cases. Our methods could enhance single energy storage asset software and have potential relevance beyond our specific application.
Comparison of least squares Monte Carlo methods with applications to energy real options (with F. Margot and N. Secomandi). European Journal of Operational Research, 256(1), 2017. [pdf]

Least squares Monte Carlo (LSM) is a state-of-the-art approximate dynamic programming approach used in financial engineering and real options to value and manage options with early or multiple exercise opportunities. It is also applicable to capacity investment and inventory/production management problems with demand/supply forecast updates arising in operations and hydropower-reservoir management. LSM has two variants, referred to as regress-now/later (LSMN/L), which compute continuation/value function approximations (C/VFAs). We provide novel numerical evidence for the relative performance of these methods applied to energy swing and storage options, two typical real options, using a common price evolution model. LSMN/L estimate C/VFAs that yield equally accurate (near optimal) and precise lower and dual (upper) bounds on the value of these real options. Estimating the LSMN/L C/VFAs and their associated lower bounds takes similar computational effort. In contrast, the estimation of a dual bound using the LSML VFA instead of the LSMN CFA takes seconds rather than minutes or hours. This finding suggests the use of LSML in lieu of LSMN when estimating dual bounds on the value of early or multiple exercise options, as well as of related capacity investment and inventory/production policies.
Relaxations of approximate linear programs for the real option management of commodity storage (with F. Margot and N. Secomandi). Management Science, 61(12), 2015. [pdf]

The real option management of commodity conversion assets gives rise to intractable Markov decision processes (MDPs), in part due to the use of high dimensional models of commodity forward curve evolution, as commonly done in practice. Focusing on commodity storage, we identify a deficiency of approximate linear programming, which we address by developing a novel approach to derive relaxations of approximate linear programs (ALPs). We apply our approach to obtain a class of tractable ALP relaxations, also subsuming an existing method. We provide theoretical support for the use of these ALP relaxations rather than their corresponding ALPs. Applied to existing natural gas storage instances, our ALP relaxations significantly outperform their corresponding ALPs. Our best ALP relaxation is both near optimal and competitive with, albeit slower than, state-of-the-art methods for computing heuristic policies and lower bounds on the value of commodity storage, but is more directly applicable for dual upper bound estimation than these methods. Our approach is potentially relevant for the approximate solution of MDPs that arise in the real option management of other commodity conversion assets, as well as the valuation of real and financial options that depend on forward curve dynamics.

Reinforcement Learning and Optimization

A data efficient and feasible level set method for stochastic convex optimization with expectation constraints. (with Q. Lin, N. Soheili, T. Yang). Journal of Machine Learning Research, 21(143), 2020. [pdf]

Stochastic convex optimization problems with expectation constraints (SOECs) are encountered in statistics and machine learning, business, and engineering. In data-rich environments, the SOEC objective and constraints contain expectations defined with respect to large datasets. Therefore, efficient algorithms for solving such SOECs need to limit the fraction of data points that they use, which we refer to as algorithmic data complexity. Recent stochastic first order methods exhibit low data complexity when handling SOECs but guarantee near-feasibility and near-optimality only at convergence. These methods may thus return highly infeasible solutions when heuristically terminated, as is often the case, due to theoretical convergence criteria being highly conservative. This issue limits the use of first order methods in several applications where the SOEC constraints encode implementation requirements. We design a stochastic feasible level set method (SFLS) for SOECs that has low data complexity and emphasizes feasibility before convergence. Specifically, our level-set method solves a root-finding problem by calling a novel first order oracle that computes a stochastic upper bound on the level-set function by extending mirror descent and online validation techniques. We establish that SFLS maintains a high-probability feasible solution at each root-finding iteration and exhibits favorable iteration complexity compared to state-of-the-art deterministic feasible level set and stochastic subgradient methods. Numerical experiments on three diverse applications validate the low data complexity of SFLS relative to the former approach and highlight how SFLS finds feasible solutions with small optimality gaps significantly faster than the latter method.
Network-based approximate linear programming for discrete optimization (with A. Cire). Operations Research, 68(6), 2020. [pdf]

