"Robust-stochastic Models for Profit Maximizing Hub Location Problems"
Doctoral Candidate Mojtaba Hosseini
Co-author(s): Gita Taherkhani and Sibel A. Alumur
Accepted at: Transportation Science
This paper introduces robust-stochastic models for profit maximizing capacitated hub location problems in which two different types of uncertainty including stochastic demand and uncertain revenue are simultaneously incorporated into the problem. First, a two-stage stochastic program is presented where demand and revenue are jointly stochastic. Next, robust-stochastic models are developed to better model uncertainty in the revenue while keeping the demand stochastic. Two cases are studied based on the dependency between demand and revenue. In the first case, a robust-stochastic model with a min-max regret objective is developed assuming a finite set of scenarios that describe uncertainty associated with the revenue under a revenue-elastic demand setting. For the case when demand and revenue are independent, robust-stochastic models with a max-min criterion and a min-max regret objective are formulated considering both interval uncertainty and discrete scenarios, respectively. It is proved that the robust-stochastic version with max-min criterion can be viewed as a special case of the min-max regret stochastic model. Exact algorithms based on Benders decomposition coupled with sample average approximation scheme are proposed. Exploiting the repetitive nature of sample average approximation, generic acceleration methodologies are developed to enhance the performance of the algorithms enabling them to solve large-scale intractable instances. Extensive computational experiments are performed to consider the efficiency of the proposed algorithms and also to analyze the effects of uncertainty under different settings. The qualities of the solutions obtained from different modeling approaches are compared under various parameter settings. Computational results justify the need to solve robust-stochastic models to embed uncertainty in decision making to design resilient hub networks.
"Optimal Subsidy Schemes and Budget Allocations for Government-Subsidized Trade-in Programs"
Professor(s): Luyi Gui
Co-author(s): Rick So (Professor Emeritus), Jiaru Bai (Merage PhD ’17), Shu Hu and Zujun Ma
Accepted at: Production and Operations Management (Journal on Financial Times Top 50 list)
Applications of government subsidies to speed up consumer trade-ins of used products can be commonly observed in practice. This paper studies the design of such trade-in subsidy programs and aims to provide implementable insights for practice. In particular, we focus on two open problems in the literature, (i) how to optimally allocate the subsidy budget among the multiple products covered by the trade-in program, and (ii) how to most effectively utilize the assigned budget to incentivize consumer trade-ins for each product. We develop a three-stage Stackelberg game model that captures the essence of the interaction between the government's subsidy decision, the manufacturer's trade-in rebate decision, and the consumer's product replacement decision. We show that a sharing subsidy scheme under which the government subsidy is proportional to the manufacturer's rebate is more effective in encouraging consumer trade-ins than fixed-amount subsidies. Moreover, a product with a higher environmental impact, a larger market size, a longer lifespan, or a lower value to consumers typically demands a larger subsidy budget allocation. We further use our results to derive a simple proportional budget allocation rule that can provide robust and near-optimal performance. We illustrate our results by a case study based on the “old-for-new” program in China that subsidizes home appliance trade-ins. Our results indicate that policy makers should pay attention to the correlation between government subsidies and manufacturer's rebate as well as key product and market characteristics when designing a subsidy scheme for trade-in programs.
"Valuing Sequences of Lives Lost or Saved Over Time: Preference for Uniform Sequences"
Professor(s): L. Robin Keller
Co-author(s): Jeffery Guyse (Merage PhD ’00) and Candice Huynh (Merage PhD ’14)
Accepted at: Decision Analysis
Policymakers often make decisions involving human-mortality risks and monetary outcomes that span across different time periods and horizons. Many projects or environmental-regulation policies involving risks to life, such as toxic exposures, are experienced over time. The preferences of individuals on lives lost or saved over time should be understood to implement effective policies. Using a within-subject survey design, we investigated our participants’ elicited preferences (in the form of ratings) for sequences of lives saved or lost over time at the participant level. The design of our study allowed us to directly observe the possible preference patterns of negative time discounting or a preference for spreading from the responses. Additionally, we embedded factors associated with three other prevalent anomalies of intertemporal choice (gain/loss asymmetry, short/long asymmetry, and the absolute magnitude effect) into our study for control. We find that our participants exhibit three of the anomalies: preference for spreading, absolute magnitude effect, and short/long-term asymmetry. Furthermore, fitting the data collected, Loewenstein and Prelec’s model for the valuation of sequences of outcomes allowed for a more thorough understanding of the factors influencing the individual participants’ preferences. Based on the results, the standard discounting model does not accurately reflect the value that some people place on sequences of mortality outcomes. Preferences for uniform sequences should be considered in policymaking rather than applying the standard discounting model.
