Operations and Decision Technologies Abstracts

"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.