We present relaxations for discrete optimization problems using approximate linear programs (ALPs) defined on multiple networks that represent different state-space aggregations. Our network ALP leverages information across these networks using a piecewise-constant value function approximation and its optimistic bound is theoretically guaranteed to weakly improve upon the bounds from the individual networks used in its construction. Solving network ALPs is challenging due to its large number of constraints -- a well-known issue when applying approximate linear programming. We side-step this issue by using a clique-based graph representation to design a network ALP restriction that admits a polynomial-time solvable extended formulation, which we show to also deliver a weakly better bound than individual networks. We execute a computational study on a challenging bilinear problem arising in marketing analytics and a routing application encountered in the preemptive maintenance of energy or city-owned assets. When used within a branch-and-bound scheme, the network ALP restriction significantly outperforms in both solution quality and time the following benchmarks: a state-of-the-art mathematical programming solver, a generic aggregation/dissaggregation scheme applied to a single network, and a heuristic that chooses the best bound among individual networks.
Partial hyperplane activation for generalized intersection cuts (with A. Kazachkov, E. Balas, and F. Margot). Mathematical Programming Computation, 12, 2020. [pdf; Received the Tepper Egon Balas PhD student paper award]

The generalized intersection cut (GIC) paradigm is a recent framework for generating cutting planes in mixed integer programming with attractive theoretical properties. We investigate this computationally unexplored paradigm and observe that a key hyperplane activation procedure embedded in it is not computationally viable. To overcome this issue, we develop a novel replacement to this procedure called partial hyperplane activation (PHA), introduce a variant of PHA based on a notion of hyperplane tilting, and prove the validity of both algorithms. We propose several implementation strategies and parameter choices for our PHA algorithms and provide supporting theoretical results. We computationally evaluate these ideas in the COIN-OR framework on MIPLIB instances. Our findings shed light on the the strengths of the PHA approach as well as identify properties related to strong cuts that can be targeted in the future.
Revisiting approximate linear programming: Constraint-violation learning with applications to inventory control and energy storage (with Q. Lin and N. Soheili). Management Science, 66(4), 2020. [pdf]

Approximate linear programs (ALPs) are well-known models for computing value function approximations (VFAs) of intractable Markov decision processes (MDPs). VFAs from ALPs have desirable theoretical properties, define an operating policy, and provide a lower bound on the optimal policy cost. However, solving ALPs near-optimally remains challenging, for example, when approximating MDPs with nonlinear cost functions and transition dynamics or when rich basis functions are required to obtain a good VFA. We address this tension between theory and solvability by proposing a convex saddle-point reformulation of an ALP that includes as primal and dual variables, respectively, a vector of basis function weights and a constraint violation density function over the state-action space. To solve this reformulation, we develop a proximal stochastic mirror descent (PSMD) method that learns regions of high ALP constraint violation via its dual update. We establish that PSMD returns a near-optimal ALP solution and a lower bound on the optimal policy cost in a finite number of iterations with high probability. We numerically compare PSMD with several benchmarks on inventory control and energy storage applications. We find that the PSMD lower bound is tighter than a perfect information bound. In contrast, the constraint sampling approach to solve ALPs may not provide a lower bound and applying row generation to tackle ALPs is not computationally viable. PSMD policies outperform problem-specific heuristics and are comparable or better than the policies obtained using constraint sampling. Overall, our ALP reformulation and solution approach broadens the applicability of approximate linear programming.
A level-set method for convex optimization with a feasible solution path (with Q. Lin and N. Soheili). SIAM Journal on Optimization, 28(4), 2018. [pdf]

Large-scale constrained convex optimization problems arise in several application domains. First-order methods are good candidates to tackle such problems due to their low iteration complexity and memory requirement. The level-set framework extends the applicability of first-order methods to tackle problems with complicated convex objectives and constraint sets. Current methods based on this framework either rely on the solution of challenging subproblems or do not guarantee a feasible solution, especially if the procedure is terminated before convergence. We develop a level-set method that finds an $\epsilon$-relative optimal and feasible solution to a constrained convex optimization problem with a fairly general objective function and set of constraints, maintains a feasible solution at each iteration, and only relies on calls to first-order oracles. We establish the iteration complexity of our approach, also accounting for the smoothness and strong convexity of the objective function and constraints when these properties hold. The dependence of our complexity on $\epsilon$ is similar to the analogous dependence in the unconstrained setting, which is not known to be true for level-set methods in the literature. Nevertheless, ensuring feasibility is not free. The iteration complexity of our method depends on a condition number, while existing level-set methods that do not guarantee feasibility can avoid such dependence. We numerically validate the usefulness of ensuring a feasible solution path by comparing our approach with an existing level set method on a Neyman-Pearson classification problem.
Relationship between least squares Monte Carlo and approximate linear programming (with N. Secomandi). Operations Research Letters, 45(5), 2017. [pdf]