"Communicating Health Risks to the Public"
Professor(s): L. Robin Keller
Co-author(s): Jim Leonhardt (Merage PhD ’13) and Ronald S. Lembke
Accepted at: Organization Review
Health risks, such as the probability of experiencing a side effect from a medication, are typically communicated numerically. However, presenting risks in strictly numeric formats is problematic considering that the public often experiences difficulty in comprehending strictly numeric probabilities. To help overcome this problem, Leonhardt and Keller (2018) tested the efficacy of using pictographs to visually present probabilistic information to health consumers. They found that the addition of pictographs alongside numeric probability information increased probability comprehension and lessened the perceived risk of a multiple risk health option. Here, we review relevant work on probability format and build on general evaluability theory to posit why pictographs may result in lower risk perceptions of multiple risk options. We discuss current limitations in our understanding of how the public perceives multiple risk options, and we highlight opportunities for future research. For instance, we introduce Quick Response (QR) codes as a potential tool to help consumers view health risks in multiple formats on the Internet.
"The Implications of Recycling Technology Choice on Extended Producer Responsibility"
Professor(s): Luyi Gui
Accepted at: Production and Operations Management (Journal on Financial Times Top 50 list)
We study recycling technology choice, a critical factor that has received little attention in the context of extended producer responsibility, and its interaction with product design-for-recycling in driving the environmental benefits of recycling systems. Collective recycling systems have long been criticized for restricting the environmental benefits of extended producer responsibility because of free riding issues among producers, which can undermine incentives for product design-for-recycling. We revisit and refine this assertion by analyzing the interaction between recycling technology and product design-for-recycling choices. We develop game-theoretic models where producers and processors decide on product design-for-recycling and recycling technology choices, respectively. We then compare the equilibrium benefits of recycling in collective and individual systems. The key result in this paper is that when recycling technology choice is taken into account, collective recycling systems can lead to higher environmental and economic benefits than individual recycling systems. This is because collective recycling systems provide stronger incentives for recycling technology improvements. In turn, these improvements can help overcome the drawbacks associated with inferior product design-for-recycling outcomes caused by free riding concerns among producers in collective recycling systems. In light of these results, we posit that an exclusive focus on product design-for-recycling to assess the environmental benefits of extended producer responsibility-based recycling systems may need scrutiny. Producers and policy makers may need to evaluate recycling systems with respect to the incentives they provide for both product design-for-recycling and recycling technology improvements.
"Benders Decomposition for Profit Maximizing Capacitated Hub Location Problem with Multiple Demand Classes"
Professor(s): Doctoral Candidate Mojtaba Hosseini
Accepted at:Transportation Science
This paper models profit maximizing capacitated hub location problem with multiple demand classes to determine an optimal hub network structure that allocates available capacities of hubs to satisfy demand of commodities from different market segments. A strong deterministic formulation of the problem is presented and a Benders reformulation is described to optimally solve large-size instances of the problem. A new twophase methodology is developed to decompose the Benders subproblem and two effective separation routines are derived to strengthen the Benders optimality cuts. The algorithm is enhanced by the integration of improved variable fixing techniques. The deterministic model is further extended by considering uncertainty associated with the demand to develop a twostage stochastic program. To solve the stochastic version, a Monte-Carlo simulation-based algorithm is developed that integrates a sample average approximation scheme with the proposed Benders decomposition algorithm. Novel acceleration techniques are presented to improve the convergence of the algorithm proposed for the stochastic version. The efficiency and robustness of the algorithms are evaluated through extensive computational experiments. Computational results show that large-scale instances with up to 500 nodes and three demand classes can be solved to optimality, and that the proposed separation routines generate cuts that provide significant speedups compared to using Pareto-optimal cuts. The developed twophase methodology for solving the Benders subproblem as well as the variable fixing and acceleration techniques can be used to solve other discrete location and network design problems.
"Recycling Infrastructure Development under Extended Producer Responsibility in Developing Economies"
Professor(s): Luyi Gui
Accepted at: Production and Operations Management
To tackle the severe pollution caused by electronic waste (e-waste), several developing countries have introduced ewaste legislation based on Extended Producer Responsibility (EPR). A major challenge to implement EPR in developing countries is the lack of formal recycling infrastructure. In this paper, we study if a collective form of EPR implementation where producers may jointly invest in recycling facilities can promote their incentives to do so. We develop a Nash bargaining model that captures the decision dynamics underlying joint recycling facility investment. We show that despite its advantage in reducing producers' fixed investment costs, joint investment in the collective system may lead to a worse recycling infrastructure development outcome than independent investment in an individual system. This can particularly happen when the collective system involves products whose recycling costs are highly differentiated. We further show that cost sharing based on the principle of Individual Producer Responsibility (IPR) may undermine the recycling infrastructure development outcome in the collective system compared to simple proportional cost sharing rules. In practice, it is generally believed that IPR leads to better design incentives than proportional cost sharing rules. Accordingly, our result indicates that there exists a tradeoff between these two cost sharing rules, and promoting recycling infrastructure development via collective systems may come at the expense of design incentives and vice versa.
"Cost Analysis in Global Supply Chains"
Professor(s): Shuya Yin
Co-author(s): Yuhong He (Merage PhD ’14)
Accepted at: Operations Research Letters
This paper aims to explore effects of supply chain members’ costs change on participants of the network. On one perspective, it explores when there is a cost change to a firm, how other firms are affected and who bear(s) the most effect. On the other perspective, it investigates how an individual firm’s performance is affected by the other members in its network and whose cost change would impose a most significant effect on its profit.
