# Sarah M. RyanIowa State University | ISU · Department of Industrial and Manufacturing Systems Engineering

Sarah M. Ryan

PhD, IOE, The University of MIchigan

## About

117

Publications

18,397

Reads

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2,693

Citations

Citations since 2017

Introduction

**Skills and Expertise**

Additional affiliations

August 1999 - present

## Publications

Publications (117)

Farm management decisions under uncertainty are important, not only for farmers trying to maximize their net income, but also for policy makers responsible for incentives and regulations to achieve environmental goals. We focus on corn production as a significant contributor to the economy of the US Midwest. Nitrogen is one of the key nutrients nee...

To examine the familiar tradeoff between risk and return in financial investments, we use a rolling two-stage stochastic program to compare mean-risk optimization models with time series momentum strategies. In a backtest of allocating investment between a market index and a risk-free asset, we generate scenarios of future return according to a mom...

Electricity generation increasingly relies on natural gas for fuel. The competing demands for gas by other users who may have higher priority, the lack of coordination between gas and electricity markets, and extreme weather events all pose risks to systems with high dependence on gas. When the gas supply on which generators have planned is limited...

Stochastic programming models for portfolio optimization rely on scenario paths for returns derived from stochastic process models. This paper investigates a variant of the geometric Brownian motion process for stock index returns that incorporates index momentum. Based on this model, three different processes for generating scenarios on a rolling...

Optimization models for making decisions over time in uncertain environments rely on probabilistic inputs, such as scenario trees for stochastic mathematical programs. The quality of model outputs, i.e., the solutions obtained, depends on the quality of these inputs. However, solution quality is rarely assessed in a rigorous way. The connection bet...

A nutrient reduction strategy for Iowa identifies land use and conservation alternatives to reduce nutrient loss from agriculture and the resulting Gulf of Mexico hypoxia. From the viewpoint of a policy maker concerned with regional costs and benefits, we develop a land use optimization model to maximize profit while satisfying nutrient reduction c...

Unit commitment seeks the most cost effective generator commitment schedule for an electric power system to meet net load, defined as the difference between the load and the output of renewable generation, while satisfying the operational constraints on transmission system and generation resources. Stochastic programming and robust optimization are...

In minimization problems with uncertain parameters, cost savings can be achieved by solving stochastic programming (SP) formulations instead of using expected parameter values in a deterministic formulation. To obtain such savings, it is crucial to employ scenarios of high quality. An appealing way to assess the quality of scenarios produced by a g...

We compare approaches for addressing uncertainty in the joint scheduling of a combined power and gas system, with the goal of minimizing the total cost of meeting demands for gas and electricity, while satisfying operational and equilibrium constraints. A stochastic programming model and a deterministic model with reserves are formulated to investi...

Probabilistic wind power scenarios constitute a crucial input for stochastic day-ahead unit commitment in power systems with deep penetration of wind generation. To minimize the cost of implemented solutions, the scenario time series of wind power amounts available should accurately represent the stochastic process for available wind power as it is...

The adjustable robust counterpart (ARC) of an uncertain linear program extends the robust counterpart (RC) by allowing some decision variables to adjust to the realizations of some uncertain parameters. The ARC may produce a less conservative and costly solution than the RC does but cases are known in which it does not. While the literature documen...

This paper proposes a novel method to co-optimize distribution system operation and repair crew routing for outage restoration after extreme weather events. A two-stage stochastic mixed integer linear program is developed to dispatch distributed generators, reconfigure the distribution network, and efficiently dispatch the repair crews to damaged c...

This paper proposes a novel method to co-optimize distribution system operation and repair crew routing for outage restoration after extreme weather events. A two-stage stochastic mixed integer linear program is developed. The first stage is to dispatch the repair crews to the damaged components. The second stage is distribution system restoration...

We optimize the design of a closed-loop supply chain network that encompasses flows in both forward and reverse directions and is subject to uncertainty in demands for both new and returned products. The model also accommodates a carbon tax with tax rate uncertainty. The proposed model is a three-stage hybrid robust/stochastic program that combines...

Environmental, social and economic concerns motivate the operation of closed loop supply chain networks (CLSCN) in many industries. We propose a novel profit maximization model for CLSCN design as a mixed-integer linear program in which there is flexibility in covering the proportions of demand satisfied and returns collected based on the
firm's po...

We present a method for computing lower bounds in the progressive hedging algorithm (PHA) for two-stage and multi-stage stochastic mixed-integer programs. Computing lower bounds in the PHA allows one to assess the quality of the solutions generated by the algorithm contemporaneously. The lower bounds can be computed in any iteration of the algorith...