Least squares Monte Carlo (LSM) is commonly used to manage and value intractable early and multiple exercise financial and real options. Recent research in this area has started applying approximate linear programming (ALP) and its relaxations, which aim at addressing a possible ALP drawback. We show that LSM is itself an ALP relaxation that potentially corrects this ALP shortcoming. Our analysis consolidates two separate streams of research and suggests using LSM rather than ALP for the considered class of problems.
Less-Than-Truckload carrier collaboration problem: modeling framework and solution approach (with J. H. Bookbinder). Journal of Heuristics, 19(6), 2013. [pdf]

Less-Than-Truckload (LTL) carriers generally serve geographical regions that are more localized than the inter-city line-hauls served by truckload carriers. That localization can lead to urban freight transportation routes that overlap. If trucks are traveling with less than full loads, there typically exist opportunities for carriers to collaborate over such routes. We introduce a two stage framework for LTL carrier collaboration. Our first stage involves collaboration between multiple carriers at the entrance to the city and can be formulated as a vehicle routing problem with time windows (VRPTW). We employ guided local search for solving this VRPTW. The second stage involves collaboration between carriers at transshipment facilities while executing their routes identified in phase one. For solving the second stage problem, we develop novel local search heuristics, one of which leverages integer programming to efficiently explore the union of neighborhoods defined by new problem-specific move operators. Our computational results indicate that integrating integer programming with local search results in at least an order of magnitude speed up in the second stage problem. We also perform sensitivity analysis to assess the benefits from collaboration. Our results indicate that distance savings of 7–15 % can be achieved by collaborating at the entrance to the city. Carriers involved in intra-city collaboration can further save 3–15 % in total distance traveled, and also reduce their overall route times.

Conference Proceedings and Workshop Papers

Offline-online reinforcement learning for energy pricing in office demand response: Lowering energy and data costs (with D. Jang, L. Spangher, T. Srivistava, M. Khattar, U. Agwan, and C. Spanos). Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys '21), 2021. [pdf]
Our team is proposing to run a full-scale energy demand response experiment in an office building. Although this is an exciting endeavor which will provide value to the community, collecting training data for the reinforcement learning agent is costly and will be limited. In this work, we examine how offline training can be leveraged to minimize data costs (accelerate convergence) and program implementation costs. We present two approaches to doing so: pretraining our model to warm start the experiment with simulated tasks, and using a planning model trained to simulate the real world's rewards to the agent. We present results that demonstrate the utility of offline reinforcement learning to efficient price-setting in the energy demand response problem.
A machine learning approach to methane emmisions mitigation in the oil and gas industry (with Jiayang Wang, Jingfan Wang, and P. Ravikumar). Tackling climate change with machine learning workshop, NeurIPS 2020. [pdf; Selected for a Spot Light Talk and Overall Best Paper. Research covered by Fortune magazine in its Eye on A.I. newsletter.]
Reducing methane emissions from the oil and gas sector is a key component of climate policy in the United States. Methane leaks across the supply chain are stochastic and intermittent, with a small number of sites (‘super-emitters’) responsible for a majority of emissions. Thus, cost-effective emissions reduction critically relies on effectively identifying the super-emitters from thousands of well-sites and millions of miles of pipelines. Conventional approaches such as walking surveys using optical gas imaging technology are slow and time-consuming. In addition, several variables contribute to the formation of leaks such as infrastructure age, production, weather conditions, and maintenance practices. Here, we develop a machine learning algorithm to predict high-emitting sites that can be prioritized for follow-up repair. Such prioritization can significantly reduce the cost of surveys and increase emissions reductions compared to conventional approaches. Our results show that the algorithm using logistic regression performs the best out of several algorithms. The model achieved a 70% accuracy rate with a 57% recall and a 66% balanced accuracy rate. Compared to the conventional approach, the machine learning model reduced the time to achieve a 50% emissions mitigation target by 42%. Correspondingly, the mitigation cost reduced from $85/t CO2e to $49/t CO2e.
Self-guided approximate linear programs (with P. Pakiman, N. Soheili, and Q. Lin). Self-supervised learning workshop, NeurIPS 2020. [pdf]
Approximate linear programs (ALPs) are well-known models based on value function approximations (VFAs) to obtain heuristic policies and lower bounds on the optimal policy cost of Markov decision processes (MDPs). The ALP VFA is a linear combination of predefined basis functions that are chosen using domain knowledge and updated heuristically if the ALP optimality gap is large. We side-step the need for such basis function engineering in ALP – an implementation bottleneck – by proposing a sequence of ALPs that embed increasing numbers of random basis functions obtained via inexpensive sampling. We provide a sampling guarantee and show that the VFAs from this sequence of models converge to the exact value function. We also show under mild conditions that the ALP policy cost is near-optimal when the number of sampled random bases is sufficiently large. Nevertheless, the performance of the ALP policy can fluctuate significantly as more basis functions are sampled. To mitigate these fluctuations, we “self-guide” our convergent sequence of ALPs using past VFA information such that a worst-case measure of policy performance is improved. Moreover, our method provides application-agnostic policies and bounds to benchmark approaches that exploit application structure.
Least squares Monte Carlo and approximate linear programming: Error bounds and energy real option application (with N. Secomandi). Advances in supply chain finance and FinTech innovations, Foundations and Trends in Technology, Information and Operations Management, Now Publishers, 2020. [pdf]