"Interdependent Altruistic Preference Models"
Professor(s): L. Robin Keller
Co-author(s): Donald Saari (Professor Emeritus, UCI Social Science) and Jay Simon (Merage PhD ’09)
Accepted at: Decision Analysis
Altruistic preferences, or the desire to improve the well-being of others even at one's own expense, can be difficult to incorporate into traditional value and utility models. It is straightforward to construct a multi-attribute preference structure for one decision maker that includes the outcomes experienced by others. However, when multiple individuals incorporate one another's well-being into their decision making, this creates complex interdependencies that must be resolved before the preference models can be applied. We provide representation theorems for additive altruistic value functions for two-person, n-person, and group outcomes in which multiple individuals are altruistic. We find that in most cases it is possible to resolve the preference interdependencies and that modeling the preferences of altruistic individuals and groups is tractable.
"Dynamized routing policies for minimizing expected waiting time in a multi-class multi-server system "
Professor(s): Professor John Turner
Co-Author(s): Vahid Nourbakhsh (Ph.D. Alumnus)
Accepted at: Computers and Operations Research
Minimizing queue waiting time in multi-class multi-server systems, where the service time depends both on the job type and the server type, has wide applications in transportation systems such as emergency networks and taxi networks, service systems such as call centers, and distributed computing platforms. However, the optimal dynamic policy for this problem is not known and remains a hard open problem. In our approach, we develop a math program to model a static variant of this routing problem and use the solution from this math program to construct several novel dynamic policies. In three categories, namely, (i) policies that do not block jobs, (ii) policies that block jobs statically (i.e., blocking jobs using a predetermined blocking probability), and (ii) policies that block jobs dynamically (i.e., blocking jobs when all feasible servers are busy), we compare the performance of our policies with Fastest-Server-First (FSF), a well-known routing policy for such problems in practice and in the literature. Our experiments show that our proposed overflow dynamic routing policies outperform FSF and its extensions, FSFStaticBlock and FSFDynamicBlock. Moreover, to showcase our methodology, we apply our proposed policies to the problem of assigning fire incidents in Irvine, CA, to fire stations.
"Retail Distribution Strategy with Outlet Stores "
Professor(s): Professor Shuya Yin
Co-Author(s): Jiaru Bai (Ph.D. alumni)
Accepted at: Production and Operations Management (Journal on Financial Times Top 50 list)
Traditionally, outlet stores were situated away from main stores in order to provide older, less desirable products at discounted prices. More recently, some firms have featured an outlet-within-a-store concept and offer consumers the experience of outlet shopping at the same location. With interest in examining the effect of location differentiation and uncertain outlet store’s product fit on a firm’s distribution strategy, we model a firm with an existing main store and study its motivation on whether to open an outlet store, and where to locate it. The analysis leads to a number of key observations. First, if the firm decides to open an outlet store, it may be co-located with the main store or at a remote location, depending on the combined role of customer dispersion, outlet store’s product fit, customer travel sensitivity and fixed costs. Second, there exists a substitution effect between location and quality (and price) differentiation. We show that customer dispersion always encourages the firm to offer outlet stores so as to achieve better market coverage. While a higher probability of product fit at the outlet store makes its more attractive (for example, it makes customers at one location more willing to travel to the outlet store at another location), interestingly, it has a non-monotone effect on its location. Also, in contrast to intuition, higher customer travel sensitivity may even lead to locating the outlet store away from the city center depending on the level of customer dispersion. We also study three extensions. First, the results of our base model are shown to be robust if product quality at the outlet store can positively influence the outlet product’s fit probability. Second, heterogeneous consumer travel sensitivity makes it easier for the firm to segment consumers and hence can increase profits even when travel sensitivity is high. Finally, we consider a continuum of possible outlet store locations, and demonstrate the robustness of the base model results.
“Designing an Accountability Index: A Case Study of South America Central Governments”
Professor(s): Visiting Professor Cristina del Campo
Co-author(s): Paola Hermosa del Vasto, Elena Urquía-Grande, Susana Jorge
Accepted at: Central European Journal of Public Policy
The aim of this paper is to evaluate accountability using a newly constructed multivariate accountability index based on the Global Reporting Initiative (GRI), as well as on the accessibility of government disclosure for each country in the South America context. That will allow to analyse and compare the accountability disclosure issues among the South American countries. This study uses the statistical dimensional structure of data to identify the number of (dominant) dimensions. The findings were eight dimensions defined as Environmental, Expenditure, Social, Strategic, Economic, Information, Macroeconomic and Organizational perspectives. Scores are recorded for the twelve countries in South America that are classified accordingly. The contributions of this research represent an advance in the theoretical and empirical framework by creating an accountability index that takes into account the principles of good governance to improve the South America Central Governments’ transparency performance. This index could be used both by academics and practitioners to classify countries and their web site accountability.