Short-term load forecasting is important for power system generation planning and operation. For unit commitment and dispatch processes to incorporate uncertainty, a short-term load model must not only provide accurate load predictions but also enable the generation of reasonable probabilistic scenarios or uncertainty sets. This paper proposes a te...

Environmental, social and economic concerns motivate the operation of closed-loop supply chain networks (CLSCN) in many industries. We propose a novel profit maximization model for CLSCN design as a mixed-integer linear program in which there is flexibility in covering the proportions of demand satisfied and returns collected based on the firm's po...

Environmental, social and economic concerns motivate the operation of closed-loop supply chain networks (CLSCN) in many industries. We propose a novel profit maximization model for CLSCN design as a mixed-integer linear program in which there is flexibility in covering the proportions of demand satisfied and returns collected based on the firm's po...

Unit commitment decisions made in the day-ahead market and during subsequent reliability assessments are critically based on forecasts of load. Traditional, deterministic unit commitment is based on point or expectation-based load forecasts. In contrast, stochastic unit commitment relies on multiple load scenarios, with associated probabilities, th...

In power systems with high penetration of wind generation, probabilistic scenarios are generated for use in stochastic formulations of day-ahead unit commitment problems. To minimize the expected cost, the wind power scenarios should accurately represent the stochastic process for available wind power. We employ some statistical evaluation metrics...

In this second portion of a two-part analysis of a scalable computational approach to stochastic unit commitment (SUC), we focus on solving stochastic mixed-integer programs in tractable run-times. Our solution technique is based on Rockafellar and Wets’ progressive hedging algorithm, a scenario-based decomposition strategy for solving stochastic p...

We present a method for integrating the Progressive Hedging (PH) algorithm and the Dual Decomposition (DD) algorithm of Carøe and Schultz for stochastic mixed-integer programs. Based on the correspondence between lower bounds obtained with PH and DD, a method to transform weights from PH to Lagrange multipliers in DD is found. Fast progress in earl...

A two-stage stochastic program is formulated for day-ahead commitment of thermal generating units to minimize total expected cost considering uncertainties in the day-ahead load and the availability of variable generation resources. Commitments of thermal units in the stochastic reliability unit commitment are viewed as first-stage decisions, and d...

We propose a stochastic generation expansion model, where we represent the long-term uncertainty in the availability and variability in the weekly wind pattern with multiple scenarios. Scenario reduction is conducted to select a representative set of scenarios for the long-term wind power uncertainty. We assume that the short-term wind forecast err...

This paper addresses a multi-period capacitated closed-loop supply chain (CLSC) network design problem subject to uncertainties in the demands and returns as well as the potential carbon emission regulations. Two promising regulatory policy settings are considered: namely, (a) a carbon cap and trade system, or (b) a tax on the amount of carbon emis...

We propose an integrated model of the asset management decisions for a fleet of identical product units and the inventory management decisions for a closed-loop supply chain in the context of a product–service system, in which the two types of decisions are closely coupled. A joint optimization technique is developed to obtain the parameters of the...

We study a tri-level integrated transmission and generation expansion planning problem in a deregulated power market environment. The collection of bi-level sub-problems in the lower two levels is an equilibrium problem with equilibrium constraints (EPEC) that can be approached by either the diagonalization method (DM) or a complementarity problem...

We present a stochastic programming model for investments in thermal generation capacity to study the impact of demand response (DR) at high wind penetration levels. The investment model combines continuous operational constraints and wind scenarios to represent the implications of wind variability and uncertainty at the operational level. DR is re...

The articles in this special section focus on the topic of optimization methods and algorithms applied to smart grid.

A challenging aspect of applying stochastic programming in a dynamic setting is to construct a set of discrete scenarios that well represents multivariate stochastic processes for uncertain parameters. Often this is done by generating a scenario tree using a statistical procedure and then reducing its size while maintaining its statistical properti...

Unit commitment decisions made in the day-ahead market and resource adequacy assessment processes are based on forecasts of load, which depends strongly on weather. Two major sources of uncertainty in the load forecast are the errors in the day-ahead weather forecast and the variability in temporal patterns of electricity demand that is not explain...

Given increasing penetration of variable generation units, there is significant interest in the power systems research community concerning the development of solution techniques that directly address the stochasticity of these sources in the unit commitment problem. Unfortunately, despite significant attention from the research community, stochast...