Least squares Monte Carlo (LSM) is an approximate dynamic programming (ADP) technique commonly used for the valuation of high dimensional financial and real options, but has broader applicability. It is known that the regress-later version of this method is an approximate linear programming (ALP) relaxation that implicitly provides a potential solution to a familiar ALP deficiency. We provide numerical backing for the usefulness of this solution using a numerical study dealing with merchant ethanol production, an energy real option application, based on an ALP heuristic that we propose. When both methodologies are applicable, our research supports the use of regress-later LSM rather than this ALP technique to approximately solve intractable Markov decision processes. Our findings motivate additional research to obtain even better methods than the regress-later version of LSM.
Interpretable user models via decision-rule Gaussian processes. (with D. Mohseni-Taheri, T. Tulabandhula). Advances in Approximate Bayesian Inference workshop, NeurIPS 2019. [pdf]

Models of user behavior are critical inputs in many prescriptive settings and can be viewed as decision rules that transform state information available to the user into actions. Gaussian processes (GPs), as well as nonlinear extensions thereof, provide a flexible framework to learn user models in conjunction with approximate Bayesian inference. However, the resulting models may not be interpretable in general. We propose decision-rule GPs (DRGPs) that apply GPs in a transformed space defined by decision rules that have immediate interpretability to practitioners. We illustrate this modeling tool on a real application and show that structural variational inference techniques can be used with DRGPs. We find that DRGPs outperform the direct use of GPs in terms of both out-of-sample performance and the quality of optimized decisions. These performance advantages continue to hold when DRGPs are combined with transfer learning.
Robust demand learning. (with B. Chen, S. Jasin). Workshop on Safety and Robustness in Decision Making, NuerIPS 2019.

We consider a common revenue management problem where a seller needs to determine the pricing decisions for a product with fixed initial inventory over T time periods. Some parameters of the demand distribution (as a function of price) are not known, and the firm needs to learn them from historical sales data while simultaneously trying to maximize expected total revenue. Departing from the typical no-regret learning environment, where T can be scaled to infinity, the number of time periods is pre-specified and fixed in our setting. We propose a new robust demand learning (RDL) framework that combines robust Markov decision processes (MDPs) and no-regret learning such that the seller is “hedged” during initial periods of the selling horizon when sales may be low (i.e., high model ambiguity), while simultaneously leveraging learning to minimize regret when sufficient sales data is available. RDL relies on the construction of dynamic uncertainty sets that are consistent with the goal of regret minimization. We provide theory showing that the RDL pricing policy has the same regret guarantee as pure learning but has lower risk exposure to model ambiguity. In addition, RDL is less conservative than a pure robust MDP approach based on static uncertainty sets.
SMOILE: Shopper marketing optimization and inverse learning engine. (with A. Chenreddy, P. Pakiman, R. Chandrasekaran, R. Abens). Proceedings of the 25th ACM SIGKDD conference on knowledge discovery and data mining, Anchorage, Alaska, 2019. (accepted for oral presentation; acceptance rate 6.4%) [pdf]

Product brands employ shopper marketing (SM) strategies to convert shoppers along the path to purchase. Traditional marketing mix models (MMMs), which leverage regression techniques and historical data, can be used to predict the component of sales lift due to SM tactics. The resulting predictive model is a critical input to plan future SM strategies. The implementation of traditional MMMs, however, requires significant ad-hoc manual intervention due to their limited flexibility in (i) explicitly capturing the temporal link between decisions; (ii) accounting for the interaction between business rules and past (sales and decision) data during the attribution of lift to SM; and (iii) ensuring that future decisions adhere to business rules. These issues necessitate MMMs with tailored structures for specific products and retailers, each requiring significant hand-engineering to achieve satisfactory performance -- a major implementation challenge.