“Valuing Sequences of Lives Lost or Saved Over Time: Preference for Uniform Sequences”
Co-author(s): Jeffery L. Guyse (Merage PhD ’00) and Candice H. Huynh (Merage PhD ’14)
Accepted at: Decision Analysis
Policymakers often make decisions involving human mortality risks and monetary outcomes that span across different time periods and horizons. Many projects or environmental regulation policies involving risks to life, such as toxic exposures, are experienced over time. The preferences of individuals on lives lost or saved over time should be understood to implement effective policies. Using a within-subject survey design, we investigated our participants’ elicited preferences (in the form of ratings) for sequences of lives saved or lost over time at the participant level. The design of our study allowed us to directly observe the possible preference patterns of Negative Time Discounting or a Preference for Spreading from the responses. Additionally, we embedded factors associated with three other prevalent anomalies of intertemporal choice (Gain/Loss Asymmetry, Short/Long Asymmetry, and the Absolute Magnitude Effect) into our study for control. We find that our participants exhibit three of the anomalies: Preference for Spreading, Absolute Magnitude Effect and Short/Long Term Asymmetry. Furthermore, fitting the data collected, Loewenstein and Prelec’s model for the valuation of sequences of outcomes allowed for a more thorough understanding of the factors influencing the individual participants’ preferences. Based on the results, the standard discounting model does not accurately reflect the value that some people place on sequences of mortality outcomes. Preferences for uniform sequences should be considered in policymaking, rather than applying the standard discounting model.
“Comparing Markov and non-Markov Alternatives for Cost-effectiveness Analysis: Insights from a Cervical Cancer Case”
Co-author(s): Jiaru Bai (PhD ’17 alumna)
Accepted at: Operations Research for Health Care
Markov models allow medical prognosis to be modeled with health state transitions over time and are particularly useful for decisions regarding diseases where uncertain events and outcomes may occur. To provide sufficient detail for operations researchers to carry out a Markov analysis, we present a detailed example of a Markov model with five health states with monthly transitions with stationary transition probabilities between states to model the cost and effectiveness of two treatments for advanced cervical cancer A different approach uses survival curves to directly model the fraction of patients in each state at each time period without the Markov property. We use this alternative method to analyze the cervical cancer case and compare the Markov and non-Markov approaches. These models provide useful insights about both the effectiveness of treatments and the associated costs for healthcare decision makers.
“Models for Drone Delivery of Medications and Other Healthcare Items”
Co-author(s): Judy E. Scott (PhD and MBA Alumna)
Accepted at: International Journal of Healthcare Information Systems and Informatics
This article describes how a healthcare delivery drone has the potential for developing countries to leapfrog the development of traditional transportation infrastructure. Inaccessible roads no longer will prevent urgent delivery of blood, medications or other healthcare items. This article reviews the current status of innovative drone delivery with a particular emphasis on healthcare. The leading companies in this field and their different strategies are studied. Further, this article reviews the latest decision models that facilitate management decision making for operating a drone fleet. The contribution in this article of two new models associated with the design of a drone healthcare delivery networks will facilitate a more timely, efficient, and economical drone healthcare delivery service to potentially save lives.
“Planning Online Advertising Using Gini Indices”
Co-author(s): Miguel A. Lejeune
Accepted at: Operations Research
We study an online display advertising planning problem in which advertisers’ demands for ad exposures (impressions) of various types compete for slices of shared resources, and advertisers prefer to receive impressions that are evenly-spread across the audience segments they target. We use the Gini coefficient measure and formulate an optimization problem that maximizes spreading of impressions across targeted audience segments while limiting demand shortfalls. First, we show how Gini-based metrics can be used to measure spreading that publishers of online advertising care about, and how Lorenz curves can be used to visualize Gini-based spread so that managers can effectively monitor the performance of a publisher’s ad delivery system. Second, we adapt an existing ad planning model to measure Gini-based spread across audience segments, and compare and contrast our model to this baseline with respect to key properties and the structure of the solutions they produce. Third, we introduce a novel optimization-based decomposition scheme which efficiently solves our instances of the Gini-based problem up to 60 times faster than the commercial solver CPLEX solves a basic formulation directly. Finally, we present a number of model and algorithmic extensions, including (1) an online algorithm which mirrors the structure of our decomposition method to serve well-spread ads in real-time, (2) a model extension which allows an aggregator buying impressions in an external market to allocate them to advertisers in a well-spread manner, and (3) a multi-period model and decomposition method which spreads impressions across both audience segments and time.