This article describes the fuel transportation and storage components of the supply chain for electricity. We focus on dispatchable generation based on transportable fuels. Coal has very flexible transportation and storage requirements. Natural gas requires pressurized pipelines and storage facilities; or it can be liquefied, then stored and transp...

Two-stage stochastic mixed-integer programming models are formulated for minimizing expected cost or Conditional Value-at-Risk (CVaR) of a long-term power generation expansion planning problem incorporating load duration curves. The multivariate stochastic processes, such as electricity demands and fuel prices, are modeled as geometric Brownian mot...

This paper describes the ways that students’ problem-solving behaviors evolve when solving multi-faceted, context-rich problems
within a web-based learning environment. During the semester, groups of two or three students worked on five physics problems
that required drawing on more than one concept and, hence, could not be readily solved with simp...

We formulate a generation expansion planning problem to determine the type and quantity of power plants to be constructed
over each year of an extended planning horizon, considering uncertainty regarding future demand and fuel prices. Our model
is expressed as a two-stage stochastic mixed-integer program, which we use to compute solutions independe...

Constraints in fuel supply, electricity generation, and transmission interact to affect the welfare of strategic generators and price-sensitive consumers. We consider a mixed integer bilevel programming model in which the leader makes capacity expansion decisions in the fuel transportation, generation, and transmission infrastructure of the electri...

The April 2011 DOE workshop, 'Computational Needs for the Next Generation Electric Grid', was the culmination of a year-long process to bring together some of the Nation's leading researchers and experts to identify computational challenges associated with the operation and planning of the electric power system. The attached papers provide a journe...

Motivated by the increasing use of condition moni- toring technology for electrical transformers, this paper deals with the optimal replacement of a system having a hazard function that follows the proportional hazards model with a semi-Markovian co- variate process, which we assume is under continuous monitoring. Although the optimality of a thres...

The welfare of electricity producers and consumers depends on congestion in the transmission grid, generation costs that consist mainly of fuel costs, and strategic behavior. We formulate a game theoretic model of an oligopolistic electricity market where generation costs are derived from a fuel supply network. The game consists of a fuel dispatche...

Fixed demands for electricity are incorporated into a game theoretic model of strategic generators who are supplied by a fuel transportation network and produce power for a congested electricity transportation network governed by an independent system operator. Some counter-intuitive effects on measures of market power result from reducing the prop...

This article investigats the value of perfect monitoring information for optimal replacement of deteriorating systems in the Proportional Hazards Model (PHM). A continuous-time Markov chain describes the condition of the system. Although the form of an optimal replacement policy for system under periodic monitoring in the PHM was developed previous...

To explore the effects of uncertain fuel costs on the bulk energy flows in the US, we introduce stochastic fuel costs in a
generalized network flow model of the integrated electric energy system, including coal, natural gas, and electricity generation.
The fuel costs are modeled as discretely distributed random variables. A rolling two-stage recour...

We consider a closed loop supply chain where new products are produced to order and re-turned products are refurbished for reselling. The solution to a price-setting problem enforces the "heavy traffic" condition, under which we address the production rate control problem under two types of cost functions. We solve a drift-control problem for an ap...

We formulate a mathematical equilibrium model of an oligopolistic electricity market where generation costs are derived from a fuel supply network. In numerical results for a small test system, the equilibrium electricity prices and quantities along with resulting welfare measures are compared against the results of a computational agent simulation...

For a service provider facing stochastic demand growth, expansion lead times and economies of scale complicate the expansion timing and sizing decisions. We formulate a model to minimize the infinite horizon expected discounted expansion cost under a service-level constraint. The service level is defined as the proportion of demand over an expansio...

In this paper, a systematic risk-based transmission line expansion approach is studied. The approach addresses three objectives: load-driven expansion, security-enhancement expansion and risk-based expansion. The first problem identifies transmission expansions necessary to correct inadequacy; the second problem identifies transmission expansion ne...

A provider of product-based services must maintain its products continuously in working order while they are dispersed among client firms. Because it retains ownership and control over the products, it can reuse and remanufacture them extensively. These practices motivate the use of condition monitoring to increase visibility of the productpsilas c...

To explore the effects of uncertain fuel costs on the optimal electric flows in U.S., we introduce stochastic fuel costs in a generalized network flow model of the integrated electric energy system, including coal, natural gas, and electricity generation. The fuel costs are modeled as discretely distributed random variables and a rolling two-stage...

Educators in engineering and science disciplines are well aware of student difficulties in formulating problems. Correct problem formulation is a critical phase in the problem solving process because the solution follows directly from the formulation. Students in this phase are engaged in reasoning and argumentation activities that result in suppor...