We propose an SM Optimization and Inverse Learning Engine (SMOILE) that combines optimization and inverse reinforcement learning to streamline implementation. SMOILE learns a model of lift by viewing SM tactic choice as a sequential process, leverages inverse reinforcement learning to explicitly couple sales and decision data, and employs an optimization approach to handle a wide-array of business rules. Using a unique dataset containing sales and SM spend information across retailers and products, we illustrate how SMOILE standardizes the use of data to prescribe future SM decisions. We also track an industry benchmark to showcase the importance of encoding SM lift and decision structures to mitigate spurious results when uncovering the impact of SM decisions.
A three-echelon integrated production-distribution system (with J. H. Bookbinder). International Conference on Decision Sciences and Technology for Globalization, Decision Sciences Institute, Ghaziabad, India. 2008.
Enhancing transportation efficiencies through carrier collaboration (with J. H. Bookbinder). BPC World Conference , Mumbai, India. 2007.
Non-destructive evaluation by low pulsing acoustic tap technique: Spectral relationships (with T.S. Niranjan and A.V.Varun). Flight 2006, National Aerospace Symposium, Chennai, India. 2006.

Book Chapters

Real option management of hydrocarbon cracking operations (with N. Secomandi, G. Sowers, and J. Wassick). Real Options in Energy and Commodity Markets, Now Publishers, 2017. [pdf]

Commodity conversion assets play important economic roles. It is well known that the market value of these assets can be maximized by managing them as real options on the prices of their inputs and/or outputs. In particular, when futures on these inputs and outputs are traded, managing such real options, that is, valuing, hedging, and exercising them, is analogous to managing options on such futures, using risk neutral valuation and delta hedging methods. This statement holds because dynamically trading portfolios of these futures and a risk less bond can replicate the cash flows of these assets. This basic principle is not always appreciated by managers of commodity conversion assets. Moreover, determining the optimal operational cash flows of such an asset requires optimizing the asset operating policy. This issue complicates the real option management of commodity conversion assets. This chapter illustrates the application of this approach to manage a hydrocarbon cracker, a specific commodity conversion asset, using linear programming and Monte Carlo simulation. The discussion is based on a simplified representation of the operations of this asset. However, the material presented here has potential applicability to the real option management of more realistic models of hydrocarbon cracking assets, as well as other energy and commodity conversion assets.

Technical Reports

Least squares Monte Carlo and approximate linear programming: Error bounds and energy real option application (with N. Secomandi). [pdf; extended version of the conference proceedings with the same title.

Least squares Monte Carlo (LSM) is an approximate dynamic programming (ADP) technique commonly used for the valuation of high dimensional financial and real options, but has broader applicability. It is known that the regress-later version of this method is an approximate linear programming (ALP) relaxation that implicitly provides a potential solution to a familiar ALP deficiency. Focusing on a generic finite horizon Markov decision process, we provide both theoretical and numerical backing for the usefulness of this solution, respectively using a worst-case error bound analysis and a numerical study dealing with merchant ethanol production, an energy real option application, based on an ALP heuristic that we propose. When both methodologies are applicable, our research supports the use of regress-later LSM rather than this ALP technique to approximately solve intractable Markov decision processes. Our numerical findings motivate additional research to obtain even better methods than the regress-later version of LSM.
Dynamic pricing for hotel rooms when customers request multiple-day stays (with Y. F. Lim, Q. Ding). [pdf]

Prominent hotel chains quote a booking price for a particular type of rooms on each day and dynamically update these prices over time. We present a novel Markov decision process (MDP) formulation that determines the optimal booking price for a single type of rooms under this strategy, while considering the availability of rooms throughout the multiple-day stays requested by customers. We analyze special cases of our MDP to highlight the importance of modeling multiple-day stays and provide guidelines to potentially simplify the implementation of pricing policies around peak-demand events such as public holidays and conferences. Since computing an optimal policy to our MDP is intractable in general, we develop heuristics based on a fluid approximation and approximate linear programming (ALP). We numerically benchmark our heuristics against a single-day decomposition approach (SDD) and an adaptation of a fixed-price heuristic. The ALP-based heuristic (i) outperforms the other methods; (ii) generates up to 7% and 6% more revenue than the SDD and the fixed-price heuristic respectively; and (iii) incurs a revenue loss of only less than 1% when using our pricing structure around peak-demand events, which supports the use of this simple pricing profile. Our findings are potentially relevant beyond the hotel domain for applications involving the dynamic pricing of capacitated resources.