“Coordinating Supply and Demand on an On-demand Service Platform with Impatient Customers”
Professor(s): Rick So
Co-author(s): Jiaru Bai (PhD Alumnus), Chris Tang, Xujun Chen, and Hai Wang
Accepted at: Manufacturing and Service Operations Management, December 2017
We consider an on-demand service platform using earning sensitive independent providers with heterogeneous reservation price (for work participation) to serve its time and price sensitive customers with heterogeneous valuation of the service. As such, both the supply and demand are "endogenously'' dependent on the price the platform charges its customers and the wage the platform pays its independent providers. We present an analytical model with endogenous supply (number of participating agents) and endogenous demand (customer request rate) to study this on-demand service platform. To coordinate endogenous demand with endogenous supply, we include the steady-state waiting time performance based on a queueing model in the customer utility function to characterize the optimal price and wage rates that maximize the profit of the platform. We first analyze a base model that uses a fixed payout ratio (i.e., the ratio of wage over price), and then extend our model to allow the platform to adopt a time-based payout ratio. We find that it is optimal for the platform to charge a higher price when demand increases; however, the optimal price is not necessarily monotonic when the provider capacity or the waiting cost increases. Furthermore, the platform should offer a higher payout ratio as demand increases, capacity decreases or customers become more sensitive to waiting time. We also find that the platform should lower its payout ratio as it grows with the number of providers and customer demand increasing at about the same rate. We use a set of actual data from a large on-demand ride-hailing platform to calibrate our model parameters in numerical experiments to illustrate some of our main insights.
“Do Pictographs Affect Probability Comprehension and Risk Perception of Multiple-Risk Communications?”
Professor(s): Robin Keller
Co-author(s): James Leonhardt (PhD Alumnus)
Accepted at: Journal of Consumer Affairs, December 2017
Pictographs can be used to visually present probabilistic information using a matrix of icons. Previous research on pictographs has focused on single rather than multiple-risk options. The present research conducts a behavioral experiment to assess the effect of pictographs on probability comprehension and risk perception for single and multiple-risk options. The creation of the experimental stimuli is informed by a review of the Centers for Disease Control and Prevention’s vaccine information sheets. The results provide initial evidence that, in the context of childhood vaccines, the inclusion of pictographs alongside numeric (e.g. 1 in 5) probability information can result in higher probability comprehension and lower risk perception for multiple-risk options but not for single-risk options. These findings have implications for how health-related risks are communicated to the public.
“Improving Micro-Retailer and Consumer Welfare in Developing Economies: Replenishment Strategies and Market Entries”
Professor(s): Luyi Gui and Shuya Yin
Accepted at: Manufacturing & Service Operations Management, November 2017
Micro-retailers in remote rural areas in developing countries face high replenishment cost due to poor road infrastructure and the lack of formal distribution channels. This paper investigates the effectiveness of two innovative replenishment strategies (purchasing cooperatives and non-profit wholesaler) deployed by NGOs to reduce micro-retailers' replenishment cost and improve consumer welfare. The problem is relevant in practice as travel cost has been documented as a major cost burden that leads to meager earnings for the micro-retailers, few retailers in the market, and high prices to the consumers (due to less competition). Analyzing how these innovative strategies alleviate the above problems enable us to develop practical insights as well as fill an important gap in the literature. We adopt a stylized model that captures price competition, consumer welfare, and market entry decision of micro-retailers under the replenishment strategies considered. We compare the equilibrium retailer profit, consumer welfare, and the number of retailers entering the market under these strategies. We find that in a regulated market, a non-profit wholesaler creates supply chain inefficiency, and thus can lead to a higher retail price, which benefits the retailers yet harms the consumers. However, with free market entry (unregulated market), we show that under certain market conditions, both the cooperative and the non-profit wholesaler strategies can be Pareto improving. Yet between these two strategies, there typically exists a trade-off in both regulated and unregulated markets, i.e., the cooperative strategy enhances consumer welfare while the wholesaler strategy leads to higher retailer profits. Our results indicate that the policy maker needs to be mindful about the market conditions and the relative emphasis between the profit and market participation of micro-retailers and consumer welfare when choosing and implementing the purchasing cooperative and the non-profit wholesaler strategies.
“Designing Safety Regulations for High-Hazard Industries”
Professor(s): Robin Keller
Co-author(s): Committee for a Study of Performance-Based Safety Regulation
Accepted at: The National Academies Press, October 2017
TRB Special Report 324: Designing Safety Regulations for High-Hazard Industries, examines key factors relevant to government safety regulators when choosing among regulatory design types, particularly for preventing low-frequency, high consequence events. In such contexts, safety regulations are often scrutinized after an incident, but their effectiveness can be inherently difficult to assess when their main purpose is to reduce catastrophic failures that are rare to begin with. Nevertheless, regulators of high-hazard industries must have reasoned basis for making their regulatory design choices.
Asked to compare the advantages and disadvantages of so-called “prescriptive” and “performance-based” regulatory designs, the study committee explains how these labels are often used in an inconsistent and misleading manner that can obfuscate regulatory choices and hinder the ability of regulators to justify their choices. The report focuses instead on whether a regulation requires the use of a means or the attainment of some ends—and whether it targets individual components of a larger problem (micro-level) or directs attention to that larger problem itself (macro-level). On the basis of these salient features of any regulation, four main types of regulatory design are identified, and the rationale for and challenges associated with each are examined under different high-hazard applications.