One of the most important decisions regarding reverse logistics ( RL) is whether to outsource such functions or not, due to the fact that RL does not represent a production or distribution firm's core activity. To explore the hypothesis that outsourcing RL functions is more suitable when returns are more variable, we formulate and analyse a Markov...

In this paper, a risk-based unit commitment (RBUC) approach for the day-ahead market is introduced. A risk-based economic dispatch, an independent part of the risk- based unit commitment, can be used for the real-time market. This provides for consideration of both economic benefit as well as reliability within a single integrated approach. A three...

Electric power transmission systems are comprised of a large number of physical assets, including transmission lines, power Electric power transmission systems are comprised of a large number of physical assets, including transmission lines, power
transformers, and circuit breakers, that are capital-intensive, highly distributed, and may fail. Mana...

This paper is the first of a two-part paper presenting a multiperiod generalized network flow model of the integrated energy system in the United States. Part I describes the modeling approach used to evaluate the economic efficiencies of the system-wide energy flows, from the coal and natural gas suppliers to the electric load centers. Under the p...

In this project, we show how citizen-based assessment and improvement efforts of e-government services can be implemented through a focused study of five small and medium-sized communities in three Midwestern states. This is a current project in progress, and the results obtained thus far as well as the near future plans are described. Further deta...

Investments in high-voltage transmission facilities require large financial commitments and significantly affect the future reliability and economy of the interconnected power system. Transmission expansion planning is complicated by the potential component unavailability due to forced or scheduled outage. Transformers comprise one important set of...

Engineering economic decision problems encountered in practice are embedded in information-rich environments, where large volumes of data are available from multiple sources. However, the information that is most relevant to solving the problem may be unavailable, inaccessible, inaccurate, or uncertain. In contrast, typical engineering economy text...

Many retail product returns can be refurbished and resold, typically at a reduced price. The price set for the refurbished products affects the demands for both new and refurbished products, while the refurbishment and resale activities incur costs. To maximize profit, a manufacturer in a competitive market must carefully choose the proportion of r...

The total replacement value of the US transmission lines alone (excluding land) is conservatively estimated at over $100 billion dollars (1) and triples when including transformers and circuit breakers. Investment in new transmission equipment has significantly declined over the past 15 years. Some of the equipment is well beyond intended life, yet...

http://deepblue.lib.umich.edu/bitstream/2027.42/7383/5/bam7748.0001.001.pdf http://deepblue.lib.umich.edu/bitstream/2027.42/7383/4/bam7748.0001.001.txt

Strategic decisions about reverse logistics (RL) are complicated by the uncertainty of product returns. To aid firms in deciding whether to outsource RL activities, a characterization of RL networks according to two critical factors is proposed. These factors are the length of the product life cycle, which affects variability of expected returns ov...

For a service provider, stochastic demand growth along with expansion lead times and economies of scale may encourage delaying the start of expansion until after some shortages have been accumulated. Assuming demand follows a geometric Brownian motion, we define the service level in terms of the proportion of demand satisfied, which is then analyti...

The geometric Brownian motion (GBM) process is frequently invoked as a model for such diverse quantities as stock prices, natural resource prices and the growth in demand for products or services. We discuss a process for checking whether a given time series follows the GBM process. Methods to remove seasonal variation from such a time series are a...

To operate a multiple product manufacturing system under a CONWIP control policy, one must decide how to assign kanbans to products. With a fixed total number of kanbans in a competitive environment, the goal is to determine their allocation to product types in order to minimize lost sales equitably. In particular, we consider systems in which the...

For service providers, uncertain demand for capacity and expansion lead time may create unavoidable capacity shortages, which may be allowed to accumulate before initiating an expansion. For the demand following a geometric Brownian motion process, we assume a stationary expansion policy where the timing and size of expansion are determined as fixe...

Problem solving is a major focus of the engineering profession, and upon graduation new engineers are faced with increasingly complex problems. Yet, existing engineering education practices often fall short in preparing students to tackle complex engineering problems that may be ambiguous, open-ended and ill-structured. In this paper, we describe a...

The combination of demand uncertainty and a lead time for adding capacity creates the risk of capacity shortage during the lead time. We formulate a model of capacity expansion for uncertain exponential demand growth and deterministic expansion lead times when there is an obligation to provide a specified level of service. The service level, define...

In an effort to improve students' problem solving skills with information technology across the industrial engineering curriculum, we created an Internet based problem-solving environment. The module implemented for engineering economy presents a realistic problem that establishes connections with other courses. The design of the learning environme...