
(click on paper title to view abstract)
Accepted/Published Papers
 Merchant energy trading in a network
(with N. Secomandi). Operations Research, Forthcoming, 2017. [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.
 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.
 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 stateoftheart 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 hydropowerreservoir management. LSM has two variants, referred to as regressnow/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, stateoftheart 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.
 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.
 LessThanTruckload carrier collaboration problem: modeling framework and solution approach (with J. H. Bookbinder). Journal of Heuristics, 19(6),
2013. [pdf]
LessThanTruckload (LTL) carriers generally serve geographical regions that are more localized than the intercity
linehauls 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 problemspecific 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 intracity collaboration can further save 3–15 % in total
distance traveled, and also reduce their overall route times.
Submitted Papers
 Managing shutdown decisions in merchant commodity and energy production: A social commerce perspective
(with A. Trivella, S.E. Fleten, D. Mazieres, D. Pisinger). [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 hardtoassess 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 highdimensional 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
anticipatedregret policy. We show that anticipatedregret policies possess desirable asymptotic properties absent
in classification and reoptimizationbased policies. On instances created using real data, anticipatedregret
and classificationbased policies outperform practicebased 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.
 Revisiting approximate linear programming using a saddle point approach
(with Q. Lin and N. Soheili). [pdf]
Approximate linear programs (ALPs) are wellknown models for computing value function approximations (VFAs)
of intractable Markov decision processes (MDPs) arising in applications. VFAs from ALPs have desirable theoretical
properties, define an operating policy, and provide a lower bound on the optimal policy cost, which can be used
to assess the suboptimality of heuristic policies. However, solving ALPs nearoptimally 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
saddlepoint 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 stateaction space. To solve this reformulation,
we develop a proximal stochastic mirror descent (PSMD) method. We establish that PSMD returns a nearoptimal 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 problemspecific 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.
 Networkbased approximate linear programming for discrete optimization
(with A. Cire). [pdf]
We develop a new class of approximate linear programs (ALPs) that project the highdimensional value function
of dynamic programs onto a class of basis functions, each deﬁned as a network that represents aggregations over
the state space. The resulting ALP is a minimumcost ﬂow problem over an extended variable space that synchronizes
ﬂows across multiple networks. Its solution provides a value function approximation that can be used to obtain
feasible solutions and optimistic bounds. Such bounds from multiple networks are weakly stronger than their counterparts
from a single network. We present a scheme for iteratively constructing a ﬁnite sequence of network ALPs with improving
optimistic bounds that converge to the optimal solution value of the original problem. In addition, we provide
a tractable approximation of a network ALP based on the chordalization of an auxiliary graph. We assess the performance
of the ALP bounds and feasible solutions using a branchandbound scheme to obtain optimal solutions. We apply
this scheme to challenging bilinear and routing problems arising in marketing analytics and preemptive maintenance,
respectively. Numerical results show that the network ALP signiﬁcantly outperforms a stateoftheart mathematical
programming solver both in solution quality and time.
 A levelset method for convex optimization with a feasible solution path
(with Q. Lin and N. Soheili). [pdf]
Largescale constrained convex optimization problems arise in several application domains. Firstorder methods
are good candidates to tackle such problems due to their low iteration complexity and memory requirement. The levelset
framework extends the applicability of firstorder 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 levelset 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 firstorder 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 levelset methods in the literature. Nevertheless, ensuring feasibility
is not free. The iteration complexity of our method depends on a condition number, while existing levelset 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 NeymanPearson
classification problem.
 Dynamic pricing
for hotel rooms when customers request multipleday stays
(with Y. F. Lim and Q. Ding). [pdf]
Some prominent hotel chains quote a booking price of a particular type of rooms on each day and dynamically
update these prices over time. We present a novel Markov decision process formulation that determines the optimal
booking price for a single type of rooms under this strategy, while considering the availability of rooms throughout
the multipleday stays requested by customers. We unveil the importance of modeling multipleday stays by comparing
the optimal policy with a singleday decomposition approach (SDD). Our analysis of the optimal policy around peakdemand
events (such as public holidays or conferences) suggests that hotels should substantially raise the booking prices
for some highdemand days, and simultaneously, significantly lower the booking prices for the lowdemand days that
are immediately adjacent to these highdemand days. This finding may potentially simplify the implementation of
pricing policies. Since computing an optimal policy is intractable, we develop heuristics based on a fluid approximation
and approximate linear programming (ALP). We numerically benchmark them against an SDD and an adaptation of a fixedprice
heuristic. The ALPbased heuristic (i) outperforms the other methods; (ii) generates up to 7% and 6% more revenue
than the SDD and fixedprice heuristic respectively; and (iii) incurs a revenue loss of only less than 1% when
using our pricing structure around peakdemand 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 used by multiple products.
 Partial hyperplane activation for generalized intersection cuts
(with A. Kazachkov, E. Balas, and F. Margot). [pdf]
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 COINOR 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.
Conference Proceedings
 A threeechelon integrated productiondistribution 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.
 Nondestructive 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.