Informed by academic research and by insights from case studies of the regulatory regimes of four countries governing two high-hazard industries, the report concludes that too much emphasis is placed on simplistic lists of generic advantages and disadvantages of regulatory design types. The report explains how a safety regulator will want to choose a regulatory design, or combination of designs, suited to the nature of the problem, characteristics of the regulated industry, and the regulator’s own capacity to promote and enforce compliance. This explanation, along with the regulatory design concepts offered in this report, is intended to help regulators of high-hazard industries make better informed and articulated regulatory design choices.
“Design Incentives under Collective Extended Producer Responsibility: A Network Perspective”
Professor(s): Luyi Gui
Co-author(s): Atalay Atasu, Ozlem Ergun, Beril Toktay
Accepted at: Management Science, July 2017
A key goal of Extended Producer Responsibility (EPR) legislation is to provide incentives for producers to design their products for recyclability. EPR is typically implemented in a collective system, where a network of recycling resources are coordinated to fulfill the EPR obligations of a set of producers, and the resulting system cost is allocated among these producers. Collective EPR is prevalent because of its cost efficiency advantages. However, it is considered to provide inferior design incentives compared to an individual implementation (where producers fulfill their EPR obligations individually). In this paper, we revisit this assertion and investigate its fundamental underpinnings in a network setting. To this end, we develop a new biform game framework that captures producers' independent design choices (non-cooperative stage) and recognizes the need to maintain the voluntary participation of producers for the collective system to be stable (cooperative stage). This biform game subsumes the network-based operations of a collective system and captures the interdependence between producers' product design and participation decisions. We then characterize the manner in which design improvement may compromise stability and vice versa. We establish that a stable collective EPR implementation can match and even surpass an individual implementation with respect to product design outcomes. In particular, we show that when the processing technology efficiency and product recyclability are substitutes (complements), a recycling network where processor capacity pooling leads to sufficiently low (high) cost reduction will lead to superior designs in the collective system and maintain its stability, and we propose cost allocation mechanisms to achieve this dual purpose.
“Information Presentation in Decision and Risk Analysis: Answered, Partly Answered, and Unanswered Questions”
Professor(s) Robin Keller
Co-author(s): YiTong Wong, PhD Alumnus
Accepted at: Risk Analysis, September 2016
For the last thirty years, researchers in risk analysis, decision analysis, and economics have consistently proven that decision makers employ different processes for evaluating and combining anticipated and actual losses, gains, delays and surprises. While rational models generally prescribe a consistent response, people’s heuristic processes will sometimes lead them to be inconsistent in the way they respond to information presented in theoretically equivalent ways. We point out several promising future research directions by listing and detailing a series of answered, partly answered, and unanswered questions.
“Mixed Planar and Network Single-Facility Location Problems”
Professor(s) Carlton Scott and John Turner
Co-author(s): Zvi Drezner
Accepted at: Networks, August 2016
We consider the problem of optimally locating a single facility anywhere in a network to serve both on-network and off-network demands. Off-network demands occur in a Euclidean plane, while on-network demands are restricted to a network embedded in the plane. On-network demand points are serviced using shortest-path distances through links of the network (e.g., on-road travel), whereas demand points located in the plane are serviced using more expensive Euclidean distances. Our base objective minimizes the total weighted distance to all demand points. We develop several extensions to our base model, including: (i) a threshold distance model where if network distance exceeds a given threshold, then service is always provided using Euclidean distance, and (ii) a minimax model that minimizes worst-case distance. We solve our formulations using the “Big Segment Small Segment” global optimization method, in conjunction with bounds tailored for each problem class. Computational experiments demonstrate the effectiveness of our solution procedures. Solution times are very fast (often under one second), making our approach a good candidate for embedding within existing heuristics that solve multi-facility problems by solving a sequence of single-facility problems.
“Markov Chain Models in Practice: A Review of Low Cost Software Options”
Professor(s) Robin Keller
Co-author(s): Jiaru Bai (PhD Student) and Cristina del Campo
Accepted at: Investigación Operacional, July 2016
Markov processes (or Markov chains) are used for modeling a phenomenon in which changes over time of a random variable comprise a sequence of values in the future, each of which depends only on the immediately preceding state, not on other past states. A Markov process (PM) is completely characterized by specifying the finite set S of possible states and the stationary probabilities (i.e. time-invariant) of transition between these states. The software most used in medical applications is produced by TreeAge, since it offers many advantages to the user. But, the cost of the TreeAge software is relatively high. Therefore in this article two software alternatives are presented: Sto Tree and the zero cost add-in program "markovchain" implemented in R. An example of a cost-effectiveness analysis of two possible treatments for advanced cervical cancer, previously conducted with the Treeage software, is re-analyzed with these two low cost software packages. This paper was also written in Spanish to facilitate communication with Spanish-speaking scholars in Cuba and elsewhere, who aim to conduct Markov cost effectiveness analyses and would benefit from low cost software alternatives.
“Group Selling, Product Durability and Consumer Behavior”
Professor(s) Shuya Yin
Co-author(s): Yuhong He (Ph.D. Alumna) and Saibal Ray
Accepted at: Production and Operations Management, May 2016
Firms producing complementary goods often strategically form groups and jointly sell their products to better coordinate their decisions. For consumer durables, decisions about such collaboration might be complicated due to two factors. Because of their durability and presence of used goods markets, such products engender “future” price competition between new and used goods. On the other hand, consumers of such products might be forward-looking and patient, both of which affect their purchasing behavior. In this paper, we study how the above product and consumer characteristics interact to affect the group selling decisions of complementary firms. We do so through a two-period model consisting of a value chain with two upstream manufacturers and a downstream retailer. When consumers are relatively impatient and reluctant to wait to buy later, group selling by manufacturers will take place only when the end product is relatively perishable, i.e., product durability is low. However, if consumers are patient, i.e., willing to wait, collaboration happens only when the end product is quite durable; for relatively perishable products the manufacturers sell their products separately. We also comment on how our results are affected by factors like manufacturers directly selling to end consumers or there being multiple opportunities to decide whether or not to use group selling strategy.
“Managing a Closed-Loop Supply System with Random Returns and a Cyclic Delivery Schedule”
Professor(s) Rick So
Co-author(s): Candice Huyhn (Ph.D. Alumna) and Haresh Gurnani
Accepted at: European Journal of Operational Research, May 2016
Motivated by an industry example, we develop a mathematical framework to address the inventory replenishment and capacity planning problem for a closed-loop supply system with random returns. The provider needs to deliver new or refurbished products to a group of clients under a fixed cyclic schedule, and also collects back a random portion of the used products in the subsequent delivery cycle for refurbishment. We first address the product replenishment strategy, in which only a random portion of the delivered products will be returned for refurbishment and the supplier must regularly purchase new products to replace the lost units. We then analyze the capacity decision problem where the provider uses his facility to refurbish the returned products for reuse, and the provider could incur extra refurbishing cost to handle the returned product at the end of each cycle due to insufficient capacity. Our models provide a simple decision support tool for making effective replenishment and capacity decisions in managing such a closed-loop supply system.
“The Value of Demand Forecast Updates in Managing Component Procurements for Assembly Systems”
Professor(s) Rick So
Co-author(s): James Cao (Ph.D. Alumnus)
Accepted at: IIE Transactions, May 2016
This paper examines the value of demand forecast updates in an assembly system where a single assembler must order components from independent suppliers with different lead-times. By staggering each ordering time, the assembler can utilize the latest market information, as it is developed, to form a better forecast over time. The updated forecast can subsequently be used to decide the following procurement decision. The objective of this research is to understand the specific operating environment under which demand forecast updates are most beneficial. Using a uniform demand adjustment model, we are able to derive analytical results that allow us to quantify the impact of demand forecast updates. We show that forecast updates can drastically improve profitability by reducing the mismatch cost caused by demand uncertainty.
“Thinking Styles Affect Reactions to Brand Crisis Apologies”
Professor(s) Robin Keller
Co-author(s): Shijian Wang , Liangyan Wang (Ph.D. Alumna) and Jie Li
Accepted at: European Journal of Marketing, April 2016
Purpose – This article examines how a person’s thinking style, specifically holistic versus analytic, and a firm’s crisis apology with the remedial solution framed in “why” (vs. “how”) terms can interactively impact consumers’ perceived efficacy of the firm to respond to the crisis and their impression or evaluation of the brand. Design/methodology/approach – Hypotheses were tested through three experimental studies involving 308 participants recruited in China. Participants answered survey questions investigating the interactive effects from consumers’ thinking style (culture as a proxy in study 1, measured in study 2 or primed in study 3) and a brand’s crisis apology with the remedial solution framed in “why” (vs. “how”) terms on consumers’ perceived efficacy and evaluation of the firm. Findings – The frame of the remedial solution resulting in a higher evaluation improvement depended on a consumer’s thinking style. For holistic thinkers, a “why” (vs. “how”) framed remedial solution resulted in a higher evaluation improvement; however, for analytic thinkers, a “how” (vs. “why”) framed remedial solution resulted in a higher evaluation improvement. Additionally, the results showed that a consumer’s perceived efficacy of the brand being able to successfully respond to the crisis mediated the interactive effects of the remedial solution framing and thinking styles on the evaluation improvement. Research limitations/implications – Different ways of framing the remedial solution in a firm’s apology will have different impacts on people with different thinking styles. Participants in studies 2 and 3 were recruited from samples on campus in China. Additionally, the automobile brand used in this study is fictional to avoid prior brand name or brand commitment impact. Practical implications – Our findings provide evidence that framing of the remedial solution can be leveraged as a tool to reduce negative impact resulting from a brand crisis. Specifically, our results suggest that companies may do well to employ a “why” framed remedial solution, particularly in cases where consumers are likely to process information holistically. Conversely, a “how” framed remedial solution may be effective in situations where consumers are likely to process information analytically. Originality/value – This research contributes to the literature, being among the first to consider how the remedial solution framing in a firm’s apology can enhance people’s evaluation of the brand and decrease the perceived negative impact resulting from the brand crisis.
“Trust Antecedents, Trust and Online Microsourcing Adoption: An Empirical Study from the Resource Perspective”
Professor(s) Robin Keller
Co-author(s): Baozhou Lu, Tao Zhang, and Liangyan Wang (Ph.D. Alumna)
Accepted at: Decision Support Systems, April 2016
The online microsourcing marketplace is a new form of outsourcing that is organized over online platforms for the performance of relatively small service tasks. Microsourcing offers a more flexible way to hire contract workers or to outsource. Prior research indicates the importance of individual-level trust when choosing providers in online sourcing marketplaces. We argue that institution-based trust is also crucial for online microsourcing adoptions. Drawing on a trust framework adapted from prior literature, this paper uncovers the trust-building mechanisms in online microsourcing marketplaces, as well as the marketplace-related attributes for online microsourcing adoption. The proposed research model is tested with a data set collected from the clients of a typical marketplace in China – zhubajie.com. The findings suggest that perceptions of resource-based attributes of a marketplace, together with the perceived effectiveness of its intermediary role, can help to build trust towards the marketplace, enhancing trust towards the community of providers and driving the intent to adopt online microsourcing. Thus, this paper confirms the roles of online marketplaces as both the resource pool and the transaction intermediary from the perspective of clients. Finally, this paper not only indicates the relevance of resource theories in understanding this new trend in outsourcing, but also suggests the importance of trusted relational governance in governing online microsourcing transactions.
“A Queueing Model for Managing Small Projects under Uncertainties”
Professor(s) Rick So and Ph.D. Student Jiaru Bai
Co-author(s): Chris Tang
Accepted at: European Journal of Operation Research, March 2016
We consider a situation in which a home improvement project contractor has a team of regular crew members who receive compensation even when they are idle. Because both projects arrivals and the completion time of each project are uncertain, the contractor needs to manage the utilization of his crews carefully. One common approach adopted by many home improvement contractors is to accept multiple projects to keep his crew members busy working on projects to generate positive cash flows. However, this approach has a major drawback because it causes "intentional" (or foreseeable) project delays. Intentional project delays can inflict explicit and implicit costs on the contractor when frustrating customers abandon their projects and/or file complaints or lawsuits. In this paper, we present a queueing model to capture uncertain customer (or project) arrivals and departures, along with the possibility of customer abandonment. Also, associated with each admission policy (i.e., the maximum number of projects that the contractor will accept), we model the underlying tradeoff between accepting too many projects (that can increase customer dissatisfaction) and accepting too few projects (that can reduce crew utilization). We examine this tradeoff analytically so as to determine the optimal admission policy and the optimal number of crew members. We further apply our model to analyze other issues including worker productivity and project pricing. Finally, our model can be extended to allow for multiple classes of projects with different types of crew members.
“Intention-Behavior Discrepancy of Foreign versus Domestic Brands in Emerging Markets: The Relevance of Consumer Prior Knowledge”
Professor(s) Robin Keller
Co-author(s): Luping Sun, Xiaona Zheng, and Meng Su
Accepted at: Journal of International Marketing, November 2016
Most research on the performance of foreign versus domestic brands in emerging markets examines measures of product evaluation or purchase intention. However, consumers intending to buy a product may switch to competing brands, displaying an intention-behavior discrepancy (IBD). Drawing upon literature on country associations and dual process theory, we examine the difference in IBD of foreign versus domestic brands in emerging markets and the moderating role of prior knowledge. We conducted an intention survey followed by a post-purchase survey in the Chinese automobile and smartphone industries. We found that foreign brands have an advantage on IBD relative to domestic brands, indicating that they have the dual advantage of higher evaluations and lower IBDs. Furthermore, foreign brands’ advantage on IBD is smaller for consumers with inaccurate prior knowledge, as they are more likely to systematically reprocess information and discount foreign brands’ favorable country associations. For these consumers, overestimating the product reduces foreign brands’ advantage to a smaller degree than underestimating it due to confirmation bias. These findings provide implications for brands in emerging markets.
“A Unified Framework for the Scheduling of Guaranteed Targeted Display Advertising under Reach and Frequency Requirements”
Professor(s) John Turner
Co-author(s): Ali Hojjat (Ph.D. Alumnus), Suleyman Cetintas, Jian Yang
Accepted at: Operations Research, October 2016
Motivated by recent trends in online advertising and advancements made by online publishers, we consider a new form of contract which allows advertisers to specify the number of unique individuals that should see their ad (reach), and the minimum number of times each individual should be exposed (frequency ). We develop an optimization framework that aims for minimal under-delivery and proper spread of each campaign over its targeted demographics. As well, we introduce a pattern-based delivery mechanism which allows us to integrate a variety of interesting features into a website’s ad allocation optimization problem which have not been possible before. For example, our approach allows publishers to implement any desired pacing of ads over time at the user level or control the number of competing brands seen by each individual. We develop a two-phase algorithm that employs column generation in a hierarchical scheme with three parallelizable components. Numerical tests with real industry data show that our algorithm produces high-quality solutions and has promising run-time and scalability. Several extensions of the model are presented, e.g., to account for multiple ad positions on the webpage, or randomness in the website visitors’ arrival process.
